Don't test the wrong things; if you care about some precondition, that should be an input. If you need to measure a side effect, that should be an output. Don't tweak global state to do your testing.
Don't test the wrong things; if you care about some precondition, that should be an input. If you need to measure a side effect, that should be an output. Don't tweak global state to do your testing.
Rarely should a mock be “interacting with the underlying code”, because it should be a dead end that returns canned data and makes no other calls.
If your mock is calling back into other code you’ve probably not got a mock but some other kind of “test double”. Maybe a “fake” in Martin Fowler’s terminology.
If you have test doubles that are involved in a bunch of calls back and forth between different pieces of code then there’s a good chance you have poorly factored code and your doubles are complex because of that.
Now, I won’t pretend changes don’t regularly break test doubles, but for mocks it’s usually method changes or additions and the fix is mechanical (though annoying). If your mocks are duplicating a bunch of logic, though, then something else is going on.
A test should not fail when the outputs do not change. In pursuit of this ideal I often end up with fakes (to use Martin Fowler's terms) of varying levels of complexity, but not "mocks" as many folks refer to them.
[0] - https://docs.python.org/3/library/unittest.mock.html#unittes...
A particularly complex fake can even be unit-tested, if need be. Of course, if you're writing huge fakes, there's probably something wrong with your architecture, but I feel like good testing practices should give you options even when you're working with poorly architected code.