August 2018
Intermediate to advanced
332 pages
9h 12m
English
Content preview from Clean Code in Python
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Property-based testing
Property-based testing consists of generating data for tests cases with the goal of finding scenarios that will make the code fail, which weren't covered by our previous unit tests.
The main library for this is hypothesis which, configured along with our unit tests, will help us find problematic data that will make our code fail.
We can imagine that what this library does is find counter examples for our code. We write our production code (and unit tests for it!), and we claim it's correct. Now, with this library, we define some hypothesis that must hold for our code, and if there are some cases where our assertions don't hold, the hypothesis will provide a set of data that causes the error.
The best thing about unit ...
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