Industrial Programming Languages and Pragmatics

The approach of applying pragmatics and principles of industrial programming language design to formal methods, verification, and dependently-typed languages is something I care a great deal about.  It’s the focus of my programming language project Salt, and it was the subject of my recent lightning talk at IEEE SecDev and many of the discussions that followed.

Those who interact with me know I tend to talk about this subject quite a bit, and that I have a particular concern for the pragmatic aspects of language design.  The origin of this concern no doubt comes from my time with the Java Platform Group at Oracle; there I saw the issues that a successful industrial language must confront firsthand and the demands that arise from real-world industrial use.   I’ve also seen these sorts of issues in other places, like working on the FreeBSD and Linux kernels.

A common question I get is “what do you mean by ‘industrial language’?”  I see this frequently in the context of the academic programming language and Haskell communities.  I’ve seen this frequently enough that it’s prompted me to write an article on the topic.

Academic and Industrial PL

In my experience, there’s something of a gulf between academic knowledge about programming languages and industrial knowledge.  Academic PL is primarily concerned with things like semantics, idealized language features, and type systems (particularly type systems).  Academic PL knowledge is available through the usual academic conferences: POPL, PLDI, OOPSLA, ICFP, and the like, as well as various books and journals.

Industrial PL, by contrast, is generally less structured and accessible in its knowledge, existing primarily in the form of institutional knowledge in organizations like OpenJDK and others dedicated to maintaining major industrial languages.  Some of this makes it out into industrial conferences (Brian Goetz, the Java language architect has given a number of talks on these subjects, such as this one).

In recent history, there has been a concerted effort by the industrial PL world to tap into this knowledge.  This manifests in recent and upcoming Java language features as well as in languages like Rust which incorporate a number of advanced type system features.  I saw this firsthand in my time on the Java Platform Group, and particularly in the work that went into the Lambda project and is now going into the Valhalla project.

Academic PL, on the other hand, has tended to be dismissive of the knowledge base of industrial PL as it lacks the kind of formalism that modern academic PL work demands.  The source of this, I believe, is rooted in the differing goals and scientific foundations.  The concerns of academic PL are well-addressed by higher mathematics, where the concerns of industrial PL are better answered through disciplines such as organizational psychology.  In truth, both are important.

It is my belief that both worlds need to make an effort to get out of their silos.  The industrial PL world needs to do a better job at describing the problems it solves and reporting its solutions and findings.  The academic PL world needs to acknowledge the intellectual merit of creating real-world realizations of the idealized features presented in academic PL conferences.

Principles of “Industrial Programming Languages”

A first step towards getting out of the silo is to clearly express what the notion of an “industrial programming language” means.  This phrase is an example of what might be called a “common-sense” term.  Any of my colleagues from OpenJDK certainly could have expounded at length as to its meaning, industrial programmers could probably list a number of examples, and even academics have some notion of what it means (often some variation on “icky details we don’t want to deal with”).

The problem with “common sense” notions is that everyone has their own ideas of what they mean.  To effectively study an issue, we must refine such notions into more concrete and unambiguous ideas.  A truly principled effort in this direction would, in my opinion, be worthy of peer-reviewed publication.  My goal here is merely to begin to explore the ideas.

As a first draft, we can list a number of issues that “industrial” software development must confront, and which “industrial” programming languages must provide tools to address:

  • Very large codebases, running from hundreds of thousands to tens of millions of lines of code
  • Long-running development, spanning many development cycles and lasting years to decades
  • Very large staff headcounts, distributed teams, staff turnover, varying skill-levels
  • Withstanding refactoring, architectural changes, and resisting “bit-rot”
  • Large and complex APIs, protocols, interfaces, etc, possibly consisting of hundreds of methods/variables
  • Interaction with other complex systems, implementation of complex standards and protocols
  • Need for highly-configurable systems, large numbers of possible configurations
  • Large third-party dependency sets and management thereof

The common theme here is unavoidable complexity and inelegance.  It is a well-known fact in many disciplines that different factors emerge and become dominant at different scales.  In algorithms and data structures for example, code simplicity is often the dominant factor in execution time at small scales, asymptotic complexity dominates at mid-scale, and I/O cost dominates all other concerns at large scales.  Similarly, with software development, certain stresses (such as the ones listed above) emerge and become increasingly important as the scope of a development effort increases.

Industrial practice and industrial languages aim to withstand these factors and the stresses they produce as development scope scales up to modern levels.  An important corollary is that the failure to provide mechanisms for dealing with these factors effectively limits the scope of development, as has been the case throughout the development of industrial practice.

Dealing with Development at Scale

I routinely use the term “industrial pragmatics” to refer to the various methods that have been developed in order to cope with the stresses that emerge in large-scale software development.  In particular, I use the word “pragmatics” because the efficacy of these techniques generally can’t be effectively evaluated using theory alone.  They involve both complex interactions at scale as well as human behavior- two phenomena that defy theoretical abstraction.

It is worth exploring some of these techniques and why they have been so successful.

The Path of Least Eventual Cost/Pain

At very large scales, the only successful strategy for managing things quickly becomes the path of least eventual cost (or for the more blunt, pain).  A key word in this is eventual, as it is critically important to realize that some decisions may minimize short-term cost or maximize short-term gain in some way but eventually lead to a much higher cost in the long run.  At large scales and in long-running projects, short-sighted decision-making can dramatically increase the eventual cost, to the point of compromising the entire effort.

This is, in my opinion, one of the critically-important principles of industrial pragmatics.  Any tool, technique, language, or practice must be evaluated in terms of its effect on eventual cost.

Modularity and Interfaces

More than any other technique, modularity has proven itself invaluable in managing the complexity of industrial-scale software development.  The most widely-successful adoption of this technique came in the form of Object-Oriented programming, which removed the barriers that prevented software development from scaling up.  Since the advent of OO, other adaptations have emerged: Haskell’s typeclasses, various module systems, and the like, all of which share common features.

A key features of these systems is that they manage complexity by encapsulation- walling off part of a huge and complex system, thereby limiting the concerns of both the author of that component as well as the user.  This significant limits the set of concerns developers must address, thereby limiting the size and complexity of any “local” view of the system.

Constraining Use

Large systems quickly build up a huge number of interfaces and configurations, and the fraction of possible uses of those interfaces and configurations that represent “valid” or “good” use quickly shrinks to the point where the vast majority of uses are incorrect in some way.  Well-designed systems provide mechanisms to restrict or check use of interfaces or configurations to identify misuse, or else restrict use to the “good” cases in some way.  Badly designed systems adopt an “anything goes” mentality.

An prime example of this comes in the form of cryptographic APIs.  Older crypto APIs (such as OpenSSL) provide a dizzying array of methods that can result in an insecure system at the slightest misuse.  This problem was identified in recent academic work, and a series of new crypto APIs that restrict use to correct patterns have been created.  Type systems themselves also represent a technology that restricts usage, greatly constraining the space of possible cases and greatly improving the degree to which a program can be analyzed and reasoned about.

Managing Implicit Constraints

In a sufficiently-large system, a significant effort must be dedicated to maintaining knowledge about the system itself and preventing the introduction of flaws by violating the implicit logic of the system.  Well-managed systems minimize the externally-facing implicit constraints, expose APIs that further minimize the degree to which they can be violated, and ensure that the unavoidable constraints are easily detected and well-known.  Badly-designed systems are rife with such implicit constraints and require expert knowledge in order to avoid them.

This is one area that I believe formal methods can make a huge impact if adapted correctly.  There are many ways of approaching this problem: documentation, assertions, unit tests, and so on, but they are all deficient for various reasons.  Even the ability to explicitly codify invariants and preconditions would be a considerable boon.  More modern languages and tools such as Rust and JML approach this, but only formal methods can provide a truly universal solution.

The Art of API (and Protocol/Standard) Design

The design of APIs is an art that is underappreciated in my opinion.  It takes considerable skill to design a good API.  It requires the ability to generalize, and more importantly, to know when to generalize and when to hammer down details.  It requires the ability to foresee places where people will want to change things and how to make that process easy.  It requires a good sense for how people will build upon the API.  It requires a good sense of necessary vs. unnecessary complexity.  Lastly, It requires a knack for human factors and for designing things to make doing the right thing easy and the wrong thing hard or impossible.

Managing Failure

Failure of some kind becomes unavoidable at large scales.  Software systems subject to real use will eventually be used incorrectly, and it will become necessary to diagnose failures quickly and efficiently.  This is unavoidable, even in a perfect world where all of our own developers are perfect- users and third-party developers can and will do things wrong, and they need to be able to figure out why.  In the real world, our own developers also must diagnose failures, if only in the internal development and testing processes.

This, I think, is one of the most undervalued principles in both academic and even industrial circles.  Far too many people want to answer the problem of managing failure with “don’t fail”.  But failure can’t be avoided or wished away, and failing to provide tools to quickly and effectively diagnose failure translates to increased cost in terms of development times, testing times, and diagnosis of failure in the field (in other words, much more eventual cost and pain).

In my own career, Java and other JVM languages have proven themselves to have the best track record in terms of easy diagnosis of failure of any language I’ve seen in common use, both with myself and with others.  By contrast, languages that don’t provide the kind of functionality that JVM languages do, either because they can’t (like C), or because they choose not to (like Haskell) tend to slow things down as it takes longer to diagnose issues.  Lastly, I’ve also dealt with some truly nightmarish platforms, such as embedded or boot-loaders, where diagnosis is a task for experts and takes considerable effort and time.

Human Factors

In addition to the principles I’ve discussed thus far, human factors- particularly the difficulty of modifying human behavior -plays a key role in the design of industrial languages.  A considerable amount of thought must go into how to influence user behavior and how to get them to adopt new techniques and methodologies, often with little chance of recovering from mistakes.  Unlike the other principles, these are rooted in psychology and therefore cannot be the subject of the kinds of mathematical theories that underlie many aspects of PL.  They are nonetheless critical to success.

Making it Easy to be Good/Hard to be Bad

One of the most successful principles I’ve learned is that of making it easy to be good and hard to be bad.  This means that “good” use of the tool or language should be easy and intuitive, and it should be difficult and non-obvious how to do “bad” things (this of course presupposes an accurate notion of good vs. bad, but that’s another discussion).

An excellent example of this comes from the language Standard ML.  Functional programming with immutable data is the notion of “good” in SML, and imperative programming with mutable data is strongly discouraged.  It’s tolerated, but the syntax for declaring and using mutable state is gnarly and awkward, thereby encouraging programmers to avoid it in most cases and encapsulate it when they do use it.  Java and other languages’ design reflects a notion of object-orientation being “good”, with global state being strongly discouraged and made deliberately difficult to use.


Coordinating human activity at scale is an extremely difficult problem.  Regimentation- creating common idioms that serve to make behavior predictable and facilitate implied communication is a very common technique for dealing with the problem at scale.  In the context of software development, this takes the form of things like design patterns and anti-patterns, well-defined interfaces and standards, style documents, and programming paradigms.

Languages that succeed at large-scale development tend to provide facilities for this kind of regimentation in one way or another.  This incidentally is one major argument in favor of types: they provide a considerable amount of implicit communication between developers and users of an interface.  Similarly, languages with well-known idioms and built-in usage patterns tend to produce more coherent codebases.  Java is one example of this.  Haskell is quite good in some ways (such as its typeclasses, tightly-controlled use of side-effects, and very powerful type system) and deficient in others (five string types).  Python achieves good regimentation despite its lack of types, which I believe is a crucial factor in its popularity.

Making Complex Ideas Accessible

A key to success in industrial development is the ability to make a complex idea accessible.  In my own experience, this was one of the core design principles of the Lambda project in Java.  We had the challenge of introducing higher-order functional programming and all the notions that come along with it in a way that was accessible to programmers used to the stateful OO style of thinking.  Conversely, many efforts have gotten this wrong, as exemplified in the infamous quote “a monad is just a monoid in the category of endofunctors”.

This translation between idioms is quite difficult; it requires one to deeply understand both sides of the fence, as well as which use cases and concerns are most common on both sides.  However, it’s critical- people don’t use what they can’t understand.  The best way to facilitate understanding is to present an idea or principle in a context the audience already understands.


Just as failure must be dealt with in the context of software systems, changing behavior and practices must be dealt with in the context of human factors.  As an example, programming practices based on the functional paradigm are becoming increasingly recommended practice due to a number of changing factors in the world.  Many languages face the challenge of adapting their user base to these new practices.

The mentality of harm-reduction often proves the most effective attitude when it comes to changing human behavior, both here and elsewhere.  Harm-reduction accepts that “bad” behavior takes place and focuses its efforts on minimizing the negative impacts on oneself and others and encouraging a transition towards better behavior in the long-run.  This reflects the realities of industrial programming: software can’t be rewritten into a new paradigm all at once, and some legacy systems can’t be eliminated or rewritten at all.

In the design of industrial languages and tools, this takes the form of several adages I’ll list here: provide options instead of demands, minimize upfront costs and facilitate gradual transitions, work with the old but encourage the new.

On Seeming Contradictions

It might seem, particularly to the more academically and philosophically inclined, that many of these principles I’ve elaborated contradict each other.  How can we provide regimentation while working with the mentality of harm-reduction?  How can we restrict use to the “good” behaviors while gracefully handling failure?

The solution is in itself another critically important principle: the notion of avoiding absolutist conflicts of ideas.  In the practical world, ideas tend to be useful in certain contexts, and a given principle needs to be evaluated in the context of use.  Put another way, “it’s important to use the right tools for the job”.

On a deeper level, ideological wars tend to end with both sides being “wrong”, and often some synthesis of the two being right.  In physics, the debate over whether light was a wave or a particle raged for centuries; in truth, it’s both.  There are many other such examples throughout history.


If we reduce everything I’ve discussed down to its essence, we arrive at the following statement: as the scale of industrial programming increases, the dominant concerns become irreducible complexity and inelegance, and human factors associated with coordinating effort.  Thus, “industrial pragmatics” refers to a set of techniques and principles for managing these concerns, and industrial programming languages are those languages that consider these techniques in their design.


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