nw-pbt-python

Property-based testing generates diverse inputs automatically to uncover edge cases. Using Hypothesis strategies, it explores the input space of Python code to ensure correctness beyond what manual example tests can cover.

You can use Hypothesis with pytest by writing tests with Hypothesis strategies and decorators like given. Running pytest automatically generates diverse inputs, seamlessly integrating property-based testing into your existing test suite.

Yes, you can test stateful systems using Hypothesis stateful testing capabilities. It supports composite strategies to model and validate complex stateful systems, ensuring data structure invariants hold during transitions.

Property-based testing with Hypothesis is well-suited for validating sorting algorithms, data structure invariants, and API validation. It applies to unit, integration, and stateful testing across data-heavy Python projects.

Example-based tests check specific hardcoded inputs, while Hypothesis property tests generate diverse inputs from strategies to explore the input space broadly. This approach reveals edge cases that example tests often miss.

You use assume to filter out invalid generated inputs and settings to configure test behavior like max examples. These decorators help refine Hypothesis strategies for precise property-based testing control.