Notes on Unit Testing

This post covers my view on unit testing, why they are important, how to make time for them, how much to test, and why I don’t believe in TDD. It draws from my personal experience working on multiple software projects, small and large, both on new code and legacy code, practices I tried to apply, what worked well and not so well. While it is more highlevel and I do not provide code snippets, this is by no means a purely theoretical essay.

The “not enough time” fallacy

Engineers and engineering teams who are not “bought” on unit testing use the excuse that there is not enough time to write unit tests. There is always a deadline or pressure to ship and unit tests, not being “product” code, get lower priority.

The problem is that we never get it right the first time - as we get a better understanding of our problem space, we need to refine our solution, which includes refactoring to better structure the code and redesign to accommodate for new requirements. This is where unit tests become invaluable in ensuring that such radical alterations of the codebase can be made safely and easily.

What I noticed first hand is that a team who is not disciplined about testing starts by churning out a significant amount of code but over surprisingly little time development slows down to a crawl because there is a lot of manual testing involved in validating any change, nasty bugs come up, and since refactoring is now scary (who knows what will break?), engineering debt keeps building up. On the other hand, teams that author unit tests from the very beginning can maintain a steady development pace indefinitely.

The funny thing is that engineers who worked in a code base with good test coverage could never go back - they immediately see the benefits and are sold on the practice - while engineers who haven’t done it know the theory, pay lip service to it, but never have time to actually implement tests.

Making time

There is, unfortunately, never enough time to do the right thing. My advice is to make time: unit tests are part of feature development, so they should be accounted for as such. Do not have separate tasks for implementing the feature and writing the unit tests - these unit testing tasks are prime candidates to be deprioritized and cut, after all, the feature works, right? Instead consider testing as part of implementation, create a single task, and adjust estimates accordingly. There is always pressure to ship, the job of a good engineer is to not cave under this pressure, set expectations, and deliver a robust solution. As mentioned above, the ability to keep a steady development pace makes the average cost of authoring tests over time seem like nothing compared to the alternative - a steady drop in development agility.

Advice to managers is to encourage a culture of quality and best practices. Strategically, shipping next week vs the week after is not as important as shipping in a couple of months vs shipping in a year, which is where the brittleness of the codebase becomes a major factor. Reward good engineering practices and you end up with well-engineered code.

That being, sometimes we do need to ship next week.

MQs

In the old waterfall development days, we had several major milestones, each spanning months of development: M1, M2 etc. As ship date came near, pressure increased, and shortcuts were taken more often. In the end, ship dates were met, but with a lot of compromises. What followed right after, when the team was burned out after the final stretch, was the so call “quality milestone” or MQ. Here, engineers were free to reduce debt while project managers went to define the future version of the product.

I personally love the concept of MQ. While I don’t doubt the existence of purely agile teams where everything is delivered after week-long sprints with high quality, most businesses make promises to customers and must meet deadlines. Sometimes the pressure increases enough that we knowingly take engineering shortcuts:

If I had a week, I’d do it the right way, but this works for now. I’ll come back and fix it later.

- Every programmer in the world at some point

After a hard deadline it’s the perfect time to schedule a mini-MQ - spend a week or two recovering from burnout and reducing debt.

This expands beyond unit tests to things like refactoring and rearchitecting code, automating manual processes, writing documentation etc.

The Test-Driven Development fallacy

The other extreme is test-driven development. The premise of test-driven development is that turning requirements into unit tests, then writing code to make those tests pass is a solid approach to engineering. This sounds great in theory but falls flat in practice.

Good software is correct, efficient, and maintainable. These qualities come from a good understanding of the problem being solved and a thought-through solution, not by making a set of test cases pass. An anecdote I like to reference when discussing this is the Sudoku solver. Ron Jeffries, one of the founders of extreme programming, wrote several blog posts in which he attempted to implement a Sudoku solver using test-driven development here, here, here, here, here, and here. The attempt failed. Around the same time, Peter Norvig implemented a Sudoku solver and wrote a blog post with a beautiful explanation of a thorough approach to analyzing the problem and coming up with a good algorithm to solve it. The point here is that a set of unit tests, no matter how comprehensive, will not design an algorithm for you. The algorithm comes from stepping back and thinking about the problem, which a test-centric approach actively discourages.

The one good thing that TDD encourages is writing tests which initially fail, then providing the implementation to make them pass, which ensures the tests themselves are correct. We can always create a test that exercises a function and then asserts a tautology (Assert.IsTrue(true)), which covers the code, makes the test pass, but provides zero value. Having a test that fails when invoked with a stub and passes when invoked with the real implementation avoids this issue.

Tests are about behavior, not design

Almost forgotten across the industry nowadays is that software can be formally proven correct. A given number of passing tests can only guarantee that for that particular set of inputs, we get the expected output - which, in case of test bugs, might not mean anything. The way to be 100% confident that the code does what we think it does is to prove this fact formally. This is not always feasible at scale, but for critical pieces of functionality, formalism is better than test cases.

That doesn’t mean tests are not needed - as soon as a line of code changes (bug fix, optimization, etc.), the formalism must be re-evaluated, and, outside fringe programming languages, we can’t automatically detect when a proof no longer holds. The point is that tests are about behavior not about design - we design to solve the problem, we test to make sure that our solution does what we expect it to do. Design comes first, tests come second, implementation is third.

Unit tests become valuable when we can make deep changes within our code and ensure there is no observable change in output. This is invaluable to engineering velocity.

How much is enough?

In terms of code coverage, I believe something around 90% can easily be achieved with a minimum of effort. Full coverage is unrealistic because the code always has some interaction with the world - making network calls, relying on time, random numbers, IO etc. These are all interactions that can sporadically fail and unit tests, by definition, must be 100% reliable. A testable design abstracts all the world interactions under interfaces that can be mocked during testing. This way, we end up with a thin layer that implements these interfaces and forwards to the real OS/library functions. This thin layer should not contain any logic beyond forwarding arguments since it is not really testable and attempting to write unit tests against it ends up with an ongoing cost of analyzing random test breaks due to failures in components outside of our control. The other place where ROI is small is testing trivial code like getters/setters. This is wasted engineering effort and provides questionably little value. That being said, this layer should be at most 10% of the code base, more likely somewhere in the 1-2% range for larger projects. Everything else should be covered by unit tests.

There is also an interesting distinction between explicit vs implicit testing - a function can be covered explicitly, by writing unit tests against it, or implicitly, by writing unit tests against other functions that end up calling this function. A good rule of thumb is to test against the interface not against the private implementation. If you can’t reach the same amount of code coverage by testing the public interface as you can by testing the implementation details, it means you have dead code in the implementation - code that cannot be reached from the public interface for any possible input. This code should be removed not tested. Unit tests have a cost themselves - if we have tests exercising a function and, during a refactoring, we change the signature of that function, we have to go update all these tests. If any refactoring we make breaks unit tests and requires us to fix them, engineering cost of maintaining test coverage is increased needlessly.

Ideally, we should break and have to update tests when we break the interface (the unit’s contract to the outside world). We should be able to freely move the implementation guts around, as keeping tests green in this case is the ultimate purpose of unit testing - ensuring output through the contract doesn’t break during internal changes. A couple of gotchas here: if we feel we need to test an implementation detail because it’s scarily complex, we have a code smell - that implementation detail should be split into multiple, less scary pieces; if we have a lot of implementation logic underneath a thin interface, we have another smell - the component (unit) is too clever and should be split into multiple components, which would necessarily pull some of the code to the interface level.

The bottom line is that we can achieve +90% test coverage without taking dependencies on implementation details.

Ease of testing

Unit testing must be easy.

Authoring unit tests should be cheap. Running unit tests should be fast and 100% reliable. Unit tests should be part of the engineering inner-loop - code/compile/unit test. Code coverage should be easy to measure. Mocking should be easy. If any of these points fall short, test coverage suffers. Good infrastructure makes it easy to author and execute unit tests. This is key in encouraging a team to use good engineering practices.

The other aspect of testing cost is design - code that is well componentized is easily testable. Monolithic code, code that implements lots of branching for various conditions, code that directly calls components outside of our control (network, UI etc.), are all hard to test. This is not an excuse to bypass testing, it’s a smell of the code itself.

Learned hopelessness

It’s easy to agree with all of the above but resign yourself to the fact that in your organization things are different - the infrastructure is not there, the culture is not there, there is no time. I believe that the most successful and long-lived software projects have a codebase ridden with compromises and outdated software practices, which is not a symptom of any problem, it’s the result of implementing a successful business. It is our duty as software craftsman to remove the compromises and update the outdated practices, question the status quo and strive to make things better. Write unit tests!