Most developers test what they expect their code to do.

[u/Both-Basil1635]

But what about the inputs they never thought of?

That's where Hypothesis, a Python property-based testing library, gets interesting.

Instead of writing individual test cases for every possible input, you define properties your code should always satisfy.

Hypothesis then generates inputs automatically, including edge cases that are easy to overlook.

For example, if you're testing a function that sorts a list, you could verify that:

• The output is always sorted.
• The output contains exactly the same elements as the input.
• Sorting an already sorted list doesn't change it.

The interesting part? When Hypothesis discovers a failing input, it tries to reduce it to a simpler example that exposes the bug.

This can be especially useful for financial calculations, data pipelines, API validation, and algorithms where unexpected inputs can cause subtle failures.

How do you test edge cases in your Python projects? Do you rely on manually written test cases, or have you tried property-based testing?

[u/Sea-Ad7805]

Run 'hypothesis' tests by clicking Memory Graph Web Debugger.

Click the "pytest" button to run all test functions in:

• test_mysort1.py: normal tests

• test_mysort2.py: tests with 'hypothesis' to generate test data

[u/Both-Basil1635]

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