This screencast is a very cursory introduction to a variant of TDD that's described as everything from "London style" to "Mockist", "Outside-in", "Isolation", and "GOOS". I've recently started calling it "Discovery Testing" to emphasize its intended benefit and distance it from the now too-conflated-to-be-useful term "unit test". Since none of those other names have stuck, perhaps this one will.

The screencast may provide you with a clearer picture about what some people who talk about mock objects in a positive light are actually talking about. My goal is to illustrate and demonstrate their very narrow (but well-defined) purpose in this alternative approach to TDD. Our use of mock objects is fundamentally different from how they're used by most well-published practitioners of TDD, but both tribes tend to confuse the community by using the same language to describe differently-motivated activities.

A lot of criticism about mock objects (e.g. mocks returning mocks, mocks reducing confidence in a test's value, mocks that produce fantasy green tests) simply have no bearing on discovery testing as I practice it. Each of those symptoms suggest a wildly different (and less valuable, in my opinion) approach to mock objects which—depressingly—also represents the vast majority of their use.

If you haven't yet, please read Gary's post on test isolation this morning. I agree with Gary wholeheartedly. As much as I love how test doubles provide a mechanism to accomplish a workflow that I really enjoy using, I only use mocks to the extent I need them to specify the contract between a subject's dependencies. The real goal of discovery testing is to quickly reduce the scope of a problem and expose a rich harvest of leaf nodes in the object graph. Leaf nodes that have no dependencies and implement pure functions are easy to test, maintain, and understand. As a result, the name of the game is to maximize the leaf nodes in your object graph and minimize the collaborators. I tend to write a deep and wide tree with lots of collaborators when reducing a very complex task, and I tend to arrive at much flatter trees when the task is either simple or well-understood.

Here are some links to things covered or mentioned in the screencast:

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