Does this work with pytest?

Yes, Hypothesis is fully compatible with pytest. You can use the @given decorator within your existing pytest suites to add automated data generation to your tests.

Can I use this with Polars DataFrames?

Yes, this skill includes specific patterns for Polars integration, allowing you to generate parametric DataFrames with defined schemas, null probabilities, and constraints for testing data pipelines.

How does Hypothesis find edge cases?

Hypothesis generates diverse inputs based on defined strategies and, when a failure is found, 'shrinks' the input to the simplest possible version that still triggers the error, making debugging much easier.

What is property-based testing?

Property-based testing verifies that certain properties or invariants of your code always hold true across a wide range of automatically generated inputs, rather than testing only specific hardcoded examples.

Hypothesis Property-Based Testing

Name: Hypothesis Property-Based Testing
Author: anam-org

byanam-org

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보안 및 테스팅

Automates edge-case discovery and test data generation using the Hypothesis property-based testing framework.

소개

This skill enables Claude to implement robust property-based testing using the Hypothesis framework, allowing developers to verify code invariants rather than just individual examples. It streamlines the creation of automated test strategies, handles complex data generation for libraries like Polars, and utilizes Hypothesis's powerful 'shrinking' capability to find the simplest possible failing cases. Whether you are testing simple functions, complex stateful systems, or large-scale data pipelines, this skill provides the patterns and best practices needed to write more reliable and resilient Python code.

주요 기능

Stateful testing via rule-based state machines
Support for custom composite strategies and type-based generation
Automatic test data generation using customizable strategies
Deep integration with Polars for parametric DataFrame testing
20 GitHub stars
Minimal failing example shrinking for simplified debugging

사용 사례

Testing complex system behaviors through rule-based state machine simulations
Verifying data integrity and schema invariants in Polars data pipelines
Discovering hidden edge cases in mathematical or logical functions

소개

주요 기능

Stateful testing via rule-based state machines
Support for custom composite strategies and type-based generation
Automatic test data generation using customizable strategies
Deep integration with Polars for parametric DataFrame testing
20 GitHub stars
Minimal failing example shrinking for simplified debugging

사용 사례

Testing complex system behaviors through rule-based state machine simulations
Verifying data integrity and schema invariants in Polars data pipelines
Discovering hidden edge cases in mathematical or logical functions