Thursday, September 22, 2011

"Strict" Unit Testing -- Everything In Isolation Is Too Much Work

Folks like to claim that unit testing absolutely requires each class be tested in isolation using mocks for all dependencies.  This is a noble aspiration, but doesn't work out perfectly well in Python.

First, "unit" is intentionally vague.  It could be a class, a function, a module or a package.  It's "unit" of code.  Anything could be considered a "unit".

Second--and more important--the extensive mocking isn't fully appropriate for Python programming.  Mocks are very helpful in statically-typed languages where you must be very fussy about assuring that all of the interface definitions are carefully matched up properly.  

In Python, duck typing allows a mock to be defined quite trivially.  A mock library isn't terribly helpful, since it doesn't reduce the code volume or complexity in any meaningful way.

Dependencies without Injection

The larger issue with trying to unit test in Python with mock objects is the impact of change.

We have some class with an interface.

class AppFeature( object ):
    def app_method( self, anotherObject ):
        etc.

class AnotherClass( object ):
    def another_method( self ):
        etc.

We've properly used dependency injection to make AppFeature depend on an instance of AnotherClass.  This means that we're supposed to create a mock of AnotherClass to test the AppFeature

class MockAnotherClass( object ):
    def another_method( self ):
        etc.

In Python, this mock isn't a best practice.  It can be helpful.  But adding a mock can also be confusing and misleading.

Refactoring Scenario

Consider the situation where we're refactoring and change the interface to AnotherClass.  We modify another_method to take an additional argument, for example.

How many mocks do we have?  How many need to be changed?  What happens when we miss one of the mocks and have the mysterious Isolated Test Failure?  

While we can use a naming convention and grep to locate the mocks, this can (and does) get murky when we've got a mock that replaces a complex cluster of objects with a simple Facade for testing purposes.  Now, we've got a mock that doesn't trivially replace the mocked class.

Alternative: Less Strict Mocking

In Python--and other duck typing languages--a less mock-heavy approach seems more productive.  The goal of testing every class in isolation surrounded by mocks needs to be relaxed.  A more helpful approach is to work up through the layers.
  1. Test the "low-level" classes--those with few or no dependencies--in isolation.  This is easy because they're already isolated by design.
  2. The classes which depend on these low-level classes can simply use the low-level classes without shame or embarrassment.  The low-level classes work.  Higher-level classes can depend on them.  It's okay.
  3. In some cases, mocks are required for particularly complex or difficult classes.  Nothing is wrong with mocks.  But fussy overuse of mocks does create additional work.
The benefit of this is 
  • The layered architecture is tested the way it's actually used.  The low-level classes are tested in isolation as well as being tested in conjunction with the classes that depend on them.
  • It's easier to refactor.  The design changes aren't propagated into mocks.
  • Layer boundaries can be more strictly enforced.  Circularities are exposed in a more useful way through the dependencies and layered testing.
We need to still work out proper dependency injection.  If we try to mock every dependency, we are forced to confront every dependency in glorious detail.  If we don't mock every single dependency, we can slide by without properly isolating our design.