property-based-testing

Property-based testing validates pure functions and data transformations by generating random inputs and asserting invariant properties hold. It automates edge case discovery for normalization, validation, and serialization logic, replacing manual example-based tests with comprehensive verification.

Use property-based testing for pure functions and data transformations with clear contracts, such as serialization and normalization. When you need to validate invariant properties across a wide range of random inputs rather than isolated examples, this pattern detects edge cases example-based tests miss.

Create effective input strategies by following input strategy best practices that define how to generate random inputs matching your function's contract constraints. This ensures generated data covers valid ranges and boundary conditions for robust validation and normalization testing.

Common mistakes when writing property-based tests include generating inputs that violate function contracts, asserting trivial properties, and ignoring library-specific settings. A quality checklist and red flags guide help identify these pitfalls and configure settings correctly to ensure meaningful validation.

Yes, property-based testing works exceptionally well for data normalization and validation. By automating the creation and verification of properties, it generates random inputs to ensure your validation rules and normalization logic consistently maintain expected invariant properties.