Testing your Python Code with Hypothesis
I can think of a several Python packages that greatly improved the quality of the software I write. Two of them are pytest and hypothesis. The former adds an ergonomic framework for writing tests and fixtures and a feature-rich test runner. The latter adds property-based testing that can ferret out all but the most stubborn bugs using clever algorithms, and that’s the package we’ll explore in this course.
In an ordinary test you interface with the code you want to test by generating one or more inputs to test against, and then you validate that it returns the right answer. But that, then, raises a tantalizing question: what about all the inputs you didn’t test? Your code coverage tool may well report 100% test coverage, but that does not, ipso facto, mean the code is bug-free.
One of the defining features of Hypothesis is its ability to generate test cases automatically in a manner that is:
So let’s look at how Hypothesis can help you discover errors in your code.
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