On Programming Language Choice

June 21, 2019

My opinion on programming language choice has changed over the years, from “Java is the only language I know” to “Standard ML is clearly the right answer” to something a little more nuanced now. Still, since this post is largely my opinion, I withold the right to make claims without evidence and say things you’ll disagree with. But enough qualifiers—let’s get into it.

Earlier in my career, I thought that the answer to “which language should I use” ought to be constant. That there should be One Best Language. And while I still think it makes sense to compare languages on the relative merits of their designs, answering the question “which language is best designed” is a completely different question. The question I’ll answer in this post is: “Which language should I choose to start this new project?”1

Answering “which language is best designed” is better left to programming language theorists. To claim that I (or most people reading this) have any authority to answer this question is absurd. Language design is a well-studied, complex problem, with a wealth of peer-reviewed, prior work. One semester studying programming language theory in college is not sufficient qualification to answer this question.

As it turns out, most people trying to answer the former question of “which language is best designed” knowingly or unknowingly end up answering the latter question: “which language should I choose to start this new project?” Luckily, this is a much easier question to answer, because we can narrow the choice space by asking questions like these:

But there’s one big question missing, and it 100% overshadows all other questions. Absolutely the most regret from choosing a programming language has come from forgetting to ask this question:

In this language, how easy is it to delete code?

Code is a liability. More code means more to understand and more systems to maintain. More moving parts means more points of failure. More failures mean more people trying to fix old code with new code. Company pressures to ship more features mean new code accumulating on top of old code.

This is a nightmare.

Code is a liability, so regardless of language it must be trivial to delete. And this means our language must be easy to statically analyze (ideally, though not necessarily, a language with a type system). Static analysis means that when I delete code, I can know whether other code relied on it. Renaming a function, relocating files, deleting unused features—I choose languages that make these operations easy.

As a quick aside, I’d like to elaborate on what I mean by “not necessarily” a type system. Take for example the case of JavaScript’s package.json files (specified with JSON) versus Ruby’s Gemfiles (specified with Ruby code). Neither of these configuration files are “typed” in the traditional sense, but that does not mean they’re statically unanalyzable:

My point is that even though JSON is untyped2 it’s still statically analyzable, which is better than nothing.

Going back to the case where the language does use a type system to achieve static analysis, the set of features we get expnads from “safely delete code” to loads of other things:

The full list is of course longer, but I want to re-iterate: choosing a typed language is for me a downstream consequence of choosing a language where it’s trivial to delete code.

Until now I’ve relied on an implicit assumption that only via static analysis or type checking can we easily reason about how to delete code. The alternative might be to use some sort of dynamic analysis, like running tests, rolling out refactors behind feature flags, or using some sort of manual QA checklist.

And while these techniques are still valuable, on their own they’re a poor substitute for static analysis. Why? First: they’re opt in. Programmers have to remember to write tests and to choose to use feature flags. Static analysis on the other hand is opt out. Having chosen a language with static analysis from the beginning, it applies everywhere. This also means if we choose a language we’re not satisfied with, it’s easier to change our mind in the future.

Second: they over-index on the quality of the data they’re fed. An example of “poor data quality” might be excessive use of mocks and stubs in tests. I’ve seen all too many test suites that overuse mocking and stubbing to the point where they’re really just testing the testing framework.

Another common dynamic analysis technique we use where I work is adding “soft assertions” which we define as an assertion that raises an exception if it fails in tests, but logs to Sentry in production. Before deleting the core code, we’ll preface all calls to it with unconditional soft assertions, and merge to production to see whether any assertions fire. Our confidence is directly tied to how well the production data collected in that time represents all production data.

How long spent waiting for no soft assertions is enough to get a representative sample? A day? A week? What if we have behavior that only executes on the first of the month? Or code paths that customers only hit when they’re computing quarterly accounting statements? Or yearly when they’re doing taxes? A week of data collected after changing tax code might as well be useless if that week wasn’t in March or April.

For these two reasons, dynamic analysis fails us when we need it the most: when we’re trying to delete the code that’s untested, uncommonly run, and yet critically important. With only dynamic analysis, the code that we understand the least is also the code that’s the hardest to remove.

So here’s my unsubstantiated claim: dynamic analysis techniques (anything that involves running the code) are too weak to empower people to delete code. If we want to delete code, and we do because code is a liability, we want static analysis.3

The next thing to point out is that not all forms of static analysis are created equal. Arguably Haskell’s static analysis is more powerful than C’s. While I’ll acknowledge that some languages give more static guarantees than others, as long as a language can at least reason about code that’s been mistakenly deleted, I prefer to turn my attention to other questions getting into those minutia. Comparing C and Haskell gets back into debating language design which, again, is a bit fruitless.

After all that, here’s my checklist when choosing a language to start a new project:

  1. Rule out languages where we can’t easily delete code.
  2. Narrow the remaining languages to those that fit the circumstances.
  3. Pick any language that’s left, because according to step (2) they all fit our project’s needs.

Optimizing for deleting code minimizes the biggest regret I’ve seen stemming from a language choice and keeps the door open so we can change our mind in the future. As a consequence we usually pick up extra benefits in the process (namely those that come from a good type system), but choosing a language to fit the circumstances trumps attempting to debate which type system is The Best.

 

If you read this far and were hoping that I declare one winner in the end, I’m sorry to disappoint. I’m happy to indulge you over lunch or email, or you can read the rest of my blog and guess at what I might say. Regardless, thanks for your time.


  1. Fundamentally, I believe language choice only applies to new projects. Given an existing project, the question is not “which language” but instead “should we rewrite,” to which the answer is overwhelmingly no. But that’s a topic for another post.

  2. I’m aware that it’s possible to use schemas and specs to approximate types for JSON, but this only goes to strengthen my argument: those make it even easier to statically analyze JSON.

  3. At this point you might think that I don’t believe in dynamic analysis techniques at all. That’s not the case, as I’ve written before about how I value them. I’m only saying that relying on running the code to check if code can be deleted safely does not work. Tests are still useful for plenty of other things.

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