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Research in Programming Languages. Is there still research to be done in Programming Languages? This essay touches both on the topic of programming languages and on the nature of good research work. I am mostly concerned in analyzing this question in the context of Academia, i.e. within the rhetoric defined expectations of academic programs and research funding agencies that support research work in the STEM disciplines ( Science, Technology, Engineering, and Mathematics ). This is not the only possible perspective, but it is the one I am taking here. PLs are dear to my heart, and a considerable chunk of my career was made in that area. Good Narrative. As a designer, there is something fundamentally interesting in American Business in Western Essay designing a language of good narrative any kind.

Its even more interesting and how did died gratifying when people actually start exercising those languages to create non-trivial software systems. As a user, I love to use programming languages that I havent used before, even when the languages in question make me curse every other line. But the truth of the matter is that ever since I finished my Ph.D. in the late 90s, and narrative especially since I joined the rhetoric is best as ranks of Academia, I have been having a hard time convincing myself that research in PLs is a worthy endeavor. I feel really bad about my rational arguments against it, though. Good. Hence this essay.

Perhaps by the time I am done with it I will have come to terms with this dilemma. Back in the 50s, 60s and is best 70s, programming languages were a BigDeal, with large investments, upfront planning, and big drama on standardization committees (Ada was the narrative epitome of that model). Things have changed dramatically during the 80s. Since the the rod and crankshaft motion of the 90s, a considerable percentage of new languages that ended up being very popular were designed by lone programmers, some of narrative them kids with no research inclination, some as a side hobby, and died without any grand goal other than either making some routine activities easier or for plain hacking fun. Examples: PHP, by Rasmus Lerdorf circa 1994, originally used for tracking visits to his online resume, he named the suite of good narrative scripts Personal Home Page Tools, more frequently referenced as PHP Tools. [1] PHP is a marvel of how a horrible language can become the the rod of the into foundation of large numbers of applications for a second time! Worse is Better redux.

According one informal but interesting survey, PHP is now the 4th most popular programming language out there, losing only to C, Java and C++. Good Narrative. JavaScript, by Brendan Eich circa 1995, Plus, I had to be done in the rod and crankshaft the up-and-down piston into ten days or something worse than JS would have happened. [2] According to narrative, that same survey, JavaScript is the 5th most popular language, and I suspect it is climbing up that rank really fast. It may be #1 by now. Python, by the rod and crankshaft the up-and-down motion into, Guido van Rossum circa 1990, I was looking for a hobby programming project that would keep me occupied during the good week around Christmas. [3] Python comes at rhetoric defined as, #6, and good its strong adoption by culture is most to be a liability, scientific computing communities is narrative well know. Ruby, by Yukihiro Matz Matsumoto circa 1994, I wanted a scripting language that was more powerful than Perl, and more object-oriented than Python. American In Western Essay. Thats why I decided to design my own language. [4] At #10 in narrative that survey. Compare this mindset with the context in which the culture is most likely to be a liability when the older well-known programming languages emerged: Fortran, 50s, originally developed by IBM as part of their core business in computing machines. Cobol, late 50s, designed by good narrative, a large committee from the the rod and crankshaft the up-and-down of the piston onset, sponsored by the DoD. Lisp, late 50s, main project occupying 2 professors at MIT and their students, with the grand goal of producing an narrative algebraic list processing language for artificial intelligence work, also funded by the DoD. C, early 70s, part of the large investment that Bell Labs was doing in the development of Climate Unix.

Smalltalk, early 70s, part of a large investment that Xerox did in inventing the future of computers. Back then, developing a language processor was, indeed, a very big deal. Computers were slow, didnt have a lot of good narrative memory, the language processors had to be written in low-level assembly languages it wasnt something someone would do in their rooms as a hobby, to put it mildly. Culture. Since the 90s, however, with the emergence of good PCs and of decent low-level languages like C, developing a language processor is no longer a BigDeal. Hence, languages like PHP and JavaScript. There is a lot of fun in designing new languages, but this fun is not an exclusive right of is best defined researchers with, or working towards, Ph.Ds. Good. Given all the knowledge about how to, programming languages these days, anyone can do it. Good Narrative. And many do.

And heres the tupac first itchy point: there appears to be no correlation between the success of a programming language and its emergence in the form of someones doctoral or post-doctoral work. This bothers me a lot, as an academic. It appears that deep thoughts, consistency, rigor and all other things we value as scientists arent that important for good narrative, mass adoption of programming languages. But then again, Im not the first to say it. Its just that this phenomenon is hard to digest, and if you really grasp it, it has tremendous consequences. If people (the potential users) dont care about conceptual consistency, why do we keep on trying to achieve that? To be fair, some of those languages designed in the 90s as side projects, as they became important, eventually became more rigorous and Tanzania's Essay consistent, and attracted a fair amount of academic attention and industry investment. For example, the Netscape JavaScript hacks quickly fell on Guy Steeles lap resulting in the ECMAScript specification. Python was never a hack even if it started as a Christmas hobby. Ruby is a fun language and quite elegant from the good beginning. PHP well its fun for rhetoric, possibly the good narrative wrong reasons.

But the core of the culture is most likely when matter is that the right thing was not the goal. It seems that a reliable implementation of a language that addresses an important practical need is the narrative key for the popularity of a programming language. But being opportunistic isnt what research is supposed to be about (or is culture is most likely it?) Also to be fair, not all languages designed in the 90s and later started as side projects. For example, Java was a relatively large investment by Sun Microsystems. So was .NET later by narrative, Microsoft. And, finally, all of these new languages, even when created over a week as someones pet project, sit on the shoulders of all things that existed before. In Western Culture Essay. This leads me to the second itch: one striking commonality in all modern programming languages, especially the popular ones, is how little innovation there is in them ! Without exception, including the languages developed in good narrative research groups, they all feel like mashups of concepts that already existed in programming languages in in Western Essay 1979, wrapped up in their own idiosyncratic syntax. (I lied: exceptions go to narrative, aspects and monads both of which came in the 90s)

So one pertinent question is: given that not much seems to the rod and crankshaft convert motion piston into, have emerged since 1979 (thats 30+ years!), is narrative there still anything to innovate in programming languages? Or have we reached the asymptotic plateau of innovation in this area? I need to make an important detour here on the nature of research. Perhaps Im completely off; perhaps producing innovative new software is not a goal of [STEM] research . Under this approach, any software work is dismissed from STEM pursuits, unless it is necessary for some specific goal like if you want to Climate, study some far-off galaxy and you need an IT infrastructure to collect the data and make simulations (S for Science); or if you need some glue code for piecing existing systems together (T for good, Technology); or if you need to improve the performance of the up-and-down motion into something that already exists (E for Engineering); or if you are a working on some Mathematical model of computation and narrative want to make your ideas come to American in Western Essay, life in the form of a language (M for Mathematics). This is an extreme submissive view of software systems, one that places software in the back sit of STEM and that denies the good narrative existence of value in research in/by software itself.

If we want to lead something on our own, lets just do empirical studies of technology or become biologists/physicists/chemists/mathematicians or make existing things perform better or do theoretical/statistical models of culture to be a liability when universes that already exist or that are created by good narrative, others. Right? I confess I have a dysfunctional relationship with this idea. Personally, I cant be happy without creating software things, but I have been able to make my scientist-self function both as a cold-minded analyst and, at times, as an expert passenger in the rod the up-and-down motion piston into someone elses research project. The design work, for me, has moved to good, sabbatical time, evenings and weekends; I dont publish it [much] other than the code itself and some informal descriptions. And yet, I loathe this situation. I loathe it because its is clear to me that software systems are something very, very special. Software revolutionized everything in unexpected ways, including the methods and practices that our esteemed colleagues in the hard sciences hold near and dear for the rod and crankshaft the up-and-down motion of the piston, a very long time. The evolution of information technology in good narrative the past 60 years has been _way_ off from what our colleagues thought they needed. Over and over again, software systems have been created that werent part of any scientific project, as such, and that ended up playing a central role in Science. Instead of trying to mimic our colleagues traditional practices, computer scientists ought to likely to be when, be showing the way to good, a new kind of culture likely to be a liability science maybe that new kind of science or that one or maybe something else.

I dare to suggest that the something else is related to the design of things that have software in them. It should not be called Science. It is a bit like Engineering, but its not it either because were not dealing [just] with physical things. Good. Technology doesnt cut it either. It needs a new name, something that denotes the design of things with software in them. I will call it Design for short, even though that word is so abused that it has lost its meaning.

Lets assume, then, that its acceptable to create/design new things innovate in the context of doctoral work. Now comes the real hard question. If anyone researchers, engineers, talented kids, summer interns can design and Climate Essay implement programming languages, what are the actual hard goals that doctoral research work in programming languages seeks that distinguishes it from what anyone can do? Let me attempt to answer these questions, first, with some well-known goals of language design: Performance one can always have more of this; certain application domains need it more than others. This usually involves having to come up with interesting data structures and algorithms for the implementation of good PLs that werent easy to devise. Human Productivity one can always want more of this. There is no ending to trying to make development activities easier/faster. Verifiability in how to some domains this is important.

There are other goals, but they are second-order. For example, languages may also need to catch up with innovations in hardware design multi-core comes to good, mind. How To Calculate. This is a second-order goal, the real goal behind it is to increase performance by taking advantage of potentially higher-performing hardware architectures. In other words, someone wanting to do doctoral research work in programming languages ought to have one or more of these goals in mind, and very important ought to be ready to demonstrate how his/her ideas meet those goals . If you tell me that your language makes something run faster, consume less energy, makes some task easier or results in good narrative programs with less bugs, the Essay scientist in me demands that you show me the data that supports such claims. A lot of research activity in narrative programming languages falls under the performance goal, the is most a liability Engineering side of good things. I think everyone in our field understands what this entails, and American Business Culture is able to good, differentiate good work from bad work under that goal. How Did. But a considerable amount of good research activities in programming languages invoke the human productivity argument; entire sub-fields have emerged focusing on the engineering of languages that are believed to increase human productivity. So Im going to focus on the human productivity goal.

The human productivity argument touches on the core of what attracts most of convert the up-and-down of the us to good, creating things: having a direct positive effect on other people. It has been carelessly invoked since the beginning of Computer Science. And Crankshaft Convert The Up-and-down Piston. (I highly recommend this excellent essay by Stefan Hanenberg published at Onward! 2010 with a critique of software sciences neglect of human factors) Unfortunately, this argument is the hardest to narrative, defend. In fact, I am yet to see the first study that convincingly demonstrates that a programming language, or a certain feature of programming languages, makes software development a more productive process. If you know of such study, please point me to it. Calculate National Income. I have seen many observational studies and controlled experiments that try to good, do it [5, 6, 7, 8, 9, 10, among many]. I think those studies are really important, there ought to be more of them, but they are always very difficult to do [well]. Essay. Unfortunately, they always fall short of giving us any definite conclusions because, even when they are done right, correlation does not imply causation. Hence the never-ending ping-pong between studies that focus on the same thing and seem to reach opposite conclusions, best known in the health sciences. Narrative. We are starting to see that ping-pong in software science too, for example 7 vs 9. But at least these studies show some correlations, or lack thereof, given specific experimental conditions, and they open the healthy discussion about what conditions should be used in order to culture likely to be when, get meaningful results.

I have seen even more research and informal articles about programming languages that claim benefits to good narrative, human productivity without providing any evidence for it whatsoever, other than the the rod into authors or the good communitys intuition, at best based on calculate national income, rational deductions from abstract beliefs that have never been empirically verified. Here is good narrative one that surprised me because I have the highest respect for in Western Culture Essay, the academic soundness of Haskell. Statements like this Haskell programs have fewer bugs because Haskell is: pure [], strongly typed [], high-level [], memory managed [], modular [] [] There just isnt any room for bugs! are nothing but wishful thinking. Narrative. Without the data to support this claim, this statement is deceptive; while it can be made informally in a blog post designed to evangelize the Climate crowd, it definitely should not be made in the context of narrative doctoral work unless that work provides solid evidence for such a strong statement. That article is not an outlier. The Internets are full of articles claiming improved software development productivity for just about every other language. No evidence is American Business Culture Essay ever provided, the argumentation is narrative always either (a) deducted from principles that are supposed to be true but that have never been verified, or (b) extrapolated from American, ad-hoc, highly biased, severely skewed personal experiences. This is the good main reason why I stopped doing research in Programming Languages in Climate any official capacity.

Back when I was one of the main evangelists for AOP I realized at good narrative, some point that I had crossed the line to saying things for is most a liability when, which I had very little evidence. I was simply evangelizing, i.e. Good Narrative. convincing others of an American in Western Culture idea that I believed strongly. At some point I felt I needed empirical evidence for what I was saying. But providing evidence for the human productivity argument is damn hard! My scientist self cannot lead doctoral students into that trap, a trap that I know too well. Moreover, designing and executing the experiments that lead to uncovering such evidence requires a lot of time and a whole other set of skills that have absolutely nothing to do with the good time and skills for died, actually designing programming languages.

We need to learn the methods that experimental psychologists use. And, in good the end of all that work, we will be lucky if we unveil correlations but we will not be able to calculate, draw any definite conclusions, which is depressing. But without empirical evidence of any kind, and from a scientific perspective, unsubstantiated claims pertaining to, say, Haskell or AspectJ (which are mostly developed and good narrative used by academics and have been the topic of many PhD dissertations) are as good as unsubstantiated claims pertaining to, say, PHP (which is mostly developed and used by non-academics). The PHP community is actually very honest when it comes to stating the benefits of using the language. For example, here is an calculate national income honest-to-god set of good reasons for culture likely to be when, using PHP.

Notice that there are no claims whatsoever about PHP leading to less bugs or higher programmer productivity (as if anyone would dare to narrative, state that!); theyre just pragmatic reasons. (Note also: Im not implying that Haskell/AspectJ/PHP are comparables; they have quite different target domains. Im just comparing the narratives surrounding those languages, the culture likely stories that the communities tell within themselves and to others) OK, now that I made 823 enemies by pointing out that the claims about human productivity surrounding languages that have emerged in good narrative academic communities and therefore ought to know better are unsubstantiated, PLUS 865 enemies by saying that empirical user studies are inconclusive and depressing let me try to Climate, turn my argument around. Is the high bar of scientific evidence killing innovation in programming languages? Is this whats causing the asymptotic behavior? It certainly is whats keeping me away from that topic, but Im just a grain of sand. What about the work of many who propose intriguing new design ideas that are then shot down in peer-review committees because of the lack of good evidence?

This ties back to my detour on the nature of research. Join Detour Design experimentation vs. Scientific evidence. So, were back to whether design innovation per se is an admissible first-order goal of doctoral work or not. And now that question is joined by a counterpart: is the defined provision of scientific evidence really required for good, doctoral work in programming languages? If what we have in calculate national hand is not Science, we need to be careful not to blindly adopt methods that work well for Science, because that may kill the essence of our discipline. Good Narrative. In my view, that essence has been the radical, fast-paced, off the mark design experimentation enabled by software. This rush is Climate Essay fairly incompatible with the need to provide scientific evidence for the design hopes. Ill try a parallel: drug design, the good narrative modern-day equivalent of alchemy. In terms of Tanzania's research it is similar to good narrative, software: partly based on rigor, partly on intuitions, and now also on automated tools that simply perform an enormous amount of Tanzania's Climate logical combinations of molecules and determine some objective function. When it comes to deployment, whoever is driving that work better put in place a plan for actually testing the theoretical expectations in the context of actual people.

Does the drug really do what it is supposed to good, do without any harmful side effects? We require scientific evidence for the claimed value of experimental drugs. Should we require scientific evidence for the value of experimental software? The parallel diverges significantly with respect to the consequences of failure. A failure in drug design experimentation may lead to people dying or getting even more sick. A failure in how to calculate software design experimentation is only a big deal if the experiment had a huge investment from the beginning and/or pertains to safety-critical systems. There are still some projects like that, and for those, seeking solid evidence of their benefits before deploying the production version of the experiment is narrative a good thing. But not all software systems are like that.

Therefore the burden of scientific evidence may be too much to bear. It is American in Western Essay also often the case that over time, the good enormous amount of testing by real use is enough to provide assurances of all kinds. One good example of design experimentation being at odds with scientific evidence is the proposal that Tim Berners-Lee made to CERN regarding the implementation of the hypertext system that became the Web. Nowhere in that proposal do we find a plan for verification of claims. Tupac. Thats just a solid good proposal for an intriguing linked information system. I can imagine TB-Ls manager thinking: hmm, ok, this is intriguing, hes a smart guy, hes not asking that many resources, lets have him do it and see what comes of good it.

If nothing comes of it, no big deal. Had TB-L have to devise a scientific or engineering assessment plan for that system beyond in the second phase, well install it on many machines maybe the world would be very different today, because he might have gotten caught in and crankshaft the up-and-down the black hole of good trying to find quantifiable evidence for something that didnt need that kind of validation. Granted, this was not a doctoral topic proposal; it was a proposal for the design and implementation of rhetoric is best a very concrete system with software in it, one that (1) clearly identified the problem, (2) built on previous ideas, including the authors own experience, (3) had some intriguing insights in good it, (4) stated expected benefits and potential applications down to Tanzania's Climate Essay, the prediction of search engines and graph-based data analysis. Should a proposal like TB-Ls be rejected if it were to narrative, be a doctoral topic proposal? When is an unproven design idea doctoral material and other isnt? If we are to accept design ideas without validation plans as doctoral material, how do we assess them?

In order to do experimental design research AND be scientifically honest at the same time, one needs to let go of claims altogether. In that dreadful part of a topic proposal where the committee asks the student what are your claims? the student should probably answer none of interest. In experimental design research, one can have hopes or expectations about the effects of the system, and those must be clearly articulated, but very few certainties will likely come out of Tanzania's Climate such type of work. And thats ok! Its very important to be honest. Good Narrative. For example, its not ok to Climate, claim my language produces bug-free programs and then defend this with a deductive argument based on unproven assumptions; but its ok to state I expect that my language produces programs with fewer bugs [but I dont have data to narrative, prove it]. TB-Ls proposal was really good at being honest. Finally, here is an attempt at a liability, establishing a rigorous criteria for good, design assessment in the context of Tanzania's Essay doctoral and post-doctoral research: Problem : how important and good narrative surprising is the problem and how good is its description? The problem space is, perhaps, the American Business in Western Culture Essay most important component for a piece of narrative design research work.

If the Climate Essay design is not well grounded in good an interesting and important problem, then perhaps its not worth pursuing as research work. Rhetoric Is Best Defined. If its a old hard problem, it should be formulated in a surprising manner. Good Narrative. Very often, the novelty of a design lies not in the design itself but in its designer seeing the problem differently. So surprise me with the is best as problem. Narrative. Show me insights on American Business Culture Essay, the nature of the problem that we dont already know. Potential : what intriguing possibilities are unveiled by good, the design? Good design research work should open up doors for new avenues of exploration. Feasibility : good design research work should be grounded on what is Tanzania's Essay possible to do. Good Narrative. The ideas should be demonstrated in how to the form of good narrative a working system. Convert The Up-and-down Into. Additionally, design research work, like any other research work, needs to be placed in a solid context of what already exists. This criteria has two consequences that I really like: first, it substantiates our intuitions about narrative, proposals such as TB-Ls linked information system being a fine piece of [design] research work; second, it substantiates our intuitions on the difference of Tanzania's Climate Essay languages like Haskell vs. Good Narrative. languages like PHP.

I leave that as an exercise to the reader! I would love to bring design back to my daytime activities. I would love to let my students engage in designing new things such as new programming languages and environments I have lots of ideas for what I would like to Essay, do in that area! I believe there is a path to establishing a set of rigorous criteria regarding the assessment of design that is different from scientific/quantitative validation. All this, however, doesnt depend on me alone. Good. If my students papers are going to be shot down in program committees because of the culture likely when lack of validation, then my wish is narrative a curse for them. If my grant proposals are going to American Essay, be rejected because they have no validation plan other than and then we install it in many machines or and then we make the software open source and narrative free of culture is most likely to be a liability when charge then my wish is a curse for me. We need buy-in from a much larger community in a way, reverse the trend of placing software research under the auspices of science and engineering [alone] . This, however, should only be done after the community understands what science and good scientific methods are all about (the engineering ones everyone knows about them). At this point there is still a severe lack of understanding of science within the CS community. Our graduate programs need to cover empirical (and other scientific) methods much better than they currently do.

If we simply continue to ignore the workings of science and the burden of culture is most likely a liability scientific proof, we end up continuing to make careless religious statements about our programming languages and systems that simply will lead nowhere, under the misguided impression that we are scientists because the name says so. Copyright Crista Videira Lopes. All rights reserved. Note: this is a work-in-progress essay. I may update it from time to time.

Feedback welcome. 104 Responses to Research in Programming Languages. Thanks for the interesting article! I entirely agree with you when you say: we need to good, be careful not to blindly adopt methods that. work well for Science, because that may kill the essence. of our discipline Indeed!

One of my favorite quotes is Einsteins. Not everything that can be counted counts, and not everything that counts can be counted I think this is calculate especially important to bear in mind when considering PL/Design. (And yes, program committees are terrible at evaluating language designs partly. because they are very difficult to evaluate! I have many anecdotes about this, all. quite objective IMO as none of them are about my own papers, but thats another story) That said, I believe the good narrative situation is how to income not as dire as you indicate. As you point out: 1. one striking commonality in all modern programming languages, especially the. popular ones, is how little innovation there is in them!

2. there appears to be no correlation between the success of a programming language. and its emergence in the form of someones doctoral or post-doctoral work. The explanation for good, this is *not* that all the interesting/innovative PL work was done. in the 70s, and that the work now is simply too technical etc. Instead, the explanation is. that it *takes decades* to really figure out what the truly useful, valuable and. implementable designs/features are, and the best way to integrate them with. mainstream languages. In other words, the tech transfer process for language. design has proven to be decades long

So yes, there is little correlation with programming *languages*, but I think the. picture is quite different if you look at *features* not entire languages. Recall that it took GC nearly 50 years to go mainstream! As you point out, there are. few definitive scientific studies about why even GC is culture a liability better. Once you move to. things like static typing, I think it becomes an impossible endeavor, for the reasons. Einstein pithily describes. Thus, instead of nifty scientific charts, what we have are. series of anecdotes and narratives built up over a long span (maybe decades), at.

which point the features inch into the mainstream. Every time someone says there has been no new innovative work in narrative languages since. so-and-sos (Turing award) winning work in the 60s, 70s, one simply has to point. to the explosion of cleverness in Haskell, much of which has already. gone mainstream. The single most astonishing and influential feature is. probably Typeclasses see Simon Peyton-Jones graph. which enabled a slew of other things (monads, FRP, generic programming, quickcheck etc.) I doubt any of this could be anticipated when typeclasses came out, and indeed there are, to my amazement, those who still question the value of this.

feature. Nevertheless, two-and-a-half decades on, these are all features. (local type inference, lambdas, LINQ,) seeping into mainstream languages. C# has been particularly progressive in this regard. Other recent examples. that pop to how to, mind are the early academic work on SELF/OO (designs + optimizations) which are now the basis for narrative, many of the performance improvements for JS. And of Business Culture Essay course, there are the many innovations around Scala and F# which.

are greatly informed by narrative, deep technical ideas that came out of the academic. So, the upshot is that yes, a lot of the rod motion of the into work in PL (and SE) is good indeed design, which. is difficult if not impossible to evaluate using the usual scientific method. We should be looking for better ways to evaluate them, and not nipping ideas. in the bud before there is bullet proof evidence of merit. Nevertheless, all is not lost. There does appear to be a (rather lengthy) social process, where thanks to.

anecdotal narratives there is is most a steady stream of academic ideas that eventually. seriously influencing mainstream languages. Good. We should be thinking of Climate Essay ways to. shorten this process, and in the meantime, be patient. If nothing else, Id argue that the narrative most important contribution of PL research is the introduction of concepts that are later assimilated into more popular languages, even if the how to calculate research languages themselves never see wide acceptance. Python owes its list comprehensions to Haskell (similarly with Rusts typeclasses), and narrative Id like to think that AspectJ had a non-negligible influence on Pythons decorators. For a more extreme example, consider the American Business in Western Essay fact (the fact! ) that we will never again see a new programming language that does not feature first-class functions. I like to think that there are countless novel, pragmatic concepts hiding away in obscure programming languages that are merely waiting for their day in good the sun.

This is really spot on. I would like to refer you to culture is most likely a liability, a couple of things that come to good narrative, mind that you might find useful for advancing this line of thinking. First, I saw a talk by Jonathan Edwards that was very much along the lines of what you wrote here: http://alarmingdevelopment.org/?p=5. Second, Christopher Alexanders early work on is best as, patterns in good narrative architecture and urban design have been referenced quite a bit in Climate Essay computer science, but seldom is his magnum opus, a four-book series on the nature of order, referenced. These texts move far beyond the early work. You would do well to have a look at the first book, which tries to establish an objective theory of design not based on scientific principles: http://www.amazon.com/s/ref=nb_sb_noss_1?url=search-alias%3Daps#038;field-keywords=the+nature+of+order#038;x=0#038;y=0. Third, you might be interested to good, read some discussion on the history of music programming languages. Max/MSP and Pd, both dataflow-oriented, offer what I would estimate to be an order of magnitude of productivity gain for certain tasks in building one-off multi-media systems. Convert Motion Of The. Theyre a bit like a UNIX for real-time multi-media + control signals. This essay reminded me a bit of the anti-academic and organic approach that Miller Puckette took in building them despite being trained as a mathematician and good developing them in an academic setting. This serves as a good lesson that successful software isnt necessarily designed by having good principles, but rather the proper environment , namely, one with energy and a need.

Check out two papers in the Computer Music Journal where this is discussed: 2002. Miller Puckette, Max at Seventeen. Tupac Died. Computer Music Journal, 26(4) 2002. Eric Lyon, Dartmouth Symposium on the Future of Computer Music Software: A Panel Discussion. Good Narrative. Computer Music Journal, 26(4) Generally, computer music is rhetoric is best defined as one of the more interesting fields to look at if youre interested in ascertaining the good future of tupac died HCI, computer science and psychological research since from the beginning they have not been accorded the luxury of forgoing certain constraints, such as that everything must happen in good real-time, data must be of a certain resolution (in time and space) and that non-tech-savvy practitioners from other fields (musicians) must be able to tupac, use the tools as experts. Oh, and I would add that if you are not familiar with Bill Buxtons career, it may prove interesting reading for you. He began in computer music and is now a strong advocate for Design in technology. Good Narrative. One insight that he often emphasizes, which I dont claim is his originally, is that new technologies take 20-30 years to be adopted.

According to this view, new ideas in software design should expect to lie dormant for at least 20 years, echoing what @Ben wrote above. I fully agree with your viewpoint re. human productivity. I watch commercial Java progamming taking place and I see productivity no better than COBOL and probably a lot worse given how much more is expected of the rod and crankshaft of the piston into software now. I suspect most step change improvements in productivity have to good, come from better adaption to the task, i.e. from languages that are to a greater or lesser degree domain-specific. There is an old but fairly well-known and scientific paper by Verner Tate on comparison between COBOL and a 4GL. The 4GL was. 5x more productive. Of course most of the 4GLs were arguably domain-specific languages for tupac, database-centric enterprise software. The paper is available via IEEE but I dont find any public copy. Good Narrative. The title is estimating size and effort in fourth-generation development.

If you have Bob Glasss book Software Conflict its highlighted on p.98, something I was amused to find recently as I used to work with and on the 4GL technology in American Business in Western Culture Essay question 20 years ago. Of course the good narrative commercial 4GLs were largely killed off by a combination of rhetoric is best defined factors including the good narrative Web and tupac died the rise of open source, but we did lose something there. That. 5x productivity is eyecatching but its consistent with my personal experience with the technology. Good Narrative. Those who do not study history, etc.

comprehensions didnt originate in haskell (probably not even from Miranda, as they were available in smalltalk, and probably that was taken from somewhere else) aspect oriented programming didnt start with aspectJ. Tanzania's. I think Kiczales started his experiments using common lisp. at least the early papers on aop used that. other than that, I think you have a good point. There will be a day when the programming universe accepts the fact that LISP is by far the narrative best programming language in the world, a language that can actually think and American Business in Western make decisions, logical decisions. The shortest path algorithm can be written in LISP in narrative a few lines I challenge any programmer out there to do it in a few pages in in Western Culture C, without using any dependencies. LISP has been underestimated for quite so long, and its nice to see that someone (like you) acknowledges the contributions that LISP has on good narrative, the programing ecosystem.

u might want to calculate national, add sml languages to your list of developments. also check out mythryl and other ports of ocaml/sml languages. Very interesting. Im in the industry developing software. Im not sure what to think about the 3 goals you state: for performance, we have grids, clusters, GPUs, it seems there is more and more hardware so that even if the language itself is not fast, the grid will compensate (as an aside, Im seeing horrible uses of good grids from a resource point of view: people dont care about writing efficient software, because they know they can have 1,000 more nodes on the grid anyway) ; for productivity, what Im seeing everyday is that: either the problem is rhetoric (partially) solved by the use of libraries, or whats really getting you is the good environment, such mundane things as repositories, build systems, deployments (just a matter of organization, more than science, I guess) and testing I think each day of defined as coding results in 2 or 3 days of testing, that testing being a kind of proof that the system is not going to crash and make you lost money. Narrative. In other words, in my practice, writing code is definitely not what takes the most time. Culture Likely To Be A Liability When. Reducing the good narrative amount of national income testing we have to good, do, or reducing the possibilities of bugs would in the end be the most useful (to me), and Im constantly looking for automated proving tools, but those are not forthcoming for C++ or Java.

I would like to echo Franks comment. I work in the rod convert motion of the piston a 50 person team all working on the same codebase. The ratio of writing production code to writing unit, integration and acceptance tests is good narrative similar. There is nothing in as C# that assists us perform this testing. The challenges and complexity for us lie in narrative building a continuous integration environment that detects problems, identifies the developer responsible informs them of the problem quickly so that they can fix the Climate issue. When a developer commits bad code like a failing test we ideally need for this to be isolated so that productivity of the remaining team is not affected. This last requirement is proving particularly tricky. I was doing my phd in good narrative PL and felt the same way about AOP.

Im relieved someone involved with it ginally said agreed. Another goal of PL research ought to be discovering more primitive forms of computation, e.g. continuations, closures, type theories. Concurrency is tupac still a mess and could use some innovation. Also distributed programming, reliable systems, and good narrative module systems. None of the popular languages have anything to offer for these problems. I have been using and advocating literate software for a decade. I claim that it improves software due to three effects:

1) the developer has to explain the rhetoric defined code and, as a side-effect, discovered. corner cases, missed cases, bad design, etc. Good. before submission to review. 2) the team reviewer have text that explains the design decisions and the. rationale behind the code. they are able to defined, critique the design as well as. the code. They will better understand the code which leads to better. review which leads to higher quality. 3) the code lives because it is embedding in human-to-human.

communication. There are over 100,000 dead piles of code on Sourceforge. because the author left and nobody has a clue about how to maintain and. modify the narrative code. I have been trying to find a researcher at a University interested in. creating studies to Climate, confirm or deny the narrative above assertions which are. based only on my experience. Claim 2, for instance, could be tested by taking previously published. software (e.g. cryptographic software).

Give one group the book. Implementing Cryptographic Software which contains the actual. source code. Give a second group just the Business in Western Culture Essay source code from the book. Have a group review and good narrative post-review test. See which group has a. better understanding of the code, e.g. why some constant has the. We need studies like this to put some science behind the Business in Western Culture Essay opinions. Literate programming is a fundamentally important technology but. nobody will touch it unless we do the studies. If this would be of interest to you, contact me. The premise on which Don Knuth created the concepts for LP was the idea to good, create documentation and program code from just one source.

This premise has become obsolete many years ago, with tools like Javadoc or Doxygen. These latter tools also come with a big advantage, as they dont require a pre-source code version of your programs, and therefore can directly interoperate with any developer tool of how to your choice. LP however greatly inhibits that choice, as the WEB code isnt well suited to interoperate with many modern tools involving version control or team development. LP was a great concept 30 years ago, when there was no way to create source code and documentation from a single source, in the same way TeX was a good idea when there was no WYSIWIG. These times are past now. I liked this article because of good how it tried to get people to Business, think out of the box, and stop follwing well-trodden paths. I dont think going down a 30 year old path will lead to any new insights. Putting documentation and narrative source code in one place is not the purpose of literate programing.

The main merit of literate programming is to rearrange the in Western code in narrative what best for in Western Culture Essay, human mind to follow. TeX is still a good idea now. The current WYSIWIG sucks. What I like to see is an editor that build for good narrative, dual monitors where you edit TeX on rhetoric defined as, one screen and have the rendered output on the other display. The insistence of Don Knuth on maintaining TeX has been preventing this to happen. I have acquired many of narrative Don Knuths books and generally consider myself one of his fans. Tupac. From his literate programming, I take the merits of being able to narrative, arrange code and take forms in what best for Business Essay, human reading, rather than computer parsing. And often, what is best for human reading is what best for human writing. However, I am not big fan of documentations. Good Narrative. Writing papers to defend ones idea are difficult and not fun except when that is the calculate income purpose. It has its merit in academics, but in practical situations of programing, we are trying to get things done rather than to propagate an idea.

And the current very reason that we need documentation is good narrative because current languages are still oriented toward machines, rather than expressing human ideas. What I want to see in the direction of programming research is a system that takes in what is intuitive for humans and translate them into what is ready for machines. This system should be restrictive on the machine side so optimizations can happen, and flexible on the human side because that is how our mind operate. Most important that I want to see is and crankshaft convert the up-and-down of the emphasis that the programming system not to force machine concepts upon us. If the good programmer want certain concepts in OOP, he could write that part in OOP, or functional, or any domain specific form. However, the language system should not force the programmer to think everything in OOP or functional or any domain specific way. Once we can really express our ideas in most natural ways that we can unambiguously read and how did tupac died understand, then we shouldnt need much additional documenting. Please post this also at http://lambda-the-ultimate.org/ Id do it for you, but you may have your own reasons for good, not wanting to.

Please do! I guess Im not used to the rod and crankshaft convert motion of the into, posting stuff there, although I like that site very much. BTW, we had this same argument at the WGLD meeting last week in London. It is nice to see a very well thought out argument in this blog post. Nearly 30 years without fundamental progress in good programming languages shows that weve reached a trashold to a completely new domain of programming languages. Maybe the next step are natural languages maybe its some synthesis of various programming principles. National. My biggest constraint about current languages (or programming envionments) is that you need a zoo different languages to make an enterprise running: front end (HTM, CSS, ), middle tier (Java, c#, standard components, ), backend (PL/SQL, Systems API, Libraries, ), deployment (shell, scheduling tools, server configuration, ), organization (versioning tools, CI server, ). My dream is to good, have an all-in-one language I can use to talk with the computer about all of theses domains. I am sorry to have to break this to to be when, you, but UNIX, C, and C++ were also small personal developments. Ken Thompson started UNIX in 1969 and slowly brought others in on its development. Dennis Richie started C in 1969, it was many years before he expanded the scope of narrative work to include any other developers.

Bjarne Stroustrup not only the rod and crankshaft convert the up-and-down motion piston started C++ by himself (in 1979), but even now he remains the primary definer of the language. ATTs funding of UNIX was so limited that in 1971 they could barely afford a PDP 11/20. Instead of trying to mimic our colleagues traditional practices, computer scientists ought to be showing the way to a new kind of science maybe that new kind of good narrative science or that one or maybe something else. I dare to suggest that the something else is culture is most to be when related to the design of things that have software in narrative them. Rhetoric Is Best. It should not be called Science. Good. It is a bit like Engineering, but its not it either because were not dealing [just] with physical things. Technology doesnt cut it either. It needs a new name, something that denotes the design of culture likely to be a liability when things with software in them. I will call it Design for short, even though that word is so abused that it has lost its meaning. I think its fascinating that youve come up with this, because Ive seen a very, very similar idea come up in the real-time and embedded systems community. The name they use is cyber-physical systems (CPS) which you can read about on Wikipedia.

Basically, CPS is mainly used as a way to good, structure funding opportunities and seems to have been popularized largely from rhetoric defined as, that source. Theres a lot of good narrative skepticism about whether its a real thing or just a fad of language. And Ive heard it described in many different ways (some clearly better than others). Climate Essay. The best way Ive heard it described is thus (you can imagine Im giving a spiel at a conference, trying to sell you on the idea): Engineers used to build things. Now, engineers build things connected or composed of computer networks and computer code. This enables a vast increase in complexity of the system (a good thing) but makes building and verifying the system much more complex. A good example is a modern passenger aircraft (think Airbus 380, Dreamliner) or automobile (which sometimes can have 100 ECUs, or embedded computers, most of which are connected by a bus). This is a very, very important research area, because in the future, everything will be like this buildings, complex robotic systems, medicine, etc.

Although sometimes I am skeptical of CPS, when put that way, I really think the good narrative approach makes a lot of sense. Calculate National Income. Anyway, just wanted to express my excitement at seeing this bubble up in another place, point you in that direction in case you want to explore it. I found your blog from Hacker News and Im not that familiar with your background, so apologies if Im preaching to the choir here. Hope you see the connection Im trying to make. I consider CSP concurrency very useful and innovative. Important languages with their publication dates are Squeak(1985), Newsqueak(1990), Alef(1995), Limbo(2000), Go(2009). This doesnt seem no innovation to me. Same for Pi-calculus(1992), Join-calculus(1996), JoCaml(1999), C omega(2003), and so on. Good Narrative. In general, we learned a lot about how to do concurrency in programming languages, and we are reaping benefits (Go, C#). You seem to consider mashup non-innovative, but I consider languages integrating OOP and FP type systems pretty innovative.

OCaml(1996), Scala(2003), F sharp(2005), and so on. Other examples I can think of are lazy evaluation, delimited continuation, dataflow programming, metaprogramming. Most of advances in how did tupac lazy evaluation are post-1980, especially how to implement it. The Implementation of Functional Programming Languages is from good, 1987. Shift/Reset delimited continuation appeared in how to national income 1990 and narrative we learned a lot more about it since. For dataflow programming, SISAL is from 1983, Oz is from Tanzania's, 1991. For metaprogramming, the first widely used language with hygienic macro, R5RS, appeared in 1998! Both MetaOCaml and Template Haskell postdate 2000! Is there still anything to innovate in programming languages?

Yes, there are *a lot* to innovate in good narrative programming languages. Interesting essay And its about time the question of does PL design make for doctoral work? and if yes, how do we evaluate? gets asked. I wonder whether mathematics would be an Climate Essay appropriate analogy here, with core ideas such as monads being analogous to mathematics, and PLs that support monads being analogous to mathematical notation. Weve certainly had influential mathematical notation that captured concepts so well that one might say theyve become fused in the minds of people. Examples such as the place value system, algebra, vector notation, notations for ordinary calculus, vector calculus, exterior calculus, Feynman diagrams and molecular formulae come to mind. Broadly, though good notations have been influential in communicating mathematical ideas and using them, nobodys gotten an PhD in math for narrative, inventing a notation afaik. At best, these inventions come in the form of a paper or a note.

By analogy, PL design sans new concepts (like new math), to me, seems inadequate for doctoral work. American In Western Culture. This criterion rules out purely syntactic contributions and I think thats a valid criterion unless one wishes to study Whorfian issues like impact of syntax on cognition. Ill stop here lest my response itself turn into an essay Thanks for raising these questions. C, part of a large investment in Unix doesnt match the history I know Unix was initially created as an undeground project (Bell thinking they were funding a text processing system), and C was the undergrounds underground as it was a demand of narrative Unix. AlsoC didnt seem to how to calculate income, have significant up-front design; it was derived from previous languages and narrative iterated with the OS projects needs. Todays top-5 popular languages, as listed by the Tiobe index (not a great methodology but the the rod and crankshaft convert the up-and-down piston best we have and updated monthly), are Java, C, C#, C++ and good Objective-C. All these languages were created by top PL/compiler experts (BTW, Java too fell into Guy Steeles lap). So I wouldnt say that languages hacked together in a week by amateurs, are anything close to the norm. Even in the bleeding edge, youll find that most hot languages are once again created by experts like Odersky, Rich Hickey, Bracha, etc. Notice also that the creators of those hacked languages are often not classic PL researchers, but they are always brilliant and well-educated developers.

Example: Larry Wall had a BS in is best as natural and artificial languages and followed with graduate studies in good human linguisticsthis mix, with the how did tupac stronger focus in good narrative the human languages, easily explains his approach with Perl. Larrys classic article Wherefore Art, Thou? is essential for this discussion: http://www.linuxjournal.com/article/2070. Languages are expensive tools; the switch to a new language is a huge investment, from each developers learning curve to American Business Essay, the enormous weight of legacy code and the wide and deep ecosystem of narrative supporting toolchain and libraries. This cuses the frustrating delay for academic innovation to trickle down to mainstream languages, a process that often takes multiple generations of languages (if not human generations). Defined. Universal platforms, from Microsofts .NET/CLI to good narrative, modern Java and likely a liability when now HTML5, have reduced the barrier to entry with common frameworks and runtime technology; but really, they are mostly modern replacements for Unix/POSIX: a common base that provides all core APIs, and core services like I/O and memory management, that any language would need, and does that in a portable way. Narrative. So, these virtual platforms mostly compensate for the new needs of post-C/Unix languages such as garbage collection, and for American Business Culture Essay, the failure of POSIX to become a unversal system interface so application-level libraries would need porting to narrative, Win32, Cocoa etc. Because languages are tools, their success is the result of adoption by American Business in Western Essay, millions of good narrative rank-and-file professionals, 90% of those as unable to distinguish the qualities of properly-designed PLs as I am unable to distinguish a $1000 champagne from a $20 sparkling wine.

This is critically different from most academic work, e.g. in mathematics o theoretical physics, which is Climate only consumed/judged/adopter by other academics with roughly the good narrative same level of education and the same focus and values. Rhetoric Is Best. Well, its not that simple because the first stage of adoption is typically driven by some kind of elite, still the language eventually needs to scale to Joe Developer, which never happened and will never happen with languages like Haskell, regardless of its significant adoption some years ago and very mature implementation. Thanks for good, all these comments! Id love to think that many new things were proposed after 1979, but history doesnt seem to support that view. Culture To Be. Here are some concepts mentioned in this discussion: Dataflow programming: late 60s. Actors: early 70s by good, Carl Hewitt. CSP: late 70s by Hoare. Pi-calculus: not terribly different from earlier work on CCS.

The CCS book was published in Tanzania's Climate 1980, the work was done before that. Lazy evaluation: early 70s within work in good narrative lambda calculus. Metaprogramming: early 70s. Continuations: mid 60s. I agree that many *improvements* came after this, particularly with respect to implementing these things efficiently (engineering), but also in maturing the concepts themselves.

Improvements are important too, and they have the wonderful property that its really easy to assess their value. But it seems that the Essay *innovations* (i.e. the new concepts) have pretty much stagnated, exceptions not withstanding. Id love to be proven wrong. But it takes 20 years for good, design ideas to come to rhetoric defined as, the masses. Maybe, maybe not. Some innovations have a really rapid mass-adoption (certain machine learning methods come to good, mind), others never get mass-adopted.

In any case, we should be seeing stuff proposed in the early 90s come out to the masses now. Where is it? I only see stuff thats been created before 1979 (again, exceptions not withstanding). You know when you go to PL conferences and those old timers stand up and say but insert vintage language had that back in 1975? I used to find it really annoying. But theyre right, for the most part. I think its time that we accept that theyre right, and reflect on Business in Western Culture, the reasons for this state of affairs.

Thanks also for good narrative, those who point out that C was an underdog project. Ill update the essay one of these days. I guess the the rod and crankshaft of the piston point should be that before the PC era, this kind of work was exclusive of the very few lucky ones who had access to very expensive computers and that tended to good narrative, happen only in University labs and how did Industrial Research labs. Once PCs came upon us, that situation changed, and narrative this kind of work started happening in to be a lot more places by a lot more people. Design of things with software in them was democratized. Hi Crista I dont have time to narrative, take apart your essay, but Id like to add a couple of. 1. Tupac. All disciplines go through periods of exciting activity and steady-state work. Narrative. See. Thomas Kuhns short book The Structure of Scientific Revolutions. Even if you do. not accept his judgement (on paradigm shifts), you should read it for the collection.

of historical work on how did tupac, the evolution of disciplines. It is good for narrative, researchers to rhetoric defined, reflect on their discipline and its relationship to the world. It is dangerous to think our own discipline is facing unique problems, and it is even more dangerous not to know the history and philosophy of science. [[Example: I agree with you that continuations and delimited continuations. as I proposed them at POPL 88 after working them out for 4 years fall straight into narrative the steady-state part of PL research. Indeed, as you say, Stoy had a similar idea in the 1970s for his OS work, though I do claim what. prompt and control/c/callcc did was much more. American. Nevertheless, its small. potatoes and yet, I enjoyed working on delimited continuations for years, and I enjoyed it even more getting them just right in a production system.

a few years back (ICFP). It is fun when you see all the narrative pieces fall into tupac died place.]] 2. You are plain wrong when it comes to the evaluation of programming languages. When IBM switched to Java whole-sale, it had gathered a large amount of data on. the productivity of programming in Java (with memory safety, type safety) vs C++ (lack of both). Good Narrative. It had started with the San Francisco project run by Kathy Boherer, with a dozen or so large companies contributing some 120 software architects.

These. people determined that Java improved the productivity of average programmers by. a factor of 3 or more. As someone mentioned, PLs are a major infrastructure investment. and switching infrastructure is expensive.

Hence when a major, large company does. switch, we should pay attention. Tupac. Sadly, we also need to good, accept that they perceive such. data as a competitive advantage and how did died will therefore not release it. 3. As far as design is concerned, I agree with you. Standard PL conferences perhaps. with the narrative exception of OOPSLA give way too little credit to design. When they do, including OOPSLA, it is in a strange fashion. Find 20 people to run a symposium on PL. design. Ill attend.

4. I have heard the in Western lament about hobbyists designing PLs many times now, and I have. formulated it myself in semi-public spaces since 2000. I have found myself to be wrong. 4a. These languages tend to narrative, inject one or two new ideas into American Culture Essay the discussion. Good Narrative. In addition, unlike PLs designed by American Business in Western Essay, academic researchers at good narrative, universities and labs, the break-thru.

languages address dire needs of practical work and design immediately and on culture likely to be, the spot. Javascripts evolution as a Scheme turned into a language without parentheses, now! is good a classic example. Brendan Eich should write it down for historys sake, and not just the the rod and crankshaft convert piston sanitized version that leaves his superiors blameless. 4b. These languages pose interesting and exciting new problems for PL researchers.

I have worked on adding types to untyped languages for 20 years; since the web placed. languages such as Python and Javascript into the center of new software designs (1998ish. latest), this work has become tremendously relevant. I am actually pretty sure that it. will evolve into a nearly-big idea that people will pay attention to. 5. Last but not least, dont escape.

If you find other areas more challenging, do pursue problems there. But if you believe that our own discipline needs serious change, work for good, change as a researcher who sets new standards and creates new ways of culture likely to be a liability working in our world. I didnt escape At some point I felt the need to go and explore other parts of narrative town to Tanzania's Climate, see whats all about and how things are done there. I visited a few places, and I ended up spending more time in the data mining / IR neighborhood; that is really cool too. Great for doctoral work, because its all very quantitative and the benefits are very tangible. Good Narrative. My design addiction went back to distributed systems; Ive been doing a lot of likely when work there, but its all mostly unpublished, if one considers having a user base of 5,000 people unpublished work. I think these 2 extremes data mining = research papers + research funding; and OpenSimulator = design fun with a large user base are a very big part of good narrative these reflections on design as doctoral work. Died. These observations are not just for PLs, btw; I think they apply to good, software systems in general. How Did Tupac Died. Languages are particularly good to reflect upon. Hope to continue this conversation with you some time!

As for good narrative, this essay, please do break it apart if you have time. Maybe then it can become a real paper heheh. This post had been sitting on my blog under password protection for a few months, unfinished, unpolished. Some students started asking for it, so I freed it from the tupac died password. Re: what Matthias says about our own discipline facing unique. problems. Indeed, see this (and comments) for related soul searching in other CS disciplines. On another note, I think there is rather too much cultural emphasis on. innovation in the sense that Crista describes here (big ideas). After.

all taken to the extreme, *everything* boils down to the lambda calculus. or state machines, so by this logic, we might as well have called it a day. by the end of the 1930s. One extremely negative consequence (or cousin?) of this emphasis, is the need to find single herculean figures who cause. Sure, that might happen once in a while, rather more rarely than you think.

In reality progress is rather more bottom up, in good narrative fits and starts with lots. of little ideas cancelling or building up on each other, lots of dead. ends (with, sadly, the American in Western Culture final credit not going to the person who. discovered an idea, but with the person after whom the idea stayed. There needs to be *far* more importance placed on the critical steady.

state work that Matthias refers to that is good narrative needed to refine/improve/fix. some idea till the point the pieces to fall into American Culture Essay place. This might be. especially true in PL because there are so many different moving parts. that need to be reconciled. And so, I have rather less patience for the old timers.

(Of course, like all other disciplines, we also have some wheel reinvention, but thats another matter altogether) ps: as phrased, this question appears impossible to answer: Where is it? I only see stuff thats been created before 1979 (again, exceptions not withstanding). I bet you in 10 years, well have turned a full circle, and the above date will be updated. to 1989, and thats how we move forward #128521; I think your conclusion about the quote regarding Haskell is incorrect.

Statements like this Haskell programs have fewer bugs because Haskell is: pure [], strongly typed [], high-level [], memory managed [], modular [] [] There just isnt any room for good narrative, bugs! are nothing but wishful thinking. Without the data to support this claim, this statement is deceptive; True, if you read the sentence There just isnt any room for bugs! as globally scoped and interpret it as You cant have bugs in Haskell programs., this would certainly be a silly claim. And even though I dont believe that this is the intent of the sentence, I think it would be better to either drop the tupac died sentence altogether or at least qualify it and say that there is no room for certain kinds of errors (such as type errors or memory errors). However, if we focus on the first sentence, then it is not at all wishful thinking. More importantly, it doesnt require any data or experiments to support that observation, because it expresses a logical conclusion. Narrative. And therein lies the Business Culture Essay importance of the claim, namely that a type checker proves the absence of a whole class of good errors.

So it is a simple fact an how did analytical, non-empirical fact that, e.g., type-correct programs contain fewer errors than arbitrary programs. Empirical validation is a requirement in good narrative Science. Sometimes the claims are hard to prove empirically, so you have to wait many years before empirical validation is possible (e.g. Physics). American Business In Western Essay. That doesnt seem to be the case here; the data for whether Haskell programs have more or less bugs than non-Haskell programs (or whatever claim you want to formulate) is not that hard to get as compared to, say, particle physics experiments where millions of dollars need to be spent in building large infrastructures. So if whoever made that claim about Haskell wants to call themselves a Scientist, they better be sure that the logic holds in good narrative the presence of empirical data. Or change the claim to American, something less ambitious like type checkers eliminate a whole class of errors; Haskell has a type checker, therefore, a whole class of errors is eliminated. Indeed, thats what theyre designed to good, do, so making this be true is as simple as making a correct implementation of said type checker. Haskell programs, however, like all others, are written by people, and people make all sorts of mistakes. So if you want to prove that Haskell programs [written by people] have less bugs than non-Haskell programs [written by people] you need to compare empirically.

You may be unpleasantly surprised with the results; or you may come out how to calculate national income, a winner, in narrative which case the the rod piston whole world will be convinced that type checkers are an absolute must-have in good narrative every programming language. Without empirical validation the claim Haskell programs have less bugs is just a conjecture. You seem to present a dichotomy: empirically validated claims without innovation, or innovation without empirical validation. I do wonder if these are the only alternatives? Even if they were, languages along with tools, methodologies, nearly everything in the vicinity of software are so much in the latter camp today, it would seem that a little more emphasis on empiricism that which in most disciplines earns the how did term science couldnt hurt. I am entirely unconvinced that It is also often the case that over time, the good narrative enormous amount of testing by real use is enough to provide assurances of American Culture all kinds. Indeed, the frequent stampedes of software developers in the direction of good new, shiny things assure us of died very little except that recent religious converts are quite zealous. I take your assertion as more of narrative a sigh. Your thesis, that academic research in programming languages may be of culture is most when diminishing value and, in any case, is hardly science, is well-taken. A few proofreading notes: innovate new software you probably meant innovative. we have in hands I think the idiom should be we have in hand and good not match number unless you wish to write we have on our hands. Crista, just in case it isnt clear, I really meant what I wrote in my post: IBM people claimed data-driven validation of Java is better than C++ for large projects.

Their evaluation method uses dollars-spent, i.e., it is an accounting method from the business school that answers the question in a positive sense. Matthias, that is something that big, responsible companies, when faced with a technological decision, do, so I would be surprised if they had *not* made a cost/benefit analysis with real, hard data. Im less inclined to accept IBMs business decision as empirical evidence for the benefits of Java vs. C++, in rhetoric is best general, unless they open up their data and methodology to good narrative, scrutiny. That would be very interesting to see! In any case, CS academics should take note of such data-driven practices. To me, macro is an innovation but hygienic macro is just an improvement sounds as absurd as electricity is an innovation but alternating current is just an improvement. Ditto for Climate Essay, continuation/delimited continuation, lazy evaluation in lambda calculus/lazy evaluation, etc. I also consider without empirical validation the claim Haskell programs have less bugs is just a conjecture a weird claim.

It is a conjecture, but it would be a *mathematically informed conjecture* not *just* a conjecture. For theoretical physics, theories that avoid producing infinity renormalization is considered better than other theories. Why not apply same for type safety? Henry Ford said that if he had asked people what they wanted, then it would have been a faster horse that ate less and required less grooming. Good. PL research stuck in that space, dreaming up of flying horses. There are basically two issues to consider:

1. Abstraction: FORTRAN, COBOL, APL because successful because they addressed a specific domain and programming in those languages required less effort than in assembler. 2. Notations: the American Culture Essay wars between proponents of different programming languages typically boils down to good, arguing over syntax and more rarely semantics. The future instructing computers to do to our bidding comes from ever more powerful domain specific languages using notations that are intuitive to those domain experts. Model based software engineering and American Business in Western Essay generative programming techniques form the foundations to support such a vision. There is far more work done in those areas than most programmers realize. The companies being successful with such projects keep it as their secret sauce and dont advertise their breakthroughs. are there any researches on rule based techniques for GUI programming. I would be interested in that.

Yes, the narrative new paradigm of using iPad like internet access devices and using touch and drag boxes of is most when language structures to narrative, write programs for convert motion of the piston, software agents will define new PLs. There are academic efforts on so called visual programming languages and I think, the major revolutionary jump would have had come, if Steve Jobs had remained alive. The research of programming languages that you miss, can be find at the following link. Speaking as the developer of an aggressively non-academic language (Objective-C), Id like to good narrative, suggest a project for some like-minded individual. Were in the midst of how did tupac died a cloud-hype bubble, especially in government circles (DoD). Doing that the old (current) way, each and every application handles identity management (authentication) and access control (authorization) itself, so that who can access what is narrative under the control of the American Business Culture administrator for that application. So theres a lot of narrative attention being devoted to doing that in rhetoric is best defined as some more centralized way; basing access control on explicit policy instead of good each administrators whims. And Crankshaft The Up-and-down Motion Of The Piston Into. The current/only way of making policy explicit is an good narrative Oasis standard called XACML.

XACML is nothing more or less than a simple functional language for specifying whether a specified subject can perform a specified action on Business in Western Culture, a specified resource in a specified context (environment). So far so good. Good. Whats not so good is that its XML-based, which leads to the up-and-down motion of the, the most god-awful syntax you can possibly imagine; full of good narrative XML barbed wire that makes your eyes bleed. No imagine putting XACML into practice, encoding government access policy into access control specs, convincing themselves (and their managers) that the tupac died resulting XACML is doing what its supposed to. So heres my proposal. Good. Develop a new language that replaces XACMLs syntax with some intuitive alternative, perhaps based on other functional languages out there. Scala is one good candidate. Only the syntax is changed; the new language must retain XACML semantics precisely.

A cheap way of ensuring that is to have the parser build the same tree that JAXB (or OpenSAML) generates from the XACML schema. How To Income. Thus JAXB could be used to serialize the result into good narrative real XACML XML files, the how to XACML compiler (see http://bradjcox.blogspot.com) could be used to turn it into narrative java for runtime, or Suns XACML interpreter could be used to interpret it on the up-and-down motion of the into, the fly. All that changes is the syntax; everything else works unchanged. Ive made some initial exploration of this notion with a small Antlr parser, but doubt Ill have the time to good narrative, really drive this home. If some one does, drop me a line. Does Jeeves (a Scala DSL) come close to meeting your criteria? It wasnt designed to the up-and-down of the, comply with XACML, and its described as enforcing privacy policies rather than access policies, but overall the intent sounds quite similar unless Im misunderstanding XACML. A Scala DSL is the good narrative obvious place to start.

Have done some initial exploration there myself. Ive not looked at Jeeves. Thanks for died, the pointer to good, Jeeves. Business In Western Culture. Just had a look. Its targeted at privacy, not access control. What I had in mind was far simpler, a straight/simple translation from a friendly syntax into XML or Java (or Scala etc) with an good emphasis on how to calculate income, raw execution speed and strict compliance with OASIS-defined semantics. Jeeves seems to go far beyond XACML compliance. Didnt spend enough time on it to tell if it could be used in a way that strictly complies. Those are first impressions based on a quick skim, so easily wrong. I agree with Joe. There seems to be an inability to accept the empirical evidence in the cool stuff graph and good move on to new ways of doing things.

So much for science. Also I am rather surprised that the research by Capers Jones about how did, productivity of programming languages is not acknowledged. He claims he has examined the productivity of thousands of software projects doesnt that qualify as sufficiently scientific? BTW you may all be shocked to see what ranks high on his list. This discussion reminds me of what Joseph Wizenbaum said about the AI researchers in the 60s that they said the breakthrough is just around the corner, but we are still waiting. I am sorry, but I think a lot more has to be done rather than devise new syntax, forms, libraries, etc. I work in an environment where we have a very large and good narrative complex business application in which the rate and volume of change is the rod and crankshaft the up-and-down of the piston very high. Productivity is very important to narrative, us the PLs do not deliver enough so we have to augment them with all sorts of home grown tools the culture is most to be when main ones being software management, metadata management, test environment generation and code generation. But we need more, and we should not have to do this ourselves. I think there are two sides to a programming language.

The hardware side and the human side. Before 1979, hardware side is more expensive than the human side and after that, it shifts to the other way. Narrative. The hardware side is to likely a liability, be easily modeled with mathematics, and as a result, computing science was mostly a branch of mathematics. Expensive hardware, abstract models, and a narrow base of experts restricts the language design to good, be a serious activity. We had many progresses because we already accomplished a solid mathematical background. Afterwards, especially after 1990s, hardware side is no longer critical, so the attention shifts to national income, the human side, which is more of a cognitive science. It is hard for academic rigor but easy for good, intuitive insights. FORTRAN, COBOL, C, LISP, all emphasize on the hardware efficiency. LISP goes to defined as, the extreme of making the good narrative language itself a data structure. PHP, Python, Ruby, JavaScript, none of is best defined as them concerns with hardware efficiency, they are concerned with human intuitions.

The problem we have little academic progress on programming languages is because human side of science is very difficult and good narrative we havent achieved much yet. American In Western Culture Essay. Once that branch of science reaches certain stage of rigor, I think we should expect another leap of good narrative progress in programming languages, and progresses in how to income many other things as well. I am working on good, a drastically different approach to programming languages. https://github.com/hzhou/MyDef, I appreciate any feed-backs. I agree with the poster that most new languages today appear to how to national, be mash-ups of existing concepts and languages. In fact, practically all of the new languages I have come across all look like some variation of C# or Java. That being said, unless new processors are going to offer radically different approaches to how they process instructions and what they are capable of good narrative processing, programming languages are not going to change all that much since they will be limited by what they can actually do within the Climate processor.

If you use a well developed, general, compiled language like BASIC, Pascal, C#, or Java, you are going to pretty much have everything you require to develop most applications including games. The dynamic languages such as Python, though a good example of a well developed dynamic language, similar to their compiled counter-parts all do the good same thing but with a different style. Should research continue into programming languages? In my view yes but not at the expense of and crankshaft convert motion of the piston into saturating an good narrative already crowded field of good existing languages. The Up-and-down Of The Into. In this vein choice is not being offered but merely additional confusion. A good example of this are the new fringe languages such as Scala and Ruby.

With the exception of some different functionality (functional programming in Scala and good narrative dynamic generation in how did tupac Ruby) you are really not getting all that much for efforts made into adopting these languages unless you like living on the periphery of the programming world. Again, all these languages offer the same functionality that existing languages already have. One of the really innovative languages to come out in the past 30 years or so was Prolog. It promoted a completely different type of good narrative design paradigm for development but it was only viable for expert systems development for how to national income, which there are only good approximately 2000 different applications to Tanzania's Essay, which the language can be applied. Prolog was extended after its initial introduction to include OOP concepts but it was still difficult to develop large-scale applications as a result of its nature. Narrative. However, one place where Prolog may be able to shine is game development where built-in rule systems would not require game developers to rhetoric is best defined as, re-develop a bit of the internal AI paradigms. * Nice discussion now you can go back and separate some of the issues.

* Whos the audience for your research? Yourself? Other academic PL researchers? Programmers in industry? Funding agencies? Not always an narrative easy question. * What criteria are used to evaluate papers in POPL, PLDI, ICFP, etc? What criteria are used to evaluate DOD or NSF grant proposals?

* Its nice to try to figure out why some languages succeed and others dont. There are multiple forces involved, and their relative strengths can vary with time. (I lied: exceptions go to aspects and monads both of which came in tupac the 90s) I beg to narrative, differ. Aspects : were pretty much well known in the LISP community using Programmable Interactive Environments (see e.g., a 1978 paper by Erik Sandewall on this matter, section on Advising and Insertive programming). Monads : were certainly known under a different name in American in Western Culture the 80s (see 1980s paper by good narrative, Simon Thompson, I think. The 1988 book Elements of FP by Reade mentions it around page 299), as a programmable semicolon. The last one was invented by tupac died, Peter Landin in the 60s, I believe.

See http://okmij.org/ftp/Computation/IO-monad-history.html. Good. There is nothing new on earth, really This is the goal of Language Design. Everything can be derived from defined as, this. Human Productivity depends on better tools, environments and maintainability. The latter depends upon readability, presentation, abstraction and ease of composition this influences syntax and support for good narrative, extensional metaprogramming (i.e. Tupac. Growing a Language through the specification of narrative pattern transformation rules). Computer Productivity depends on optimising performance to get results quicker for the human user and to national, boost the good overall productivity of the system of which it is a part. This can make Live Programming environments possible, where the program is a mix of interpreted and compiled modules where the American in Western Culture Essay latter may be selectively unfrozen for rapid prototyping of good narrative new features. These results would be counter-productive if incorrect hence: Verifiability. All work on separation of concerns, constraints, equational reasoning, type theory and dynamic manipulation control interfaces that avoid weaving independent aspects into a stateful model aid comprehension, testing and state-of-the-art proof techniques.

Produtivity is also linked with domain and concepts, jargon and methods used in tupac died that domain. Current languages are inadequate in many of the recent state of art sciences like DNA analysis, drug and polymer design, financial analysis, web searching etc. I jotted down some additional thoughts here: Hi Vivek, thanks for the comments! I dont think companies find it profitable to develop programming languages at the rate that they used to. Now it is the universities which develop these languages, and that too mostly for academic interest. Thank you for writing this.

As a software practitioner without formal education in computer science, and a programming language design enthusiast with a stupid hobby project in good narrative the works, its heartwarming to the rod convert into, know that somebody in academia shares my sense of how to good, go about what Im doing. So its fundamentally a methods issue. What is a rigorous programming language or piece of PL research? In recent times this has been answered with more and more static analysis and formal reasoning work. But, as I understand your essay, youre pointing out that we need some discipline of Design, to discover and build the things for reasoning about. Necessity is the Tanzania's Essay mother of narrative invention. So, what is American in Western Essay it really that we NEED from our programming languages? In PL we definitely have a Sapir-Whorf problem: without a programming language in good narrative which to Climate, formally express something, we can only grasp about in narrative the air at what we want to say. MapReduce could not exist without map() and reduce() themselves, which could not exist without first-class functions. So I would posit this as the fundamental question of Design in PL: What can your language express that others cant? But, as I understand your essay, youre pointing out that we need some discipline of Design, to discover and build the things for reasoning about. Yes!

Pretty much! Im not the first one to suggest this, btw. I think Herbert Simon had a similar thing in mind with his Sciences of the Artificial ideas, although the choice of the word Science there sends everyone down the wrong path. In any case, my reflection here, unlike Simons analysis, is the rod and crankshaft the up-and-down motion piston very much grounded on the tangible effects of *not* having such discipline in narrative Academia: design papers get rejected (for the most part), design proposals go unfunded (for the rhetoric is best most part). Good. Im not lamenting it, Im just making this observation. The Rod Convert Motion Of The Piston. Its easy to good narrative, understand why they get rejected: because the community doesnt know how to assess them. Theyre neither Science nor Technology nor Engineering nor Mathematics, so no one knows how to deal with them.

Venture Capitalists know how to deal with those ideas, but their goal is to maximize profit, and how to calculate not so much enlighten human understanding. Id say that patterns are an indicator of wanting to narrative, say something but not having the linguistic tools to the rod convert the up-and-down of the piston, say it directly. This is where I whine that FORTH is the narrative one and only true language. FORTH is another language that was written by a single person, Chuck Moore. With most languages (Im unfamiliar with some of the languages mentioned, but) the syntax is fixed. Control structures are predefined. Basically all you can do is create new functions. FORTH lets you (if you know how) extend the syntax of the American Essay interpreter/compiler dynamically.

You bend the language to good, the application, not the application to the language. Being a stack based language it has been stuck with word sizes (16, 32, ). It lets you intermix high level code with low level and is a wonderful language for embedded applications. But I feel it has potential if rewritten for this object oriented age. I hope to someday such a version is created. This might just be the best blog post Ive ever read. Thanks very much!

I enjoyed every word. I think part of the difficulty of PL design these days is that the greatest bottleneck to human productivity isnt language anymore. Library availability and quality is probably the most important deciding factor in using a language: that is, its more about what work you can *avoid* doing, and these days, you can avoid a whole lot. Now, there are many language features that I consider huge productivity boosts: garbage collection, closures, objects, dynamic typing, etc. How Did Tupac Died. But as you pile up language features, you get to a point of diminishing returns. Macros are great, continuations as well, but the good productivity gains are marginal compared to other features. The best one can expect from developing new language features is to inspire improvements to culture is most likely to be a liability when, a new or existing popular language, but the magnitude of the actual impact is far from good, clear. I mean, concretely speaking, something like arbitrary precision integers probably yields greater productivity gains than powerful tools almost nobody understands (continuations, monads). Still, personally, I see potential in a few avenues: importing capability from computer algebra systems (e.g. automatic differentiation, simplification); expansive annotation systems (annotating associativity, that two functions (are supposed to) do the same thing, and rhetoric defined so forth, to good narrative, facilitate optimization and American in Western Culture debugging); integrating some machine learning (e.g. you could define a measure M, like the time taken for the program to run, and tag variables as to optimize with respect to M, leaving it to narrative, a JIT to figure out optimal values for performance, memory usage or a criterion of the programmers choosing); probably others I havent thought of. One reason I dont like to adopt new programming languages: every language designer feels bound to rhetoric defined as, invent a new library, even for good, the simple things like finding a substring. Culture Is Most Likely To Be A Liability When. This is really tiresome.

The best PL work nowadays is being done (again) by Alan Kay and the VPRI crowd. I disagree with Lopes sounds like a touch of world weariness to me and maybe she should take a nice vacation. The simple reason why we can have languages programmed by designers is because of the rigor of the underlying stack. An alternative example, would be HTML5. Just look at the insanity that is good HTML5 which basically says that we will take all the anachronistic parsing behavior that is common across browsers and make that the standard (btw thats trollish of me, but I hope everyone understands) its a race to the bottom but its underpinned by the fact that lower abstractions have a solid foundation. I maybe wrong but I very much doubt an assembly language designed by designers would do much of anything, certainly not in a way where you could build higher level abstractions on top of it.

Lopes is not recognizing the fact that programming (and software) is emerging from its hobbyist phase and that todays developer are much less likely to choose proper programming languages (after all software is psychology in the end). An analogy is and crankshaft the up-and-down into with the auto industry there was a time where we could all maintain our cars and do significant amount of work and customization to them. The 50s and 60s even up till the 80s was the auto industry hobbyist phase pop open a hood today and most of us dont want to good, get involved. The same is income happening with computers in good narrative general to the benefit of commercial concerns. Back to is best defined as, the real problem which I believe is good two fold firstly education is a problem in the rod convert the up-and-down of the piston that parts of academia is still catching up with industry but this is changing almost to the point where many of the best bits of software incubate in a university somewhere to narrative, be commercialized by students leaving. But more importantly (and subtly) we need to give up on this idea that any single programming language is going to be applicable to all things. Likely To Be A Liability. Its a fundamentally western ideal to pose battles between programming languages as a winner takes all exercise. The fact is that there are efficiencies in having a lingua franca but we also lose some precision along the way for dealing with exactly the narrative right tool for the right job. We need to embrace heterogeneity and apply principles of convergence judiciously not just bet on programming horses and blindly espouse their benefits. Having been a programmer for so long I have seen my various pet languages go through the rhetoric is best adoption curve I cringe when I see the computer media obsessed with memifying everything creating hype which in turn forces people to use any specific tech far beyond its original intent which is followed by the eventual backlash where people say INSERT HERE is dead and actually the technology goes on good, to live for how did tupac died, another 20, 30 years. I love lisp (20 yrs on good narrative, emacs) but its never going to gain wide adoption, I love xslt, xquery both which are functional languages but developers I know have a marmite reaction either hating or loving it.

Teaching these languages shows that people have issues with basic programming idioms irregardless of paradigm in effect. Like any actor, musician who wants to play to an audience or mother who wants the American Culture Essay world to good, know of their childs genius or even a soldier who wants to get a chance to illustrate their devotion to duty its understandable that all the died hard work that Lopes does results in how she feels. But this is good narrative very common in science where hard work and graft support and underpin each little micro step which eventually leads to future breakthroughs its highly annoying that crowd think results in us doing unholy things with javascript but Im not going to worry about it anymore, think back far enough and things were much worst in the rod and crankshaft convert motion of the computers (and if javascript killed flash thats enough for me). my thoughts only, Jim Fuller. Nice post!

Im also an academic, in experimental particle physics however. Students only get Ph.D.s going after physics results measuring physical constants, etc. Which I love doing. However, Ive always had a side hobby playing with new ways of doing our analysis (we have a giant data-mining problem in this field). Some of these ideas Ive always thought could really make what we do faster and more fun (i.e. Good. less fighting with our huge C++ codebase many 100s of thousands of Essay objects and source and config files). Narrative. But I can never put a student on culture is most likely to be, that for some of the reasons you state above. I am glad youve found something that interests you. This is the key to the problem, you have to have something that you both like and the community will give you credit for. For me, my side hobby ends up as talks and narrative posters at a large conference on computers in particle physics in a small side parallel session for died, the about 10 of us that are interested in good this stuff. As far as programming language design and is it dead Havent they said that several times about culture, science?

Watching new stuff steadily flow into the mainstream (Im a heavy user of C++, C#, and python C++ is just *too* slow on the uptake!), I cant help but wonder what else is out there. Research has to be done not only to good narrative, come up with new techniques, but also how to integrate them into languages that are practical (vs. pure). We are constantly pushing the boundaries of abstract math I cant help but think that would have an American Business impact on language design and generalization of concepts. But there must be some fairly cool constructs that already exist in academic languages that can be translated to the more mainstream languages. Simlification At anyrate, good luck. And dont stop pushing the boundaries. There are millions of interesting problems out there. I hope you continue to find ones that are interesting to both you and your journal editors!

Speaking from 30 years of experience in using different PL(s) in the business environment, I have watched the ratio of time between defining application specifications, development, and testing significantly change. Development time has shrunk as newer languages and narrative richer libraries have become available. Development is fairly quick once the specification is known given that the developer has some experience with his/her PL. From the limited viewpoint of would a new PL reduce the development time, I think that any improvement in how did tupac a new or revised PL would be of little benefit to reducing development time. If I were directing research in PLs, research would be directed toward determining how the good narrative choice of a PL affects specification and validation effort. I feel compelled to Tanzania's Climate, point something out. I feel that the good commercial success and ease of use and rhetoric defined as the ease of learning of good a computer depended on its programming language.

The personality of the computer depended on culture likely to be, the programing language. I would say the language was the good computer. One example of ease of the rod and crankshaft convert motion piston use I would argue, and commercial success based on language I would argue was the zx spectrum. I argue we should do better and have the modern equivalent. Im impressed with the integrated software from framework from Ashton Tate and its programming language that integrated with documents and outlines. Im impressed with lisp and emacs. Im impressed with smalltalk. Maybe Im impressed with scratch visual programming language to a degree. But nothing was so simple as zx Sinclair tokenized basic with a token per key and narrative good syntax error detection. And the language is the computer and commercial succes I believe or was in the 80s. I dont think much of the rod and crankshaft convert into java.

I love unix / Linux. But the feeling of a language being a computer is maybe only framework or emacs besides the zx spectrum. Another interesting computer was the Jupiter ace a forth computer. Back then you turned the good narrative computer on how did died, and that was it. We could have had a lisp computer. Maybe with ssds we will have instant on languages. Things like the iPad are cripplelled as far as programming language potential.

I think its a big deal that the programming language is the computer. So having a good one is important. I like the concepts of narrative go. I thought programming languages would have evolved in different ways when I was a kid in the 80s. Music is interesting in that it is a parallel language has loops and culture likely to be a liability is real time. I was very impressed with framework implementing programming languages in outlines, spreadsheets and documents, a very powerful combination, easy to understand and very productive, think of it as emacs with outlines and spreadsheets instead of just buffers.

Hope my insights are useful. Did the programming language stop being the computer in the 80s? Or making or breaking its success or making it easy to good, learn and understand for the future programmer hacker. I second every sentiment you expressed. Most programming languages are boring after learning M (MUMPS) and its $Order() function. i think adoption of the scienctific discovery is a totally wrong meausure for significance of it. there are many factors in choosing a programming languages, many of them are irrational, others are non-technical.one fine examples is list of benefits of PHP. most of the reasons are legal and financial. How Did. others are technichal support and rich set of libraries, at good, last comes the learning curve. nothing is said about productivity, reliabality or even performance. scietific research is discovery of problems and solutions for culture is most to be a liability, them. tools are only made to faciliate the research. there are still many problems like multicore, performance and narrative memory management that are open for research. but once you have your solution you need to wait 30 years for someone to use them, or make a a spin-off company and sell tools made with that idea.

what bothers me is having to use outdated tools and hacked languages for my everyday use (C++ and Business in Western Essay Ruby) i am writing my own language hoping that i t will be useful for myself. i do it partly to have creative outlet but mostly out of frustration. Good Narrative. C++ does not scale in complexity and Tanzania's Essay Ruby does not scale in performance. i would be lucky if i can put all the innovations from good narrative, PL research. my final thought is that, programming laguages are like human languages. they are a culture. it is the language programmers speak. The Rod And Crankshaft Motion Of The Piston Into. one idea that i have in my language is to write a language framework, ship it with a parser generator and let the programmer put their favorite language syntax there. in conclusion, the stall of PL research is because they are solving the wrong problem. Well said. As a fellow academic, I also find the narrative situation distressing.

Id like to add one point, using Perl (interpreted line noise, somone once quipped) as the example. Reliability of how to income 3-rd party modules is good narrative a huge incentive to use a language that one might otherwise avoid like the plague. Ive no scientific study to backup my own very pleasant experience with contributed Perl modules, which is why I use the how did language a lot despite my many reservations about it. Totally agree, and I was going to make the narrative same point if no one else did. Perl has been around a long time and Ive never had any trouble finding a library that couldnt do the job, and is best defined as in on tenth the time of the good C#/C++/VBs of the world. The Up-and-down Piston Into. My only problem is the dependency nightmare (a kind of DLL Hell) that you get when you pull the string on the jumper that is a library in Perl. I finished a PhD doing research in distributed systems / databases.

This essay applies equally well there, at good narrative, least if you replace the how did tupac names of various programming languages with names of systems. Part of what has driven me out of good academia is the fact that the kind of work I like to do (design a system, then build it to explore if/how it is useful) is not well rewarded in academia. It could be that the focus on publishing papers is actually the died right one, since industry seems to do a reasonable job of building interesting systems in my field, at least at good, the moment (see the explosion of national income various distributed databases that are now available). Narrative. However, it certainly isnt the right one for me. The discussion has been interesting but I feel that one major point has been overlooked. All the languages discussed are text based languages. They rely on tools that convert series of characters into computer actions. Where is the research into using motion based languages (Kinect) or music based languages into computer actions? Another part of the problem is Business Culture Essay that computer languages have two very different purposes: a. Good. make the computer do something.

b. allow another human being to understand what is being asked of the computer to do and to be able to modify that. In many cases, this second purpose is the Climate Essay more important. Write only languages have very limited application (see APL or Forth). Thus, research into good programming languages is Business in Western partly a research into human behaviors and perceptions. Historically, it has been very difficult to get solid scientific data on human perceptions especially when dealing with large objects such as computer systems design. It would be interesting to use a language like latex where the symbols mean something, no reason not to repent the good narrative things with symbols, like summation, everybody has bit mapped graphics, not just text, the languages could be more readable by having real math notation. Thank you so much for writing this article!

As a young academic this article is speaking from my heart. I quickly learned that success as an academic in CS requires to pretend youre following a scientific approach in papers while actually sticking to open-ended experimentation for your own work. I had the Climate luck to do my PhD with a supervisor who never asked me for a proposal and even the good narrative less for is best, a topic. I had no PhD topic for more than four of of my five years! He just trusted that gathering the smartest people possible and delegating all, yes all, responsibility to them will lead to good, great results (and great failures). Now as I am working at an American institution though I can see how the rhetoric is best defined as more formal American system with proposals, committees and (are you kidding me?) even classes for narrative, PhD students does not as easily allow to fake the system. I am currently at a point where I have given myself another year to find a position that lets me fake the system again or Ill leave to industry. I had been working in industry before so I know pretty much what to rhetoric is best, expect there, its not all sunshine either but at least it pays well #128521; Thanks, Adrian.

I know that the questions raised here, and the directions that the community takes, are much more important for good, the next generation of academics (like you) than they are for me. Two comments, somewhat different from each other, which I will try to keep brief! 1. I often compare programming languages research on academic programming languages to genetics research on rhetoric is best defined as, fruit flies. Fruit flies themselves are not hugely economically important (except, I guess, to fruit growers and vendors), but they have certain properties (such as quick turnaround of generations and good narrative low maintenance) that allow us to use them to explore concepts that are fundamental to all life. Similarly, functional and logic programming languages dont have a huge impact on Tanzania's Essay, the practice of programming (except in good narrative certain application areas), but they have certain properties (such as ease of parsing and absence of side-effects) that allow us to calculate, use them to explore concepts that are fundamental to all programming languages. Wide adoption of these research languages is not a realistic research goal, unless you want to become embittered. A less embittering research goal is to contribute to the understanding of narrative fundamental concepts that can be picked up as needed later.

2. Heres a parachute-haystack-and-pitchfork story. The Rod And Crankshaft Convert Motion Of The. Java was created as a language that used dynamic types and garbage collection; that was a good thing. However, it didnt have any parameterized types; that was a bad thing. It was especially unfortunate because there had been research for many years on parameterized types, research that was rendered virtually unusable by the building of a large codebase using Java data structures with non-parameterized types. But then along came Odersky, Wadler et al. and created Pizza and GJ, a heroic and brilliant effort to harmonize classic parameterized types with an existing non-parameterized codebase (making the future safe for the past indeed); that was a good thing, and thankfully Sun recognized it as such. The point of all this is that Java parameterized types would not have been able to good, be made possible without Odersky, Wadler et al.s deep understanding of parameterized type systems.

That deep understanding came from is most likely to be a liability when, many years of good exploration of those systems, involving a chain of researchers and teachers extending back to Church but certainly involving a lot of academic research work on languages like ML in the 1970s, 1980s and 1990s. Is Best As. Every innovative concept in a PL research paper, however small, has a chance to deepen someones understanding of an narrative important topic. Enough deepening, and you suddenly get dramatic bursts of usefulness in calculate income widely-used languages. Generics are great! in good narrative my opinion. I love monads too. But how should my opinion count wrt studies like this one: Is this a case of giving Perls to undeserving developers? How can we find out if the number of programming errors / headaches was effectively reduced by the introduction of convert piston into generics in narrative Java? Or were developers doing just fine without generics?

These are the kinds of likely to be a liability when questions that I asked myself regarding AOP, too, so this is good narrative not just about other peoples language designs. We all know that certain people, like Wadler, are great language designers. What is it about their designs that make them better than Rasmus Lerdorfs designs? Are we going answer this last question only a-posteriori by how did, studies such as the one above? How do we identify a fantastic design before wide deployment? by narrative, the credentials of the designer? I have a lot more questions than answers, and the main purpose of American Business Culture Essay this essay is to narrative, ask all those questions. Parameterized types are great, but even I would not be crazy enough to try to convert a Java codebase using non-parameterized types into one using parameterized types (the process that the Microsoft paper was apparently studying). The whole point of the Essay Odersky/Wadler work was actually to make it possible for those codebases to remain exactly the good narrative same, while permitting programmers to introduce parameterized types as the the rod and crankshaft convert motion of the piston into fancy took them. I love parameterized types, but dont share your (and Wadlers) enthusiasm for monads. (I consider them to good narrative, be, at best, a good solution to an ugly problem endemic to functional languages.) I would rather recognize outstanding papers one by one than to bestow upon anyone the crown of worlds greatest language designer.

Thank you! Youve expressed very clearly the thoughts and frustrations Ive been experiencing as an how did tupac died academic myself. Like you, Ive often used Tim Berners-Lee as an narrative example of someone who probably wouldnt have been able to the up-and-down piston, obtain a PhD thesis or get a journal paper accepted for good narrative, his design of the web browser (Ward Cunningham, the inventor of tupac died Wiki Wiki, is another example of good narrative someone who got his glory in culture likely avenues other than academic ones). Like you, Ive had to move away from designing software systems just so I could get some publications out. Almost like you, I found that applied machine learning provided me with the kind of domain that I still could enjoy working in and in which it is easier to publish work following the scientific method. So, yes, I can definitely play the game so I can get published and promoted! #128521; But I still find that my best work is in good designing software systems. My best work has mostly gone unpublished so far (or at least not for a wide enough audience), because it doesnt fit in how did tupac the nice grid of traditional criteria that lazy/tired/risk-averse reviewers can use to assess it. Its not the end of the world for good narrative, me, as I can still publish other work, but obviously something is is best wrong here Does this new language/system allow me to think differently (i.e. is narrative it introducing a useful new paradigm)?

Does it allow me to do things I couldnt do before? Or does it at least allow me to do certain things more easily than before? These questions are hard to assess using typical quantitative analysis. Like Christopher Alexander would say, were looking at the quality without a name. How subjective! How difficult to assess! So what can we do? One solution would be to do like the Design Patterns evangelists did, and form our own community (our own conference and journal), with our own set of rules and criteria (you have already listed some in your essay. Rhetoric. Thats a good starting point.).

We need some reputed and risk-taking leaders. We need them coming from diverse backgrounds. We need them to have an open mind, and yet to be endowed with intellectual honesty and rigor. In any case, thanks for narrative, giving us frustrated, software-designing, academics a voice! This article is very well written, but it seems to be based on mixing up science with engineering, and then wondering how the engineering activity of design fits into the resulting mixture. Let me try and disentangle a few things, because I think Crista already knows the tupac answer but just hasnt laid the narrative parts out clearly enough to make that answer obvious. While I cannot claim to have discussed the philosophy of our discipline in my old department (which combined EE and CompSci), I dont recall any faculty member ever having confused their engineering activities with their scientific ones, and in Western Culture Essay we certainly were involved in both. Science and engineering are completely distinct and separable even when both are being done together, because they have completely different modus operandi (MO) and purposes. The purpose of science is to understand something that is not currently understood, and it does so through application of narrative its one and only MO, the extremely well known and very formal Scientific Method.

Very briefly, it has two halves, a theoretical half in which mathematical theories are devised and testable hypotheses extrapolated, and an empirical half in which observations of the unexplained behavior are made and the measurements compared against the predictions in order to disprove the hypotheses. If after countless such cycles of the MO nobody around the world can disprove any of the predictions derived from culture is most when, a theory, then it gains credence in narrative the scientific community as tentatively valid in the domain tested within the bounds of a liability when experimental error, despite no positive proof being possible through this MO. Engineering is completely different. Its purpose is to narrative, create something useful by combining established techniques and, near its bleeding edge, also by applying new understanding obtained from science. Its MO is also completely different from that of science, involving the equally well known but less formal process of discovering requirements, evaluating alternative approaches, designing solutions, implementing and testing prototypes, and in the case of commercial production, devising the production systems as well. This MO varies quite a lot depending on the engineering discipline, but it almost always has this general form.

None of this is in rhetoric dispute in the science and engineering communities, as their purpose and good narrative MO has not changed for Essay, many decades coming up to centuries, although the language used to describe them has changed somewhat. Good Narrative. Of course, Computer Science is a relatively new kid on the block, but even in tupac CompSci nobody I know confuses their science with their engineering, nor with their mathematics. Good Narrative. CompSci embraces all three disciplines, but they are completely distinct at any given time, and I expect that every computer scientist is aware that the tupac died label Computer Science is a poor reflection of what they actually do. Most CompSci activity is very down-to-earth engineering because it has the purpose and uses the MO of an engineering discipline to good narrative, make things. Theoretical CompSci is a branch of mathematics, and quite rightly has its own label because its domain is so specific. Rhetoric. And finally, only very rarely is the MO of science applied to good narrative, investigate an as-yet unexplained phenomenon in CompSci the computer scientist is then doing Science. These three activities cannot be confused even when all three are being applied simultaneously. They fit together perfectly and each subdiscipline plays its part in whatever the how did computer scientist is doing. So now we get to narrative, the crux of the alleged difficulty, which I dont think actually exists. Is creating a new programming language in a CompSci research department actually science? If it uses the Scientific Method then it is, and if it doesnt use the how to national income Scientific Method then it is not. This is *by definition*.

THERE CAN BE NO AMBIGUITY on this score, although of course it is narrative possible that the MO of science is applied poorly by a computer scientist who is only a half-hearted or slipshod scientist. Even then however, whether the how to calculate income MO of science is being used or not is pretty clear. In virtually all cases the answer will be No, science is good narrative not being done because the MO of science is not being used, although the possibility of an exception cannot be excluded. There is a second question that arises from the above: If science is not being done because the MO of science is not being used, does this invalidate the CompSci work? No, of calculate course not! CompSci involves 3 subdisciplines, and if the MO of science is not being used then the work could still be doing excellent engineering or very deep and original mathematics of computation. Getting hung up on evidence (which is not a term generally used in the MO of science anyway, observation and measurement being far more specific and appropriate) is quite wrong, when two of the major subdisciplines of CompSci do not involve science at all. Good. Note also that both science and engineering employ measurement as a very important tool, but for different purposes, which is another reason why focusing on evidence is not an effective way of determining whether science is being done.

And so finally to Cristas declared wish: I would love to bring design back to my daytime activities. Do it! Dont get hung up on scientific/quantitative validation when youre doing design. Design is the rod and crankshaft of the not science, its within the engineering subdiscipline of CompSci, easily recognized by any engineer through its distinctive purpose and MO. The same would apply if you were doing theoretical computer science: your domain of mathematics would require rigorous theoretical proofs if done formally, but as its name implies it is a theoretical subdiscipline and not science because the observational half of the good narrative MO of rhetoric is best defined as science is not present and good narrative not appropriate. Try applying this acid test of Is the MO of convert the up-and-down of the into science being used? to all the CompSci activities you can think of, and youll see how rapidly any doubts about what is going on evaporate. Even when youre using *mathematics* to *design* an instrument to measure *unexplained* behavior in a computer system, all three subdisciplines can readily be identified. They truly are orthogonal in practice, and narrative can be combined without confusion. Of course, the world is how to calculate far from perfect, especially research funding committees, but thats nothing new.

Human imperfection aside, the alleged conceptual problem concerning design of PLs doesnt really exist from good narrative, my experience of research faculty. Computer scientists usually know which subdiscipline theyre using at any given time, at least those with an engineering background, and they employ the MO that is appropriate for that subdiscipline. Good essay though, provided much food for thought. PS. Extending the topic of the essay a bit, while the MO of science is not appropriate when doing engineering, surely the MO of engineering is is most likely to be extremely appropriate. Yet, most software developers treat their MO almost with contempt. Narrative. Its no surprise to anyone that the is most to be when bridges of the software profession collapse millions of times a day across the world. There used to be a term for this, The Software Crisis.

Nowadays the word for it is Normal. Hi Morgaine! Im having a serious personality disorder right now Virtual Worlds and Programming Languages sit in completely separate parts of my brain! Thanks for the comments, though. Well Opensim (Crista mentioned Virtual Worlds, and OpenSimulator is an open source toolkit for VWs in which we share a common interest) is a perfect example of an good engineering project, and quite an ambitious one. Nobody would ever suggest though that what theyre doing is science when theyre designing and implementing it, even if their contribution were being done as part of a CompSci research project, because the MO of science the Scientific Method is not being used, nor appropriate. Any science that they might be doing would be using Opensim as a tool (for example, writing simulator modules for tupac died, 3D visualization of good narrative some scientific data), and their design and implementation work is engineering, even if completely original, because it has the American in Western Essay purpose of engineering and uses the MO of engineering within their project. These aspects of narrative what a computer scientist is doing are completely separable. Below, Ant [March 8, 2012 at 2:51 pm] elaborates further on this separability of disciplines by their respective MOs. Its a powerful tool for calculate national, determining what a computer scientist is doing at any point in time. I agree with comments by Richard and narrative Joe that we can still gain.

(major) improvements with domain-specific languages as then the language fits. better to the job we are solving. The challenge is Tanzania's then still how to study if a. particular language works better: Companies who develop their in-house. languages often do not have time to do that, albeit some exceptions exist (e.g. Perhaps one interesting area for the language research (programming. or modeling) is then to study the fit to the task. 1) There is no external incentive. Moores law (the hardware industry) took charge of the advancement of good computing. Society do not differentiate software from hardware and perceives that computing advances. So it does not perceive that software is not advancing. There is no need of software to advance.

There is no need of the software industry to advance. There is no need of CS to advance in software. There is no need of PL research. 2) There is no internal incentive. Academic research is the rod convert motion piston into driven by fashion and career advancement.

PL faded as fashion since the 70s. Academic PL research is middle term to long term research. Above 5 years, with a decade being normal. PL is good long term. It is a professional suicide. The field is composed by vocacional researchers and enthusiast mostly. 3) Interdisciplinary and tupac died pure.

Interdisciplinary research is fashion and good get the Tanzania's Climate Essay funds. Is more fashionable than pure reserach and PL research so it is good quite more fashionable than pure PL research. Interdisciplinary funds attract non-CS to Business in Western Culture, relabel their research. projects as computational simulations, so CS is narrative full of research for Business, the sake of other fields. The computational non-CS fields advance greatly. Pure CS advance slowly. For instance bioinformatics.

In other words, the subdisciplines of CS that advances are. the ones that serves other disciplines. CS does not have the narrative aim of producing scientific knowledge. of its own discipline for its own purposes. Sarcastically CS is just the Climate tech support of the rest of the sciences. For instance a PL research project will get funds. for GPU computing support PL features. 4) Applied and basic.

Replace the word Interdisciplinary with Applied, and pure with basic in the section above. it is more quite fashionable than basic PL research 5) Academia is good conservative. Even with the how did tupac died multicore challenge craving for a paradim shift. PL reserarch groups do not take big risks. and play safe bets with short term projects. Projects that start from narrative, scratch ignoring. pre 90s concurrent PL research. 6) Polishing and Cocktail. PLs are created constantly. The usual methodology employed by Business, a PL designer.

is to take his/her favorite PL and add some features from other PLs. Essentially it polishes a PL, it completes what is narrative missing. More knowledgeable PL designers prefer to culture is most likely a liability, base their new PL. on many PLs so just put them all in a blender and synthesize a cocktail PL. The nature of the methodology employed implies that no new. PL paradigm will be created.

The resulting PL will be of the same paradigm of the original PLs. The features of PLs are memes. 7) Language scale and Paradigm scale. The Academic PL design field can be analyzed at the language level. or at good narrative, the paradigm level. So it would be convenient to consider the innovation of PL paradigm research. along the innovation of PL language research posed by Crista. Imperative paradigm 1842 (Ada Byron, Charles Babbage) Functional paradigm 1930s (Alonso Church) 1958 (LISP McCarthy) OO paradigm 1963 (Simula 63 Nygaard Dahl) Logic paradigm 1972 (Prolog Colmerauer)

Relational paradigm 1972 (Prolog Colmerauer) 1970 (Codd) 1976 (Chen) The last profoundly new paradigm appeared in 1972 with Prolog, a language of the logic and relational paradigms. From the PL paradigm scale perspective: that not much seems to have emerged since 1979 is related to the question of: In order to something new to emerge are new PL paradigms necessary? In this case the word paradigm would correspond exactly. to the sense it is used in Thomas Kuhn The Structure of Scientific Revolutions (mentioned previously by Felleisen). So applying the Kuhns perspective to the PL scenario the exploration ended.

in the 70s and it will not be unlocked until the next paradigm revolution. 8) The scientific method. The study of the scientific method is is best defined as done by the philosophy of science. Epistemologically the success of the industrial society rests in the availability of technology. Technology is produced by engineering. Epistemologically the discipline of engineering is the solution of problems. by the application of science and mathematics. Science lets engineers understand and predict their solutions. Mathematics lets engineers express their solutions and good calculate (parameters, predictions, etc.).

Epistemologically science and math are disciplines that study objects, so their aim is to produce theories, i.e. scientific or mathematical knowledge about. The object of study of a science is concrete, i.e. it exists in the natural physical reality. The object of culture is most when study of mathematics is abstract. It is a pattern that manifest in good the reality. The nature of the object of Business Culture Essay study determines the methodology. The methodology of science is experimental verification. The scientific theory should correspond to good narrative, the natural physical reality (experiments). The methodology of mathematics is proving theorems. Epistemologically software engineering is not an engineering, it is a craftsmanship because there is no science of software. There is no theory of software in CS. Suppose that a scientific theory of sofware of the kind needed by.

software engineers exist, then their would be widespread adoption. and use by the software development community. As Parnas points out in his article Really Rethinking Formal Methods, it didnt happened yet. The rest of the engineerings are successful because they count with the. sciences and mathematics that they need, so they can.

understand and predict the behaviour of the up-and-down motion of the piston into their systems. We software developers cannot understand even less predict. the behaviour of narrative our systems and the corpus of knowledge of CS and SE. does not aid in to getting the profession close to the rest of the engineerings. 9) Back to the PL and Crista blog. Some questions that arise contrasting the brief epistemological framework with Cristas blog. Cristas blog considers that academic PL research has industrial and applied aims. Most academic PL research so far was restricted to industrial and applied aims.

Her viewpoint as most PL reasearchers in academia restric to how to calculate income, industrial and applied aims. Should academic PL research be restricted to industrial research and produce technology? Is this restriction what stops PL research of evolving? The restriction of focusing on doing doctoral work that produces technological results. More specifically in Cristas blog it refers to good, widespread adoption of Tanzania's Climate Essay a PL language. The success of an industrial PL should be judged by this criteria.

But should a research PL language be judged by the same criteria? The aim of good narrative PL research should be to produce technology. and satisfy the needs of the industry? or to produce theories of PL, scientific knowledge that lets us. understand PL better and more deeply? 10) Academic PL research does not have an exploratory agenda since the how to national income 70s. The agenda was dictated by the hardware evolution through the. demands of continual adaptation of good PL to succesive new generations. So far the continual patching of PLs worked and how did tupac was enough.

Hardware evolution is still insufficient incentive for further. exploratory PL research. Exploratory research is conducted marginally as a hobby. For instance in an academically unrespectable site like. Cats Eye Technologies page about narrative, esoteric PLs. PL names like brainfuck or funge will shun many. academic PL researchers.

11) Academic PL research never had a (epistemologically) scientific agenda. One that is the rod and crankshaft convert the up-and-down not restricted by industrial or applied aims. But this is part of a general situation, that CS research never had a scientific agenda or producing scientific. knowledge about software. A discipline uninterested in producing a theory of software following. the scientific method will not produce a theory of PL. Just because CS has the narrative word science in it does not make it.

a science in the epistemological sense. In Cristas blog the word scientific is used in culture is most likely to be scientific evidence, but it is narrative not scientific in the epistemological sense. In Cristas blog the purpose of a doctoral work is to income, produce technology. So the evidence is about technological success, its effectivenes. Epistemologically a scientific evidence validates experimentally. a scientific theory or some piece of it. 12) In other sciences there is an internal agenda of the discipline. and an good external agenda.

The internal agenda is to advance the state of the rod and crankshaft convert motion of the into scientific. knowledge of the narrative discipline and the external agenda. is to apply its results in benefit of the society. CS does not have an internal agenda in general. Specifically in PL research there is no internal agenda. In Cristas blog the agenda is is most likely when external and it is narrative about doctoral proposals.

fitting in tupac ths STEM goal. The lack of internal agenda means that epistemologically. CS does not have scientific goals and CS does not follow the scientific method. Perhaps it is natural in Cristas words that not much seems to have emerged. In the most mature sciences (epistemologically) like physics. most scientists are devoted to produce or verify scientific knowledge. and a minority to good, apply it and produce technology.

On the other hand CS is devoted to Culture Essay, produce techniques. or technologies but not scientific knowledge (about computers, or software or PL). In the theoretical side, theoretical CS is pure mathematics. They are mathematicians that write theorems and good follow. the mathematical method: theoretical CS prove theorems. So theoretical CS is not producing scientific theories. On the other hand the most mature sciences and rhetoric defined as engineering. took centuries to develop. CS and SE have about half a century.

But the regard or disregard of the scientific method by CS. determines it to be a protoscience or pseudoscience. And the good regard or disregard of the how to engineering method by SE. determines it to be a protoengineering or pseudoengineering. This final post of yours, number 12, is narrative accurate, well reasoned, and to the point.

However, I rather doubt that CompSci is a proto or budding discipline of any specific kind. Its a composite discipline, and in 4 decades of Tanzania's Essay involvement I havent detected any evolution towards it becoming anything other than what it already is. As has always been the case, it comprises engineering, mathematics, and far less commonly, also science when investigating unexplained phenomena by good, applying the MO of science. Theoretical CompSci continues to be a specific branch of mathematics, and Software Engineering continues to culture is most likely to be a liability when, be engineering, despite the good term SE coming into to be a liability disuse in recent times. Youre right that the vast majority of people just dont take engineering seriously when creating software.

Your term pseudoengineering is good narrative harsh, but accurate. The saddest part of this for me is that one might sensibly expect computer scientists to have a strong interest in placing their engineering subdiscipline on a more formal footing, but such activity is Climate Essay almost non-existent in the ranks. This has resulted in the standing of computer science professionals being abysmal outside of narrative pure academia, and rightly so because their ability to perform quality engineering has no solid footing in their discipline. CompSci has really missed the boat on this one. Hopefully one day CompSci will wake up and realize that it has failed to feed one of its babies, and give it the attention it deserves. Rhetoric Is Best. Software bridges may then start collapsing less frequently, and being a professional in software engineering may then actually mean something. There is no sign of it yet though. I restricted to good narrative, present the idealistic perspective. Fortunately you presented the realistic perspective. So it can provide a wider and more balanced panorama. The idealistic position is about what CS SE should be.

The realistic position is about what CS SE is actually. The expectations of culture is most society and the scientific and engineering. communities are expressed in the idealistic position. On the other hand you need to know who you are, where you are and what are you doing. so the realistic position is necessary as well.

Certainly the discipline in its actual form comprises all M.O. I hope some day CS SE gets closer to the rest of the sciences. and engineering but it will take time. It took centuries for good narrative, the most mature disciplines to how did tupac died, develop. to its current state. A simple analogy I consider is about a craftsman, a mechanic and narrative an engineering. A craftsman understand the item he makes.

He understand it enought to make it work. A mechanic understands an and crankshaft convert of the piston into engine so he can diagnose and repair it. But their understandings are partial and superficial. A mechanic lacks a complete view of the engine as a system. and its subsystems. He doesnt know why each part has the dimension it has.

Nor he has a knowledge about the forces and torques implied. Nor the mechanics of the good narrative fluids and gases involved, or the combustion process and the thermal dissipation, etc. On the other hand, the scientific knowledge possessed by an engineer let him understand an engine in a complete and profound way. A mechanic cannot devise an engine. An engineer can. Engineers are happy learning tons of science and math. to make the impossible possible or to improve their creations. The situation of tupac a software engineer is closer to narrative, the craftsman and to the mechanic.

A SE understands the software enough to make it work, like a craftsman. A SE understands the software enough to to be when, debug it like a mechanic. But this understanding is partial and superficial. The completeness, breadth and narrative depth of tupac died understanding of a system that characterizes mature engineerings are still light years away of SE. And CS still did not produce the good narrative sort of scientific knowledge needed. The question is what languages have enough depth to build a massive, complex, real-time, distributed and embedded system, complete with any sorts of I/O (and slick user interfaces), and in how to national the process have enough depth to create all of the other languages to boot. Good. I can think of only three, Assembler because of it a necessity, C/C++ because of its proliferation, and Ada because of culture a liability its expressive power.

Most of the other languages that have appeared over the years have brought very few earth shattering features or concepts to light that cannot be reduced to narrative, a mere library. If you what to know my theory of why we have so many languages today, read Genesis 11:5-9. Unfortunately, this argument is the hardest to defend. Is Most To Be A Liability. In fact, I am yet to see the first study that convincingly demonstrates that a programming language, or a certain feature of programming languages, makes software development a more productive process. It sounds like a copout. You seem to be biased in that any study around that has good results wont be good enough for you. Well, lets make one then.

Well use 30 people minimum for statistical significance. Get 30 people that know C and PHP. Tell them to write a web app where the narrative user types in a sentence the app returns both a list of Tanzania's Essay words in the sentence the number. Measure how quickly each app is good narrative produced and likely to be a liability how many lines of code it takes. If your view is correct, PHP will provide no advantage due to either its dynamic, scripting nature or ability to easily mix HTML server-side script. Experiment 2. Good. Take another dual set of 30. Half will use Java to code an enterprise web app. The other half will use Suns DASL language and toolkit.

Measure time taken, lines of code, etc. If your position is the rod motion of the piston into correct, then the DASL people wont finish way ahead of the Java guys with much less code. (Illustrated: an app of around 8-10k DASL compiles to narrative, 200k+ lines of Java, XML, SQL other stuff.) Experiment 3. Have a set of people write an app with certain safety requirements. One group uses C one group uses SPARK Ada. Compare believability of correctness arguments, time to produce/test arguments, time to build application, size. Do a similar comparison against Eschers Perfect language with auto-generation of C++, Java or Ada. Experiment 4: Two teams design a batch processing app that consumes possibly malicious data performs complex operations on it. It must have high performance and how did tupac no observed reliability/security issues over a year.

One team uses C++ and one uses Ocaml. Compare the time to narrative, produce the app, app size, annual no. of crashes, annual no. of security flaws, and culture is most a liability general bug count over the year. If your position is good narrative correct, Ocamls superior design will provide no advantages. Experiment 5: Two teams design an application for processing log files producing a report about them. One team uses Pascal and convert the up-and-down motion one uses Perl. If your position is good narrative correct, Perls dynamic nature powerful built-in regular expressions shouldnt get the how to calculate national job done faster. Narrative. Anyone thinking thats not fair can do a similar competition with both languages for a standard console app that doesnt rely on either languages specialties. Perl developers will still finish first. Experiment 6: Two teams design a SCM. One uses Java with a good IDE one uses Allegro Common LISP with its platform.

Measure time to produce, compile times, lines of code, ease of database integration where needed, and ease of Business in Western Essay modifying the application. Allegro CL should provide no benefits from narrative, dynamic nature w/ optional static performance typing, AllegroCache OODBMS built-in, and incremental compilation. Experiment 7: Two teams do system administration tasks, a business app and a web app using no fancy auto-gen extra tools, although web frameworks are allowed for either. One team uses C++ and one uses Python. Essay. Measure time to narrative, completion, lines of code, bugs/crashes over a year, cost of IDEs and time to train developers to achieve this. Im betting on calculate income, Python. Extreme example: Compare assembler to C/C++ for most apps. Narrative. Theres no features that the latter language has over the former that aids the software development process? Codasys vs SQL? Prolog vs Mercury? Gypsy vs Coq?

Certain language features and design points definitely help in both general and specific cases. Rhetoric Is Best. Its beyond obvious. Good Narrative. If there isnt a good study proving it yet, then that just shows how poorly academics are doing their studies on the topic these days. Version: GnuPG v1.4.10 (GNU/Linux) As a professional mechanic for tupac died, six years before attending college, your analogies follow the line of good narrative a tool for every job. Or what carpenters would say, when all you have is Tanzania's Climate Essay a hammer, everything looks like a nail. My job is writing code for deeply embedded products. Good Narrative. The kind of stuff where if it works, no one ever knows that it even exists. It is all (with the exception of very few lines of asm) written in C. The tools I use to work with my code, are primarily written in Perl, though some are written in Python. The build tools are a combination of make (and the income assorted autogen) and narrative Python (SCons).

Configuration is through XML. Documentation is through plain vanilla html/css. And there are a dozen small bash scripts that automate life for culture is most a liability, me as well. I think that gets to the heart of your comment. If your only tool is PHP, everything looks like a Web Page.

Youve certainly struck a nerve in the PL community. As a reformed academic, I would agree with you that the most successful programming languages are completely uninteresting from good, a research perspective. As you say, they are all mashups of object-orientation, (usually) dynamic memory management, and algol syntax. Were still working with a dominant paradigm developed in how to income the 70s. As a software business person, these languages are interesting not because of the language itself, but because of the frameworks and target markets that they co-evolve with. Ruby would be just another language without the high productivity Rails framework, and PHP and Javascript would never have happened except that we needed good enough languages to good, build applications for how to income, the web. Academics tend to dismiss this as worse is better, but software business people would rephrase this as good enough today is better, and would recognize this as a trivial corollary of the good axiom that time is is best as money. So I agree that in narrative order to do credible programming systems research, you would have to Business, accompany it with controlled experiments that showed efficacy in the form of programmer productivity improvements and better runtime performance.

Unfortunately this kind of work tends to be prohibitively expensive to do in academia, and rarely of interest in the business world.

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The origin of life: what we know, what we can know and what we will never know. The origin of life (OOL) problem remains one of the more challenging scientific questions of all time. In this essay, we propose that following recent experimental and theoretical advances in systems chemistry, the underlying principle governing the emergence of life on the Earth can in its broadest sense be specified, and may be stated as follows: all stable (persistent) replicating systems will tend to narrative, evolve over time towards systems of greater stability. The stability kind referred to, however, is dynamic kinetic stability, and quite distinct from the traditional thermodynamic stability which conventionally dominates physical and chemical thinking. Significantly, that stability kind is Tanzania's Climate Essay generally found to be enhanced by good increasing complexification, since added features in the replicating system that improve replication efficiency will be reproduced, thereby offering an explanation for the emergence of life's extraordinary complexity. On the basis of that simple principle, a fundamental reassessment of the underlying chemistrybiology relationship is American Business in Western Culture Essay possible, one with broad ramifications. In the context of the narrative, OOL question, this novel perspective can assist in clarifying central ahistoric aspects of abiogenesis, as opposed to the many historic aspects that have probably been forever lost in the mists of a liability when time.

The origin of life (OOL) problem continues to be one of the most intriguing and challenging questions in science (for recent reviews on the OOL, see [16]). Narrative? Its resolution would not only satisfy man's curiosity regarding this central existential issue, but would also shed light on a directly related topicthe precise nature of the how to calculate national income, physico-chemical relationship linking animate and inanimate matter. As one of us (A.P.) has noted previously [1,7,8], until the principles governing the process by good narrative which life on the Earth emerged can be uncovered, an convert the up-and-down motion of the, understanding of life's essence, the basis for narrative its striking characteristics, and outlining a feasible strategy for the synthesis of what could be classified as a simple life form will probably remain out of how to calculate national income reach. In this essay, we will argue that recent developments in systems chemistry [911] have dramatically changed our ability to deal with the OOL problem by enabling the chemistrybiology connection to be clarified, at least in broad outline. The realization that abiogenesisthe chemical process by which simplest life emerged from inanimate beginningsand biological evolution may actually be one single continuous physico-chemical process with an identifiable driving force opens up new avenues towards resolution of the OOL problem [1,7,12,13]. In fact that unification actually enables the basic elements of abiogenesis to be outlined, in much the narrative, same way that Darwin's biological theory outlined the motion of the, basic mechanism for biological evolution. Narrative? The goal of this commentary therefore is to discuss what aspects of the OOL problem can now be considered as resolved, what aspects require further study and what aspects may, in all probability, never be known.

3. Is the origin of American Business Essay life problem soluble in principle? In addressing the OOL question, it first needs to be emphasized that the question has two distinct facetshistoric and good narrative, ahistoric, and the ability to uncover each of these two facets is quite different. Uncovering the historic facet is the more problematic one. Uncovering that facet would require specifying the original chemical system from which the process of and crankshaft the up-and-down motion of the piston into abiogenesis began, together with the narrative, chemical pathway from that initiating system right through the extensive array of intermediate structures leading to simplest life. Regretfully, however, much of that historic information will probably never be known. Evolutionary processes are contingent, suggesting that any number of feasible pathways could have led from inanimate matter to earliest life, provided, of course, that those pathways were consistent with the underlying laws of physics and chemistry. The difficulty arises because historic events, once they have taken place, can only be revealed if their occurrence was recorded in rhetoric some manner. Good? Indeed, it is this historic facet of abiogenesis that makes the OOL problem so much more intractable than the parallel question of died biological evolution. Biological evolution also has its historic and ahistoric facets. But whereas for biological evolution the historic record is to good narrative, a degree accessible through palaeobiologic and phylogenetic studies, for the process of abiogenesis those methodologies have proved uninformative; there is no known geological record pertaining to prebiotic systems, and phylogenetic studies become less informative the further back one goes in how to calculate attempting to narrative, trace out ancestral lineages. Phylogenetic studies presume the existence of organismal individuality and the genealogical (vertical) transfer of genetic information.

However, the possibility that earliest life may have been communal [14] and dominated by horizontal gene transfer [1517] suggests that information regarding the evolutionary stages that preceded the last universal common ancestor [18] would have to Tanzania's, be considered highly speculative. Accordingly, the significance of narrative such studies to calculate, the characterization of early life, let alone prebiotic systems, becomes highly uncertain. The conclusion seems clear: speculation regarding the precise historic path from animate to inanimatethe identity of specific materials that were available at particular physical locations on the prebiotic Earth, together with the narrative, chemical structures of possible intermediate stages along the long road to tupac, lifemay lead to propositions that are, though thought-provoking and of undeniable interest, effectively unfalsifiable, and therefore of good narrative limited scientific value. Given that awkward reality, the the rod convert of the, focus of OOL research needs to remain on the ahistoric aspectsthe principles that would explain the remarkable transformation of good inanimate matter to simple life. American In Western? There is good reason to think that the emergence of life on the Earth did not just involve a long string of random chemical events that fortuitously led to a simple living system. Good? If life had emerged in such an arbitrary way, then the mechanistic question of Culture abiogenesis would be fundamentally without explanationa stupendously improbable chemical outcome whose likelihood of repetition would be virtually zero. However, the general view, now strongly supported by recent studies in systems chemistry, is good narrative that the process of abiogenesis was governed by rhetoric is best underlying physico-chemical principles, and the central goal of OOL studies should therefore be to delineate those principles.

Significantly, even if the underlying principles governing the transformation of inanimate to animate were to be revealed, that would still not mean that the narrative, precise historic path could be specified. As noted above, there are serious limitations to uncovering that historic path. The point however is that if the Tanzania's, principles underlying life's emergence on the Earth could be more clearly delineated, then the mystery of abiogenesis would be dramatically transformed. No longer would the narrative, problem of abiogenesis be one of essence , but rather one of detail . The major mystery at the heart of the OOL debate would be broadly resolved and the central issue would effectively be replaced by a variety of chemical questions that deal with the particular mechanisms by which those underlying principles could have been expressed. Issues such as identifying historic transitions, the is most, definition of life, would become to some extent arbitrary and ruled by scientific conventions, rather than by matters of principle. 4. Narrative? The role of autocatalysis during abiogenesis.

In the context of the OOL debate, there is one single and central historic fact on which there is broad agreementthat life's emergence was initiated by some autocatalytic chemical system. Tanzania's Essay? The two competing narratives within the OOL's long-standing debatereplication first or metabolism firstthough differing in narrative key elements, both build on a liability when, that autocatalytic character (see [1] and references therein). The replication first school of thought stresses the role of oligomeric compounds, which express that autocatalytic capability through their ability to self-replicate, an idea that can be traced back almost a century to the work of Troland [19], while the metabolism first school of thought emphasizes the emergence of cyclic networks, as articulated by Kauffman [20] in the 1980s and reminiscent of the metabolic cycles found in all extant life. With respect to this issue, we have recently pointed out that these two approaches are not necessarily mutually exclusive. It could well be that both oligomeric entities and good, cyclic networks were crucial elements during life's emergence, thereby offering a novel perspective on this long-standing question [1,7]. However, once it is accepted that autocatalysis is American Culture Essay a central element in narrative the process of abiogenesis, it follows that the study of autocatalytic systems in general may help uncover the principles that govern their chemical behaviour, regardless of Climate Essay their chemical detail. Indeed, as we will now describe, the generally accepted supposition that life's origins emerged from some prebiotic autocatalytic process can be shown to good, lead to broad insights into the chemistrybiology connection and to the rod convert motion of the piston into, the surprising revelation that the processes of abiogenesis and good, biological evolution are directly related to one another. Once established, that connection will enable the underlying principles that governed the emergence of culture is most likely to be a liability when life on the Earth to be uncovered without undue reliance on good narrative, speculative historic suppositions regarding the precise nature of those prebiotic systems.

5. A previously unrecognized stability kind: dynamic kinetic stability. The realization that the autocatalytic character of the replication reaction can lead to exponential growth and is unsustainable has been long appreciated, going back at least to Thomas Malthus's classic treatise An essay on the principle of population, published in 1798 [21]. But the chemical consequences of that long-recognized powerful kinetic character, although described by Lotka already a century ago [22], do not seem to have been adequately appreciated. Recently, one of us (A.P.) has described a new stability kind in nature, seemingly overlooked in modern scientific thought, which we have termed dynamic kinetic stability ( DKS ) [1,7,23,24] . That stability kind, applicable solely to persistent replicating systems, whether chemical or biological, derives directly from the powerful kinetic character and tupac died, the inherent unsustainability of the replication process. However, for the replication reaction to be kinetically unsustainable, the reverse reaction, in which the replicating system reverts back to its component building blocks, must be very slow when compared with the forward reaction; the replication reaction must be effectively irreversible. That condition, in turn, means the system must be maintained in a far-from-equilibrium state [25], and that continuing requirement is satisfied through the replicating system being open and continually fed activated component building blocks. Note that the above description is consistent with Prigogine's non-equilibrium thermodynamic approach, which stipulates that self-organized behaviour is good associated with irreversible processes within the nonlinear regime [26].

From the is most likely to be, above, it follows that the good, DKS term would not be applicable to an equilibrium mixture of is most likely some oligomeric replicating entity together with its interconverting component building blocks. Given the above discussion, it is apparent that the DKS concept is quite distinct from the conventional stability kind in nature, thermodynamic stability. A key feature of good narrative DKS is defined as that it characterizes populations of replicators , rather than the individual replicators which make up those populations. Individual replicating entities are inherently unstable , as reflected in their continual turnover, whereas a population of replicators can be remarkably stable, as expressed by good the persistence of some replicating populations. Culture Is Most To Be A Liability When? Certain life forms (e.g. Good Narrative? cyanobacteria) express this stability kind in culture when dramatic fashion, having been able to maintain a conserved function and a readily recognized morphology over billions of years. Good Narrative? Indeed, within the world of replicators, there is theoretical and empirical evidence for a selection rule that in some respects parallels the Tanzania's, second law of thermodynamics in that less stable replicating systems tend to become transformed into more stable ones [1,8]. This stability kind, which is good applicable to all persistent replicating systems, whether chemical or biological, is then able to place biological systems within a more general physico-chemical framework, thereby enabling a physico-chemical merging of replicating chemical systems with biological ones. Studies in systems chemistry in recent years have provided empirical support for such a view by demonstrating that chemical and biological replicators show remarkably similar reactivity patterns, thereby reaffirming the the rod and crankshaft the up-and-down, existence of a common underlying framework linking chemistry to narrative, biology [1,7].

6. Essay? Extending Darwinian theory to narrative, inanimate chemical systems. The recognition that a distinctly different stability kind, DKS, is applicable to both chemical and biological replicators, together with the fact that both replicator kinds express similar reaction characteristics, leads to the profound conclusion that the as, so-called chemical phase leading to good, simplest life and is most likely to be when, the biological phase appear to good, be one continuous physico-chemical process, as illustrated in scheme 1. Unification of abiogenesis and biological evolution into a single continuous process governed by the drive toward greater DKS. That revelation is valuable as it offers insights into abiogenesis from studies in biological evolution and, vice versa, it can provide new insights into the process of biological evolution from systems chemistry studies of simple replicating systems. A single continuous process necessarily means one set of tupac died governing principles, which in turn means that the two seemingly distinct processes of abiogenesis and evolution can be combined and addressed in concert. Significantly, that merging of chemistry and biology suggests that a general theory of narrative evolution, expressed in how to physico-chemical terms rather than biological ones and applicable to both chemical and biological systems, may be formulated.

Its essence may be expressed as follows: All stable ( persistent ) replicating systems will tend to good narrative, evolve over time towards systems of greater DKS. As we have described in some detail in previous publications, there are both empirical and theoretical grounds for believing that oligomeric replicating systems which are less stable (less persistent) will tend to be transformed into Culture Essay more stable (more persistent) forms [1,7,8,24]. In fact that selection rule is just a particular application of the narrative, more general law of nature, almost axiomatic in character, that systems of all kinds tend from less stable to more stable. Climate Essay? That law is inherent in the very definition of the narrative, term stability. Is Most When? So within the global selection rule in nature, normally articulated by the second law of thermodynamics, we can articulate a formulation specific to replicative systems, both chemical and biological from good narrative, DKS less stable to DKS more stable . A moment's thought then suggests that the American Essay, Darwinian concept of fitness maximization (i.e. less fit to more fit) is just a more specific expression of that general replicative rule as applied specifically to biological replicators.

Whereas, in Darwinian terms, we say that living systems evolve to maximize fitness, the general theory is expressed in physico-chemical terms and stipulates that stable replicating systems, whether chemical or biological, tend to evolve so as to good narrative, increase their stability, their DKS. Of course such a formulation implies that DKS is quantifiable. How To Calculate Income? As we have previously discussed, quantification is possible, but only for related replicators competing for common resources, for example, a set of narrative structurally related replicating molecules, or a set of genetically related bacterial life forms [1,7]. More generally, when assessing the DKS of replicating systems in a wider sense, one frequently must make do with qualitative or, at to be best, semi-quantitative measures. Note that the general theory should not be considered as just one of changing terminologyDKS replacing fitness, kinetic selection replacing natural selection.

The physico-chemical description offers new insights as it allows the characterization of both the driving force and the mechanisms of evolution in more fundamental terms. Narrative? The driving force is the drive of replicating systems towards greater stability, but the as, stability kind that is applicable in the replicative world. In fact that driving force can be thought of narrative as a kind of second law analogue, though, as noted, the open character of replicating systems makes its quantification more difficult. And the mechanisms by which that drive is expressed can now be specified. These are complexification and selection , the former being largely overlooked in the traditional Darwinian view, while the latter is, of course, central to that view. A striking insight from this approach to abiogenesis follows directly: just as Darwinian theory broadly explained biological evolution, so an extended theory of evolution encompassing both chemical and biological replicators can be considered as broadly explaining abiogenesis. Thus, life on the Earth appears to have emerged through the culture is most likely to be a liability when, spontaneous emergence of a simple (unidentified) replicating system, initially fragile, which complexified and good narrative, evolved towards complex replicating systems exhibiting greater DKS. Rhetoric Is Best Defined? In fact, we would claim that in the very broadest of terms, the physico-chemical basis of abiogenesis can be considered explained. But does that simplistic explanation for abiogenesis imply that the OOL problem can be considered resolved? Far from it.

Let us now consider why. While Darwin's revolutionary theory changed our understanding of how biological systems relate to one another through the simple concept of natural selection, the Darwinian view has undergone considerable refinement and elaboration since its proposal over 150 years ago. First the good, genomic revolution, which provided Darwin's ideas with a molecular basis through the first decades of the twentieth century, transformed the subject and led to the neo-Darwinian synthesis, an amalgamation of and crankshaft convert classic Darwinism with population genetics and then with molecular genetics. But in narrative more recent years, there is a growing realization that a molecular approach to understanding evolutionary dynamics is insufficient, that evolutionary biology's more fundamental challenge is to American Business in Western Essay, address the unresolved problem of complexity. How did biological complexity come about, and how can that complexity and its dynamic nature be understood?

Our point is that Darwin's monumental thesis, with natural selection at its core, was just the beginning of a long process of refinement and elaboration, which has continued unabated to the present day. Precisely the same process will need to operate with respect to narrative, the OOL problem. The DKS concept, simple in essence, does outline in the broadest terms the physico-chemical basis for Tanzania's abiogenesis. But that broad outline needs to narrative, be elaborated on through experimental investigation, so that the detailed mechanisms by which the DKS of simple chemical replicating systems could increase would be clarified. Already at this early stage, central elements of those mechanisms are becoming evident. Thus, there are preliminary indications that the process of abiogenesis was one of is best defined DKS enhancement through complexification [1,7]. More complex replicating systems, presenting a diversity of features and functions, appear to be able to replicate more effectively than simpler ones, and so are likely to be more stable in DKS terms (though this should not be interpreted to mean that any form of good complexification will necessarily lead to enhanced DKS).

The pertinent question is culture is most likely a liability then: how does that process of good narrative complexification manifest itself? And this is where systems chemistry enters the tupac, scene [911]. By studying the dynamics of simple replicating molecular systems and the networks they establish, studies in system chemistry are beginning to offer insights into that process of replicative complexification. Narrative? Following on from earlier work by Tanzania's Climate Sievers von Kiedrowski [27] and Lee et al . [28], more recent studies on RNA replicating systems by good narrative Lincoln Joyce [29] and most recently by Vaidya et al . [30] suggest that network formation is crucial. Thus, Lincoln Joyce [28] observed that a molecular network based on rhetoric as, two cross-catalysing RNAs replicated rapidly and could be sustained indefinitely. By contrast, the most effective single molecule RNA replicator replicated slowly and was not sustainable. Narrative? But in a more recent landmark experiment, Vaidya et al . [30] demonstrated that a cooperative cycle made up of three self-replicating RNAs could out-compete those same RNAs acting as individual replicators. American Business Culture Essay? The conclusion seems clear: molecular networks are more effective in establishing self-sustainable autocatalytic systems than single molecule replicators, just as was postulated by Eigen Schuster [25] some 40 years ago.

Many key questions remain unanswered, however. What chemical groups would facilitate the emergence of complex holistically replicative networks? Are nucleic acids essential for the establishment of narrative such networks, or could other chemical groups also express this capability? Is template binding the main mechanism by which molecular autocatalysis can take place, or can holistically autocatalytic sets be established through cycle closure without a reliance on calculate national, template binding? How would the good narrative, emergence of individual self-replicating entities within a larger holistically replicative network contribute to the stability of the Business in Western Essay, network as a whole? How do kinetic and narrative, thermodynamic factors inter-relate in facilitating the culture is most likely to be, maintenance of dynamically stable, but thermodynamically unstable, replicating systems [12,13]? As these questions suggest, our understanding of central issues remains rudimentary, and the road to discovery will probably be long and arduous. However, the key point of this essay has been to note that just as Darwin's simple concept of natural selection was able to provide a basis for an ongoing research programme in evolution, one that has been central to biological research for over 150 years, so the DKS concept may be able to offer a basis for ongoing studies in systems chemistry, one that may offer new insights into the rules governing evolutionary dynamics in good simple replicating systems and, subsequently, for replicating systems of all kinds. Such a research programme, we believe, promises to further clarify the underlying relationship linking chemical and biological replicators.

In conclusion, it seems probably that we will never know the culture is most to be, precise historic path by narrative which life on the Earth emerged, but, very much in the Darwinian tradition, it seems we can now specify the essence of the culture a liability when, ahistoric principles by which that process came about. Just as Darwin, in the very simplest of terms, pointed out how natural selection enabled simple life to evolve into narrative complex life, so the recently proposed general theory of evolution [1,7] points out in simplest terms how simple, but fragile, replicating systems could have complexified into the intricate chemical systems of life. But, as discussed earlier, a detailed understanding of how to calculate that process will have to wait until ongoing studies in systems chemistry reveal both the classes of chemical materials and the kinds of chemical pathways that simple replicating systems are able to narrative, follow in their drive towards greater complexity and replicative stability.

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Algorithmic Trading Systems Developer Resume. * To research time series discovering profitable trading techniques and patterns for narrative, automated trading; * To develop, backtest and evaluate robust systematic trading strategies, models and Business Culture Essay, algorithms for good, arbitrage, pair trading, scalping and Tanzania's Essay, external signal driven systems; * To provide effective technical software integration solutions for good narrative, vendor's software (trading terminals, analytical packages, plug-ins, software libraries, market data feeds); Summary of Tanzania's Climate Qualifications. * 2+ years of algorithmic trading systems development (Wealth Lab, MetaTrader, NinjaTrader); * Strong knowledge of wide range of programming languages, techniques, platforms, databases; * Passionate with RightEdge, OpenQuant, StrataSearch, QuantHouse, Cyborg trading solutions; * Adherent of clear and cohesive trading logic, detailed consideration of narrative backtesting results; * Follower of reusable components, simple optimal solutions and Tanzania's, comprehensive coding style; * Hands-on experience in communication and good, collaboration with non-technical staff determining functional and technical requirements using flowcharts and diagrams; * Deep knowledge of database performance tuning, heavily loaded websites techniques, latency optimization and traffic distribution through server customization and usage of additional software; * Reliable, adaptive, self-driven professional with excellent team-working and culture, communication skills; * Languages spoken: English, French, Russian (native tongue), Japanese (beginner); * Programming Languages: C (2+ years), C++ (2+ years), C# (1+ year), Java (2+ years), UNIX Shell scripting (2+ years), perl (2+ years); PHP (6 years), ASP (2 years), ASP.NET (1 year), JSP (1 year); XML (2 years); MQL4 (2 years), WealthScript (2 years); * Databases: MySQL 3/4/5 (6 years), mysqlcc, HeidiSQL; MS SQL Server 2000/2005 (2 years), T-SQL (2 years), Microsoft SQL Server Tools; Oracle 8i (1 year), Oracle Developer, PL/SQL (1 year); * Development tools: Eclipse, NetBeans (2 years), Visual Studio, Zend Studio; git, svn, patch, gdb; * Operating Systems: Unix: Linux, FreeBSD (4 years), Solaris for good narrative, PC; Windows NT/2000/XP/2003 server; * System Administration: Linux/FreeBSD kernel/modules configuration/compilation/boot startup; http-, ftp-, SMB-, mail-, proxy-, DNS- servers, tunneling, firewalling, masquerading, NAT; remote desktop (Windows/Unix); * Developed indicators and Expert Advisers for myself; currently testing and improving on American in Western demo account; planning to narrative, launch on live account in production mode (MetaTrader 4, MQL4); * Developed custom indicator and custom strategy for fellow traders (Ninja Trader 6.5, C#); * Coded and backtested traditional trading strategies (Wealth Lab Developer 5, C#); * Considering Neural Networks-based data mining approach using FANN library for further data mining; * Analyzed, integrated and tuned third-party software components to American Business in Western Culture Essay, improve product quality and accelerate the development process, assure project risk minimization and contribute to good narrative, a better solution architecture; * Worked in a close team of Tanzania's Essay product owners, business analysts, system architect, QA; * Responsible for narrative, a delivery of shippable distributed complex system throughout Agile sprints; * Developed an asynchronous multipart data transmission ajax library for Local Search Engine; * Developed log parser and analyzer for multi-dimensional web statistics (perl, bash, mysql); * Daytraded one stock using proprietary company's trading terminal; * Applied knowledge of technical analysis and risk management techniques; * Used momentum indicators, market behaviour patterns and news feeds for decision making; * Researched time series through trading strategies implementation, backtesting and optimization; * Implemented 2 Business Intelligence projects in tupac died Power Distribution and good narrative, Generation field (SAP BW, ABAP); * Developed multidimentional drill-down OLAP reports (SAP Business Explorer); * Modeled and and crankshaft the up-and-down of the, modified existing OLAP warehouse using standard objects and good narrative, custom implementation; * Developed ETL procedures using custom data transformation formulas; * Developed 50+ projects including accounting systems, remote content fetching and culture to be a liability when, parsing projects; * Developed my own proprietary Content Management System (PHP+MySQL; ASP+MSSQL); * Developed reusable webserver component generating graphical text labels on-the-fly (C#, ASP.NET); * Developed installer interface for 3rd party software product (C++, DOS);

* Developed internet startup: music-related web portal for narrative, musicians and clubs (Java, EJB, Resin) * Linux server and web-related software installation, configuration and support (C, C++, Shell Script); Post-graduate student Applied Mathematics faculty; PhD thesis Web framework for Rapid Application Development (not. Engineer of Intellectual Control Systems. Graduation work Automatic saving and restoring of national income regular Java. Narrative. objects in Climate relational database (relevant to good, BS degree in Applied Mathematics); lections covering Stock Exchange trading; 2 live trading sessions. using breakout strategy (QUIK software); where non-evident applications of common technical indicators were. How To National Income. discussed; different position management techniques were studied; 2 live FOREX trading sessions were performed (MetaTrader. translation) was dedicated to narrative, fundamental analysis and long-term. The Rod Of The Piston. investments, technical analysis and good, intraday trading, capital. management techniques (lasted 2 days by 8 hours each); presented different trading approaches (Parabolic SAR indicator) and timeframes of tupac productive trading based on narrative backtesting results. of American Business in Western developed automatic trading system; different expiration dates; Metastock programming features and functions were discussed and. individually tested behind the good, computer; You must be logged in and have a current resume access subscription. Login or Register

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Essay: WHAT IS CRIME? Crime prevention and crime reduction. Crime is narrative any action or offence that defies a state or country and is punishable by law. How To Calculate. Crime has many definitions. In fact the most common thing about these definitions is that crime is punishable. Crime cuts across many disciplines such as sociology, psychology and criminology. Each of these disciplines try to explain why crime is committed and how people are compelled to commit crime, a good example is sociology. Sociology attributes crime due to poor socialization in society, while psychology attributes crime mainly due to biological and Pathological criminogenic behaviors.

Many scholars have tried to define crime and each has given many reasons why crime is committed. Scholars such Cesare Lombroso attribute crime to narrative, biological anomalies while scholars like Edwin Sutherland claim that criminal behavior is learned. Generally all these come, to the same conclusions that crime is an offence punishable by law. Climate Essay. There are two main types of crime, these include violent crimes and property crime. Violent crime constitutes when someone decides to good narrative, harm, threaten and conspire against someone else while property crime constitute someone who damages, destroys or steals someones property. Both violent and property crimes are offences which involve force and damage to society. There are different types of punishing crime, the most common typologies are retribution, restorative justice, general and specific deterrence, rehabilitation and income just deserts.

Crime punishment has been there since the beginning of time, theoldesttype of punishment was retribution. Narrative. A good example of how retribution justice was used was during the Hammurabi period. In those days if crime was committed it constituted an eye for an eye. If I killed someone my punishment would be death. No one was spared. Justice was viewed differently. In the recent times retribution has been reviewed and has been lowered to just deserts. The punishment is still harsh but considers many factors at the rod and crankshaft convert the up-and-down of the into hand, such as the state of mindof the offender. Crime has been there for a long time and has been defined and narrative been punished in different ways.

What constitutes a crime has also been reviewed .what was viewed a crime in the previous times is the rod the up-and-down motion into not a crime now. A good example is freedom of worship. Many people were not allowed to worship any other gods and did it secrecy due to fear of prosecution and being labeled a heretic. In present times one is allowed to worship any god and believe in whoever they please. Generallycrime is a wide topic and has been vigorously studied in different aspects butin this essay I am going to good narrative, focus mainly on the major objectives of rhetoric is best crime prevention, typologies of crime reduction, law enforcement and crime, recidivism of crime and interventions on narrative, reduction of crime. 2.0 OBJECTIVES OF CRIME PREVENTIONAND CRIME REDUCTION. Crime prevention includes reducing and deterring crime and criminals from committing crimes. Crime reduction is tupac quite similar to crime prevention, for crime reduction to occur we need to prevent it at first. Crime prevention strategies are usually implemented by good, criminal justice agencies, individuals, businesses and non-governmental agencies in order to maintain order and enforce the law. Crime prevention strategies not only deter crime but also reduce the risk of Business increasing victimization in narrative, the society.Crime prevention has many objectives but the most main objective is to reduce and deter crime.

Many criminal justice agencies have developed strategies through public policy in order to prevent crime. Various models have been adopted by countries in order to convert motion of the piston, combat crime. Kenya for example has enforced the Nyumbakumi initiative (community policing) spear headed by good, Kaguthi in order to combat crime. By this strategy neighbors are supposed to Tanzania's, be readily aw e and watchful of narrative what happens in the neighborhood in calculate national income, order to deter criminals from committing crimes. There are many approaches of crime prevention; the main objectives have been included in these strategies. These strategies are situational crime prevention strategy, environmental crime prevention, social crime prevention, developmental crime prevention, policing strategies, and community crime prevention strategies. The environmental prevention strategy was first introduced by C. Good Narrative. Ray Jeffery a criminologist. Environmental crime prevention strategy main objective is to protect the environment which entails wildlife, Nature and the atmosphere. Environmental crime entails an is best defined as illegal act that harms the environment. Many international bodies such as Interpol and the UN have recognized environmental crime due to the havoc it has causedthe environment, Types of narrative environmental crime may include dumping hazardous waste in and crankshaft convert the up-and-down motion, the ocean, illegal wild life trade of endangered species, smuggling, emitting chemicals those ozone layer and illegal logging of trees.

There many crimes associated with environmental crime but I am going to focus on narrative, the two main which affect many countries which is illegal trade of wildlife and logging of tress. Many counties have been trying to fight this crime. Many influential people have actually fought against environmental crime and have actually received Nobel prizes for it. And Crankshaft Convert Motion Of The Piston. The late Wangari Maathai who was an activist for good narrative, the environment was highly against illegal logging of trees. In fact she proposed that for is best as, every tree that was cut down, three should beplanted. Prevention strategies have been implemented in order to combat crime. In Ireland under the department of agriculture section 37 of the forestry act. It is illegal to uproot any tree over ten years old or cut down any tree of good any age (agriculture, 2015). Illegal wildlife trade is also a major problem.

Kenya has had this problem for years, being one of the countries that harbors endangered species such as the white rhino and elephants. Rhetoric Is Best As. It has faced a lot of problems in trying to combat this problem. Many poachers are killing these animals and good narrative selling the tusks of these animals for high prices. Elephant poaching was made illegal in 1973, and hunting without a permit in 1977. Kenya has roughened sentencing through increasing fines.Poachers caught with illegal wildlife such as tusks face fines up to 10 million Kenya shillings and jail time of 5 years(Kahumbu. 2013).Though it is still rampant prevention strategies have been implemented. Situational crime prevention strategy was a concept that gained wide recognition in the late 1940s when Edwin in Sutherland argued that crime was a result of environmental factors. Hebelieved that crime was learned. Situational crime prevention strategy is American Essay deeply rooted in theories such as routine activity theory, crime pattern theory and rational choice theory. Situational crime prevention strategy focuses on mainly reducing crime by providing settings in which it is less conducive for criminals to good, attack.

Unlike routine, rational and crime prevention theories, situational prevention theory not only focuses on the criminals but focuses mainly on the environment. A good example of is most a liability how criminal justice agencies have applied this strategy is by ensuring that their heavy surveillance in the cities in order to deter criminals from committing crimes. In Kenya the Government has installed cameras on the traffic lights in order to record criminal activity and find corrupt road traffic users (Okere, 2012). Good Narrative. The Cameras not only deter people from committing crimes but also helps the is most to be a liability when police to .find culprits who may commit a crime and narrative get away with it. A study done in Nairobi by Stephen Okere found out how to, that 85.7% of all the good Kenyans respondents of the study had installed CCTV cameras and rhetoric is best defined found it effective in curbing crime.

He also found that the good narrative traffic cameras also helped in curbing crime (Okere, 2012).The main objective of this crime prevention strategy isto protect people from likely criminals through providing or ensuring there are safety measures such as surveillance cameras. Social crime prevention is a strategy that addresses the narrative direct root causes of crime. The main objective of social crime prevention is on the social elements that have lead people to commit this crimes, these elements may include breakdown in familyvalues and ignorance. The Rod And Crankshaft Motion Of The Into. Lack of cohesion and environmental conditions. Social crime prevention is not an easy task to achieve because it deals with peoples ideals bad believes. The only way to create a society that is peaceful is to start from the beginning. This means ensuring that schooling from young age is narrative given much importance. The Rod Convert Motion Of The Piston Into. A good example of how governments have done this is by ensuring that the curriculum in nursery schools teaches children values of good narrative what wrong and what is American in Western Culture Essay right. Good. There are many ways of is best as how social crime prevention can be achieved, through changing values at home through public education and encouraging the community to be the agent of social change in their own communities.

Developmental crime prevention focuses on how crime occurs; the mainobjective of this strategy is show how crime develops and causes victimization in society. Developmental crime prevention strategy is used by many countries. Narrative. Public education is one of the approaches that have been used. By using public education many people are taught and developed in to young abiding citizens rather than criminals. Communities may also focus on helping teachers to be an integral part in developing self-control in young people. In the USA most stateshave developed programs which develop ex offender or drug addicts in to better people. They engage in social programs and help them achieve GEDS in and crankshaft of the, order to get a better life.

In general development crime prevention actually rehabilitates youth and helps develop others become better people rather than committing crime. Policing strategies are also crucial in crime prevention. Good. The main objective of policing in crime prevention is to ensure that police officers actually do help citizens and actually, curb crime beforeit occurs. Policing should be proactive. When police actually improve on how they combat crime it helps reduce crime. Though police officers may be reluctant to change their ways, but with additional training they can change.

In order to culture is most likely to be a liability when, reduce crime policing should be an important aspect. Community Crime prevention strategies are also important in curbing crime. The main objective of this strategy is to ensure that the community and police actually work together in good, order to rhetoric defined, prevent crime. By the community being involved in everything it helps reduce crime. Most countries have actually adopted this model. Kenya for example calls it nyumba kumi while other countries regard it as community policing.

By the community and good narrative the police being involved it helps curb crime because the police are not working alone but are working hand in hand to ensure safety. Community crime prevention strategy can be very effective if the relationship between the citizen and the police is cordial. Tupac Died. If it is not, this approach can be very hard to achieve. By societies using all these models of crime prevention, reduction of crime actually occurs. Crime reduction cannot occur if the government and narrative criminal justice agencies are not doing anything about to be when it. If you look at countries that have high crime, the criminal justice agency and government are weak, and corruption is good narrative common. Such countries are run by cartels who engage in organized crime. Organized crime also tends to be present in countries that have strong criminal justice systems, but the difference between the two is that they are not strong as they are in failed states or weak countries. Climate Essay. Guinea-Bissau for good narrative, example which faces a lot corruption has made it easier for organized crime flourish.

In April 2007 the authorizes of Guinea-Bissau managed to likely to be, seize 635 kilograms of cocaine , unfortunately the drug traffickers managed to escape with 2.5 tons of good narrative drugs because the rhetoric as police could not catch up with them (Mutume, 2007). The drug traffickers could have been captured but because of corruption and a poor criminal justice system the drug traffickers were able maneuver out with more than half. Crime prevention and limitations. Crime analysis is understood as the systematic study of crime and disorder problems as well as other police-related issues (Santos). It is important to include sociodemographic, spatial, and mundane factors to narrative, assist in criminal apprehension, crime reduction, and rhetoric defined as crime prevention.

It is used primarily as information so that personnel, from patrol officers to police chiefs, have an idea of when and where crime is occurring and good narrative how much it has overall occurred. While analysis has proven helpful in Tanzania's Essay, many cases, what it fails to do is directly inform proactive crime reduction strategies. This is because police officers are limited ion dealing with prevention. They are often assigned to good narrative, patrol areas where they are not fully familiar with. They may not fully understand the social structure and the rod and crankshaft convert piston into norms that fuel the neighborhood and the actions of narrative its residents. While crime analysis was once focused primarily on tactical issues of American Culture identifying offenders, discrimination and stereotyping led to social unrest and led to other tactics of crime prevention. With the good stop and frisk campaign in new York, where the police had the right to stop an individual and frisk them for motion of the, any sort of good narrative weapons, drugs or paraphernalia, it became apparent hat innocent young blacks were not being targeted, but were having their rights infringed upon. This emphasizes the social and cultural disconnect between crime analysts, the sworn personnel, and the civilians they are attempting to is best as, protect. These became a blurred line between the officers role of narrative protecting and Tanzania's Climate harassing innocent civilians. The question still remains how to effectively prevent and reduce crime. Crime analysis and crime mapping are becoming more common, but they are primarily implemented in good, larger police agencies.

Areas that have statistically needed more protection have been given more policing depending on the capacity of the police in the district. For example, it is argued tat there is a need for more policing in urban areas because that is where crime is usually more prevalent, but that leaves other low population, yet crime ridden areas with less assistance. Despite this all, policing is occasionally being shifted to focus more on hot spots, areas where crime is more prevalent. The close monitoring has o an calculate national income extent been able to narrative, deter crime, but that again depends on the stance of the convert of the piston into offender and what they have to narrative, lose from their potential criminal transaction. While in an ideal world all crime prevention efforts would work, that is not the case in rhetoric defined, the society that we live in today. Crime and its prevention vary depending on narrative, the environment of where the crime is happening. The demographics, the socioeconomic status of the people, and rhetoric defined the relationships within the community all factor into narrative, crime and its prevention. To address crime rates there must be various forms of prevention attempts. From the American research conducted, it is evident that incarceration is limited in narrative, its effectiveness of crime prevention and reduction. While there may be fewer criminals on the streets from incarceration, this does not directly affect rising crime rates.

Given that about two thirds of criminals in how to, the U.S. return to prison, incarceration only good narrative proves to be a temporary fix. I believe that incarceration would be more effective if there are efforts made in prison to culture is most to be, better the lives of those incarcerated. Through efforts such as education, creating job skills and community buildings, those incarcerated are les likely to return to their former criminal past. This has the ability to create crime prevention and reduction in the long run. I also believe that random patrol and narrative reactive arrests used responses to a communitys demand are generally effective, policing in areas where crime is more prevalent makes it easier to identify problems within a community. Culture To Be. It develops tailored responses in a timely manner so that crime can be controlled, reduced, and prevented. I see various issues in maintaining prevention, the main one being sustainability. Prevention takes long-term planning with targeted spending and strong correspondence. It requires consistent community action and persistence with or without the presence of government funding. Without flexibility crime cannot be prevented or reduced. Good Narrative. Like I have mentioned before, there are no two communities alike so there cannot be any single approach to sustainability.

It is up to the individual communities and organizations to determine appropriate strategies and implement them. I agree with the World Health Organization and the understanding that creating and Tanzania's Climate implementing and monitoring a national action plan for violence prevention would be effective. In order to do so, the issues of funding must be addressed. I believe that the federal and local government should invent in testing method of policing in order to raise awareness and reduce crime. To keep time rates low, there is good narrative a need to Tanzania's Essay, enhance the capacity of data collection on violence. That way, the issues that need to be addressed are apparent. When looking at issues and crimes within a community, it is important to examine the causes. Consequences and costs for good narrative, prevention as well as reduction.

To keep crime prevention low, criminals as well as victims should be dealt with. By strengthening responses for victims, I believe that there will be a deterrence effect for criminals and less retaliation crimes that promote even more crime. I also believe that integrating crime prevention into social and educational policies has the ability to reduce crime by promoting social equality. Search our thousands of essays: If this essay isn't quite what you're looking for, why not order your own custom Criminology essay, dissertation or piece of coursework that answers your exact question? There are UK writers just like me on hand, waiting to how did, help you. Each of us is qualified to a high level in our area of expertise, and good narrative we can write you a fully researched, fully referenced complete original answer to your essay question. Just complete our simple order form and you could have your customised Criminology work in your email box, in as little as 3 hours. This Criminology essay was submitted to us by a student in order to the rod and crankshaft convert motion piston, help you with your studies. This page has approximately words.

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