python-testing-patterns

What problem does it solve?

This Skill equips you with comprehensive strategies for testing Python code, addressing challenges like ensuring code quality, preventing regressions, and efficiently verifying functionality. It covers unit, integration, and property-based testing, along with advanced techniques like mocking and async code testing, ensuring your applications are robust and reliable.

Core Features & Use Cases

• pytest Framework: Write clear, concise tests using pytest, fixtures, and parameterization.
• Mocking External Dependencies: Isolate your code for unit testing using unittest.mock.
• Testing Async Code: Confidently test asynchronous functions and applications.
• Property-Based Testing: Use Hypothesis to find edge cases traditional tests miss.
• Use Case: Set up a robust test suite for a new Python library, ensuring every component is thoroughly tested, external services are mocked, and the code is resilient to unexpected inputs.

Quick Start

Example: Basic pytest Test

def add(a, b): return a + b

def test_add(): """Basic test example.""" result = add(2, 3) assert result == 5

def test_add_negative(): """Test with negative numbers.""" assert add(-1, 1) == 0