In the spirit of some general discussions like this one, I'm opening this thread with the intention to gather opinions on what are the open challenges and hot topics in research on programming languages. I hope that the discussion might even bring to surface opinions regarding the future of research in programming languages.

I believe that this kind of discussion will help new student researchers, like myself, interested in PL, as well as those who are already somewhat involved.

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    $\begingroup$ community wiki ? $\endgroup$ Jun 2, 2013 at 6:14
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    $\begingroup$ I think it would really improve this question and those answering it if you quoted or summarised the text of the "Frontiers of TCS" question. The expected scope of answers to this question is unclear while the other question is more precise about what it expected. $\endgroup$
    – Vijay D
    Jun 4, 2013 at 2:01
  • $\begingroup$ when I asked this question on stackoverflow some time ago...I got downvotes and my question was closed ! $\endgroup$ Jun 5, 2013 at 16:17

4 Answers 4


I think the overall goal of PL theory is to lower the cost of large-scale programming by way of improving programming languages and the techincal ecosystem wherein languages are used.

Here are some high-level, somewhat vague descriptions of PL research areas that have received sustained attention, and will probably continue to do so for a while.

  • Most programming language research has been done in the context of sequential computation, and by now we have arguably converged on a core of features that are available in most modern programming languages (e.g. higher-order functions, (partial) type-inference, pattern matching, ADTs, parametric polymorphism) and are well understood. There is as yet no such consensus about programming language features for concurrent and parallel computation.

  • Related to the previous point, the research field of typing systems has seen most of its activity being about sequential computation. Can we generalise this work to find tractable and useful typing disciplines constraining concurrent and parallel computation?

  • As a special case of the previous point, the Curry-Howard correspondence relates structural proof theory and functional programming, leading to sustained technology transfer between computer science and (foundations of) mathematics, with e.g. homotopy type theory being an impressive example. There are many tantalising hints that it can be extended to (some forms of) concurrent and parallel computation.

  • Specification and verification of programs has matured a lot in recent years, e.g. with interactive proof assistants like Isabelle and Coq, but the technology is still far away from being usable at large scale in everyday programming. There is still much work to be done to improve this state of affairs.

  • Programming languages and verification technology for novel forms of computation. I'm
    thinking here in particular of quantum computation, and the biologically inspired computational mechanisms, see e.g. here.

  • Unification. There are many approaches to programming languages, types, verification, and one sometimes feels that there is a lot of overlap between them, and that there is some more abstract approach waiting to be discovered. In particular, biologically inspired computational mechanisms are likely to continue to overwhelm us.

One problem of PL research is that there are no clear-cut open problems like the P/NP question where we can immediately say if a proposed solution works or not.

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    $\begingroup$ if i may add, quantum computing and quantum programming languages, even if quantum computing is not happening, yet the study of how some programming concepts might be transfered in this model of computation is interesting if nothing else, programming in natural language, fuzzy programming, and even physical computation and physical programming (programming directly on matter, beyond the molecular level) $\endgroup$
    – Nikos M.
    Jun 12, 2014 at 18:48
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    $\begingroup$ @NikosM. I agree, QC is a big deal and heavily investigated. This paper shows a surprising connection between the foundations of quantum mechanics and programming language theory, unearthed only by abstraction. $\endgroup$ Jun 12, 2014 at 18:55
  • $\begingroup$ Nice, maybe a question could address these kinds of formal (or not formal) relations $\endgroup$
    – Nikos M.
    Jun 12, 2014 at 19:01

Let me list some assumptions which limit the programming language research. These are hard to break away from because they feel like they are an essential part of what programming languages are about, or because exploring alternatives would be "not programming language design anymore". With each assumption I list its limiting effects.

  1. Programs are syntactic constructs.

    • Real programmers would never use iPads to construct source code. And even if they did, they could never be as efficient as with Emacs, Eclipse, NetBeans, XCode, etc.
    • Research on alternative ways of constructing programs is not programming language design, but either graphical user interface design, or education (cf. Scratch).
  2. A partially written program cannot be executed.

    • At the very least, runtime error occurs when execution gets to a missing part.
    • What good could there be in running unfinished programs?
  3. Programs are about giving instructions to computers.

    • Programming language design has nothing to say about how to write and organize laws. apliances.
    • Bacteria do not write programs.
  4. Programming is like enginnering and cannot be done by ordinary people.

    • Ordinary people do not know the syntax, the concepts, the tools, so they cannot possibly write programs.
    • Even if we try to make it possible for ordinary people to write programs, they will only be able to write trivial stuff.

I think I could go on.

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    $\begingroup$ James: excellent, I shall inform my aunt. Martin: this is precisely the sort of thing I am talking about - non-textual programming has not been convincingly established because PL community isn't taking it seriously, because it has not been convincingly established. But it seems quite obvious to me that humans were not made for typing words on screens. We're good at throwing stuff and picking blueberries. $\endgroup$ Jun 3, 2013 at 13:21
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    $\begingroup$ @AndrejBauer As a scientific argument, "It's quite obvious to me" is not beyond improvement. If you look at the history of writing systems, of which programming languages are but a recent example, their historical trajectory has been away from logographic writing. Maybe our ability to parse strings is more relevant than blueberries. Alphabetic writing has evolved over millennia, so it's unlikely that it contains massive, easily fixable bugs. That said, I'm happy to believe that we can do better than ASCII based linear strings. I think it'll be a while before we do. $\endgroup$ Jun 3, 2013 at 16:32
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    $\begingroup$ The point of my answer is to "think outside the box". To examine hidden assumptions in PL research and to see how they limit the potential PL research has got. $\endgroup$ Jun 4, 2013 at 9:20
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    $\begingroup$ @AndrejBauer, I think limiting the scope to POPL is a mistake -- lots of this kind of work is done at OOPSLA, or at ICSE, or even at CHI. POPL isn't interested unless there's a novel formal approach, but POPL isn't the whole PL community. $\endgroup$ Jun 11, 2013 at 15:34
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    $\begingroup$ @DominicMulligan: sure, these are all very welcome ideas. With my comments I am trying to change the perception of what programming is. So if the theoretical ideas can be put to good use in practice (by which I mean that "ordinary" programmers will use them in daily life), then we have won. $\endgroup$ Jun 13, 2013 at 10:18

There is one problem I have been wondering about. I have no idea whether it qualifies as an open challenge.

Mathematical knowledge has been steadily growing with time. The theoretical foundations, concepts, notations, and proofs have evolved over the centuries. Mathematicians have managed aggregation without necessarily checking its global consistency in a systematic and formal way at any point in time (though there were attempts to do it).

We should expect programming languages and program libraries to aggregate and evolve similarly over the time. What kind of tools could help manage aggregation of programming results and libraries so as to keep them consistent and effectively usable by all, as computers can be more formal and demanding regarding consistency. Do we have to redo the libraries for each new programming language. Why should we have to choose a language because it has the right libraries for the intended application rather than for its intrinsic qualities as a programming medium?

On a different topic, you might find ideas in the following question: Are programming languages becoming more like natural languages? I realize that the idea may not appeal to many theoretical computer scientists, but it may still be useful by looking at different issues or from a different point of view. I am far from agreeing with many of the ideas that were posted, but that is what discussion is for.

  • $\begingroup$ Concistency is over-reated. $\endgroup$ Jun 3, 2013 at 5:11
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    $\begingroup$ I can see there is not much agreement on this, however modest, suggestion. Still, it might be more helpful, at least to me, to have a few explanatory words as to why. In case I was unclear, I never meant to say that mathematics could be inconsistent, only that it was not (necessarily) aggregated with consistent means (historians would tell better). From a CS point of view, I may be wrong regarding the difficulty of aggregation (I never did any technical work on this), but I am only relating user experience and commonly heard point of view, indirectly detrimental to languages produced by TCS. $\endgroup$
    – babou
    Jun 3, 2013 at 8:15
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    $\begingroup$ Well, my remark is mostly about the fact that consistency is an all-or-nothing idea, whereas in reality most software is "mostly consistent". And yet we use it and find it useful. Why then are theoreticians obsessed with what seems to be a practically unattainable and too idealistic a concept? It would seem better to be able to quantify consistency in some less trivial way. $\endgroup$ Jun 3, 2013 at 13:15
  • $\begingroup$ @AndrejBauer - Thank you for replying. I am a bit surprised by your statement, as applied to what I wrote. Nothing there supports some form of absolute consistency, but only a wish for some workable approach that would make aggregation possible and meaningful in an evolving context. Mostly consistent as you say, might do. Finding what consistent should mean for the purpose was part of the idea, and I was not suggesting any answer, trivial or otherwise. I have never been an obsessed theoretician, and I do not see from your answer where we could be at odds. $\endgroup$
    – babou
    Jun 3, 2013 at 19:10
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    $\begingroup$ I think I was just ranting about "pure theoreticians", that's all. Please ignore me. $\endgroup$ Jun 3, 2013 at 20:46

there has been a tremendous innovation and explosion in programming languages from applied and theoretical sides over the last century, yet a case might be made that this is a singular/one-time event in the history of computing, similar to an "evolutionary explosion" (see also "why are there so many programming languages?" on cs.se), and that therefore the future will not be like the past in this respect. however there are some identifiable long-range current trends in play/under development.

  • Programming/software complexity and ways of managing/minimizing/mitigating/reducing it is a topic that has always influenced language design and is possibly even more significant in the current age with very large/complex software systems quite common. it was a major aspect of OOP design rationale yet now we have highly complex OOP systems! focused pondering of it has led to classics in the field such as Mythical man-month by Brooks which in many ways is still a very valid perspective, possibly even more relevant than when it was written.

  • parallelism. there is a shift in hardware toward greater parallelism (eg multicore etc) and clock speed increases are no longer sufficient to increase performance. this shift happened around the mid 2000s and is having a major influence on language research/design. parallelism was always a topic but it has a new foremost prominence/urgency, and there is some widespread thinking/consensus that parallelism is overly complicated and difficult in programming and maybe different theoretical approaches could alleviate some of this. a nice ref on this: The Landscape of Parallel Computing Research: A View from Berkeley

  • datamining/big data. these are influencing programming language design. also new directions in database architecture are rippling/impacting programming languages.

  • supercomputing has a significant impact on language design and also overlaps with parallelism and datamining/big data eg with new languages like MapReduce.

  • visual/dataflow programming. there has been an increase in these types of "languages" (in a sense visual programming is in many ways actually decoupling programming from "languages"). also strong cross-pollination with parallelism.

  • AI. this is more of a longrange wildcard and its not very clear right now how it will impact computer languages and programming but its probably going to be very substantial. in the past [in a different form] it led to entire languages like prolog. an early indication of how it can be applied with striking results is Genetic Algorithms/Genetic Programming.

a reference that might have some helpful ideas along the lines of "future of programming languages", Beyond Java by Tate. he ponders (albeit controversially) that maybe Java (arguably one of the most sophisticated/comprehensive programming languages in existence) is starting to show its age and there are early signs of new languages/approaches emerging to fill in its place in the long term.


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