I'm Brandon Smith, a programmer in Austin, Texas. More about me.


Thoughts on Testing


Today I was thinking about tests.

Skepticism #

I'm skeptical of tests. I'm not against tests (even though my coworker opened a conversation today with "I know you hate tests" and I quickly projected my voice across the office as I said "I don't hate tests!" just to make sure everyone knew), but a lot of people really love tests and I sometimes feel like I have to pour some cold water on that.

Cost #

The thing is that tests are code, and all code is technical debt, but unlike normal code tests can grow without bounds, and they don't always get seen as a something with a cost because they don't get shipped to production. But every test is...

Code that takes time to write

Code that takes time to maintain (when requirements change, dependencies change, etc)

Code that takes time running in CI

And bad tests can give a false sense of security

So I think it's fair to say that (despite certain engineering books and management directives) the optimal number of tests isn't as many as we can possibly come up with.

Judgement calls #

So what is the optimal number of tests? It depends, but here are some ways I like to frame the question

"Is this something static analysis can test?" #

Anything that can be checked by static types, linting, etc, is better caught by those than by tests. A whole lot of things can't, but this is a starting point.

It's not just personal preference: static analysis prevents entire classes of bugs at once, while tests cover individual inputs. You always might forget to write a test for an edge case, but a type checker won't.

But static analysis isn't available in every language, and in those unit tests become more important as they try to fill that gap.

"How often will the behavior intentionally change?" #

This question tells us how often we'll get false-positives (tests that need to "be fixed"). Consider two functions having tests written for them:

The first is a fibonacci function. The fibonacci sequence is well-defined, it has a clear definition, it will never change. If we test a given input + output combination, and our test fails, it will always mean our code is broken. There will never be a false-positive (failed test when the implementation is actually doing what we want it to do), because if we ever wanted fib(3) to equal 100, we're no longer talking about a fibonacci function.

Imagine another function that returns a greeting message containing a person's name (eg. "Hello, Brandon!"). Suppose we write a test that checks that greet("Brandon") returns "Hello, Brandon!":

function greet(name) {
  return `Hello, ${name}!`

test("greeting is correct", () => {
  expect(greet("Brandon")).toBe("Hello, Brandon!")

Now, suppose that the product manager comes to us one day and says, "We have a new design, we'd now like it to say "Greetings Brandon, welcome to our website!"

function greet(name) {
  return `Greetings ${name}, welcome to our website!`

test("greeting is correct", () => {
  expect(greet("Brandon")).toBe("Hello, Brandon!") // fail

We change the code behavior, and now our test fails! But the failure isn't because we introduced a bug, it's because we changed our mind. The new message is now the correct behavior of greet(), and so the test is now incorrect and needs to be updated. Time spent "fixing" the test is pure overhead.

Put differently: what's the definition of "correct" for the code being tested, and how durable is that definition?

Most cases aren't as clear-cut as these two, but in general:

"Does the test just mirror the logic being tested?" #

Consider the following:

function calculation(n) {
  return n * 2 + 6

test("calculation returns correct output", () => {
  const n = 4
  expect(calculation(n)).toBe(n * 2 + 6)

This hasn't told us anything interesting about the logic being tested, we're just writing it twice. It'll tell us if calculation's behavior changed, but not if it's wrong. Good tests check against intent instead of behavior. Example:

function isEligible(age) {
  if (age < 18) {
    return false
  } else {
    return true

test("isEligible returns correct output", () => {

Here we're documenting a meaningful concept in our test that isn't directly represented in the code being tested, and so it's more likely to be stable over time than implementation details.

"Is the test simpler than the implementation?" #

Along the same lines, a good candidate for testing is code that's complicated enough for its behavior to be hard to just copy in a test. A great function to test is one where you're not sure it's going to do the right thing just by looking at it.

For code like this, the test will be simpler than what it's testing. "Test intent instead of implementation"; the intent of most code will be simpler than the implementation, and we want the test to reflect the intent.

"How much does the code being tested interact with the outside world?" #

Mocking adds a ton of complexity and reduces the amount of functionality actually being tested. It makes code both harder to test and less valuable to test.

But it's unavoidable when you're testing code that's coupled to external systems. Unit tests that rely on outside systems are never a good idea; they can break mysteriously and inconsistently.

Sometimes mocking can be avoided by re-structuring your code, but sometimes it can't be. If it can't be, the need for lots of mocking is one clue that a piece of code might not be worth (unit) testing.

"How important is this code?" #

Of course, the more business-critical a piece of code is, and/or the more widespread its use is (eg. a core component used in lots of places), the more valuable it is to test. It may be worth testing coreBusinessRule even if the implementation is trivial, even if our test can only make sure it doesn't change by surprise. It may be worth dealing with a ton of mocking just to make sure the testable part of an auth flow prevents illegal operations. Everything is a judgement call.

Integration tests #

So far I've been talking about unit tests, but integration tests are different in certain ways, especially the mocking question: almost by definition you aren't really mocking systems, you're testing the interactions between them that are otherwise hard to test.

Other rules still apply though: writing tests that reflect intent (eg, "click the button with the label 'Submit'") instead of implementation ("click the third <button> tag on the page") will make them less fragile. Favoring tests that check valuable behavior ("can the user log in?") over tests that check unvaluable behavior ("is the submit button blue?") makes the best use of your effort. Etc.

UI tests #

I hinted above, but my controversial take is that I think UI rendering code is rarely worth unit-testing based on these heuristics. User interfaces:

But I really only mean the rendering layer, not the entire front-end codebase:

And then, a library of UI components that are really general and used in a bunch of places may tip the scales towards unit testing. The value is higher, and the cost (of mocking, etc) is usually lower because they're meant to be reusable. They may also change less often than product features.

Closing thoughts #

I don't hate tests (I promise, Avid!), I just try to question them on a case by case basis, and I haven't met a lot of people who do.

They have a very real organizational cost, which as engineers we need to be aware of. Having more of them makes more little green things light up in the console and makes that coverage number go up, so it feels good to add them. But like anything, testing needs to be done with consideration.