As a follow up to Python Mocking 101: Fake It Before You Make It, this post covers some higher-level software engineering principles demonstrated in our Python testing over the past year and half. In particular, the idea of patching mock objects in unit tests is revisited and explored in greater depth.

Our system is made up of a collection of services engaging in sophisticated interactions. It became obvious early on that testing manually would only get us so far. Here are some insights that have proven useful as we transitioned from a mostly manual work mode to a mostly automated one in QA testing.

In Python, mocking is accomplished through the unittest.mock module. The module contains a number of useful classes and functions, the most important of which are the patch function (as decorator and context manager) and the MagicMock class. Mocking in Python is largely accomplished through the use of these two powerful components.

Building Lambda functions is fun, but we needed a way to preview what we've done before paying to do it in production. So we built an emulator. Enjoy!