There And Back Again on Code Comments

It’s interesting how learning is sometimes like travel. At the end of the journey, you end up back where you started, only with a broadened perspective.

Trivial example: code comments. As a new programmer, I commented my code extensively. This was probably to compensate for my difficulties in reading code. It also allowed me to be lazy in the code I wrote. There isn’t as much of a need for clear and readable code when there are always plain English comments to fall back on.

Then I joined a team that was militantly against comments. Every comment was an admission of a failure to write readable code. I took this on-board and disciplined myself not to rely on comments. This forced me to spend more time on refactoring my code to make it as readable and “self-documenting” as possible. I still wouldn’t say I’m there yet, but I’m confident that my code is much more readable now than it was then.

However, in the last year or so, I’ve started adding comments again. Not too many, but here and there to add more context. It felt like blasphemy at first. Some of my comments are even somewhat redundant, but I’ve since learned that encoding the same information in redundant ways aids comprehension. For example, traffic lights use both colour and position to encode the same information.

Superficially it seems as though I’ve returned to where I started: I’m commenting code again. However, if I hadn’t disciplined myself to do without comments for a while, I wouldn’t have been forced to learn how to write more readable code. Abstaining from code comments is a forcing function for more readable code. At some point, you can re-integrate commenting as a useful tool rather than a crutch.

Interestingly, I’m confident that if I could travel back in time and tell past-me or any of my old teammates that, actually, comments can be a valuable tool to aid comprehension, I’m positive I would encounter strong resistance. This is just as it should be; I don’t think it can be any different. When you’re learning a new skill, it’s necessary to become somewhat closed off and follow a direction single-mindedly for a while. It’s part of the learning process. If you were too suggestible and deviated too easily, you would go around in small circles and never get anywhere.

You see the same pattern across the development community when a new technology or framework is introduced (I won’t name names. I’m sure you can think of some examples). In the early days, there is hype and zealotry. Then over time as more developers adopt the tech into production and real issues with it emerge, there is disillusionment and abandonment. Finally a more realistic, calibrated picture of the trade-offs of the technology emerge.

Hype Cycle graph by Jeremykemp at English Wikipedia, CC BY-SA 3.0,, Middle Earth photo by Henry Xu on Unsplash

How to Improve Your Tests by Being an Evil Coder

Note: this article assumes you’re somewhat familiar with the idea of Test-Driven Development.

Automated tests improve (minimally) the quality of your code by revealing some of its defects. If one of your tests fails, in theory this points to a defect in your code. You make a fix, the test passes, and the quality of your software has improved by some small amount as a result.

Another way to think about this is that the tests apply evolutionary selection pressure to your code. Your software needs to continually adapt to the harsh and changing conditions imposed by your test suite. Versions of the code that don’t pass the selection criteria don’t survive (read: make it into production).

There’s something missing from this picture though. So far, the selection pressure only applies in one direction: from the tests onto the production code. What about the tests themselves? Chances are, they have defects of their own, just like any other code. Not to mention the possibility of big gaps in the business requirements they cover. What, if anything, keeps the tests up-to-scratch?

If tests are actually an important tool for maintaining code quality, then this is an important question to get right. Low-quality tests can’t be expected to bring about higher quality software. In order to extract the most value out of automated tests, we need a way to keep them up to a high standard.

What could provide this corrective feedback? You could write tests for your original tests. But this quickly leads to an infinite regress. Now you need tests for those tests, and tests for those tests, and so on, for all eternity.

What if the production code itself could somehow apply selection pressure back onto the tests? What if you could set up an adversarial process, where the tests force the production code to improve and the production code, in turn, forces the tests to improve? This avoids the infinite regress problem.

It turns out this kind of thing is built into the TDD process. Here are the 3 laws of TDD:

  1. You must write a failing test before you write any production code.
  2. You must not write more of a test than is sufficient to fail, or fail to compile.
  3. You must not write more production code than is sufficient to make the currently failing test pass (emphasis mine).

It’s following rule 3 that applies selection pressure back onto the tests. By only writing the bare minimum code in order to make a test pass, you’re forced to write another test to show that your code is actually half-baked. You then write just enough production code in order to address the newly failing test, and so on. It’s a positive feedback loop.

You end up jumping between two roles that are pitted against each other: the laziest developer on the planet and a test engineer who is constantly trying to show the developer up with failing tests.

Another benefit to being lazy is that it produces lean code. At some point, there are no more tests to write; you’ve implemented the complete specification as it’s currently understood. When this happens, you will often find that you’ve written far less code than expected. This is a win because all else being equal, less code is easier to understand.

Reading about this is one thing, but it needs to be tried out to really grasp its benefits. It turns out there is an exercise/game called Evil Coder that was created to practise this part of TDD. You pair up with another developer, with one person writing tests and the other taking the evil coder role:

Evil mute A/B pairing: Pairs are not allowed to talk. One person writes tests. The other person is a “lazy evil” programmer who writes the minimal code to pass the tests (but the code doesn’t need to actually implement the specification).

You can try this out by heading along to the next Global Day of Code Retreat event in your city – they are a lot of fun.

TL;DR: Improve your tests and your production code as a result, by being lazy and evil.

Thanks to Ali and Xiao for proofreading and providing feedback on a draft of this essay.