I’m a big fan of automated testing, but it can be hard to get it right. I’ve seen large code bases with no automated tests, and small code bases with lots (and lots) of automated tests. Taking for granted that no tests is A Very Bad Thing, let’s take a look at the problem of writing too many.

The Test Pyramid

The classic Test Pyramid indicates how many of what sort of tests we should write:

UI tests run the application via its user interface, and cover the whole system front-to-back. They run relatively slowly, are more complex, and therefore more expensive. They’re also more fragile - a bug is more likely to break any one UI test, as each test covers a large slice of the system.

Service tests run the application via its API, and cover the API, business logic and data stores. They’re faster and less complex than UI tests, and therefore less expensive. They’re more robust - a bug is less likely to break any one Service test, because each test covers less of the system.

Unit tests run individual components of the system, usually classes or methods. They cover its API and business logic, but not usually its data stores, or anything that can’t execute in-memory. They’re fast, simple, and therefore inexpensive. They’re robust - a bug is unlikely to break any one Unit test, because each test is narrowly focused.

Ok, so a few UI tests, a few more Service tests, and lots of Unit tests, right? Well, yes and no.

The Point of Tests

Ayende recently wrote about the goal of writing tests, which is often mistaken for:

  • Getting 100% test coverage
  • Ensuring classes are decoupled, and we have tests for every class in isolation

…it’s neither of those. The goal (or the point) of tests is to have:

  • An executable specification proving that your system behaves as it should
  • A safety net so you can add features and refactor with confidence you haven’t broken existing functionality

There are other advantages, but I’d say those are the main two. Notice they’re not focused on metrics or design approach, but on ensuring a system’s functionality. Systems generate value and make money by behaving correctly - not by having perfect decoupling, using the latest frameworks, or yielding graphs with lots of green on them. The only point of those things is to make correct behaviour easier to achieve.

With that in mind, the most valuable tests are those which do the most to ensure a system’s behaviour. Unit tests on an isolated class prove that class behaves correctly, but give very little assurance the system does. To quote Ayende:

“The key issue with unit testing the system as a set of individually separated components is that concept that there is value in each component independently. There isn’t.”

That’s an insight I’d like to see spread. With it in mind, let’s update the Test Pyramid.

Updating the Pyramid

Let’s consider a couple more characteristics of UI, Service and Unit tests - coupling, and value:

The Testing Pyramid

UI tests and a system’s implementation are decoupled. Large-scale changes can be made, and if the same UI is produced, UI tests don’t need to change. Each test covers entire slices of the system, and adds the most value.

Service tests and a system’s implementation are more coupled. Implementation changes are more likely to require Service test changes. Each test covers whole slices of the system backend, and adds lots of value.

Unit tests and a system’s implementation are tightly coupled. Implementation changes will often require Unit test changes. Each test covers parts of the system’s components, and adds the least value.

So lots and lots of unit tests does not a valuable test suite make - the best approach is to write fewer unit tests, and make each one more valuable.

Valuable Unit Tests

I’d offer the following guidelines:

  • Most unit tests should start at the API and execute the entire stack in-memory, with stubs where necessary.
  • Use your production system’s Dependency Injection code to plug together your test classes. Build your production DI container, then replace services where necessary. Using the DI container not only exercises the DI setup, but helps decouple unit tests from the object trees they’re testing.
  • Write a domain-specific framework so you can easily set up the models and dependencies a test needs.
  • Only write ‘isolation’ tests for complex business logic. Write one test per code path, with each one executing a particular business circumstance.

This approach maximises unit test value, and minimises coupling between the tests and system. You’ll write fewer tests, but get more from each one, have a lower maintenance burden and less of a barrier to improving your system.

Happy testing!