Your Python code is almost entirely untested.

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Property-based testing is an alternative to example-based testing where tests verify that properties of the output are satisfied given randomly generated input. This randomized approach allows us to more thoroughly test against a broad range of input and can detect hard-to-find bugs better than example-based testing.

In this video, we discuss the motivation behind property-based testing. We continue by exploring the Hypothesis package, a Python package that makes data generation and property testing easy. Finally, we show an end-to-end example property-based unit test and provide some parting recommendations for applying property testing to your own code.

#python #unittest

[ssshukla]

From Reddit to YouTube and From YouTube to subscription ✅

[M-Wulff]

"Dont write tests. Write test writers"

[anselmschueler]

Cool, this is like QuickCheck for Haskell. I like the Ghostwriter thing for common properties, that's something QuickCheck lacks.

[kevon]

What’s the song playing in the back? Love the bassline

[fire]

This is really awesome, do you think the hypothesis can support objects or dicts?
I'm more interested in integration testing then unit testing, regardless this is all super useful, thanks alot for being so awesome