TheBundle Testing Module Guide

This document an overview of the bundle.testing module, focusing on its advanced decorator-based approach for robust, maintainable, and scalable testing in Python projects.

Key Features

  • Decorator-first design: Powerful decorators for profiling, data validation, and test orchestration, minimizing boilerplate and maximizing clarity.

  • Integrated profiling with cProfile: The @cprofile decorator enables per-test performance profiling, automatic stats dumping, and threshold-based warnings.

  • Tracy profiler integration: Run tests with --tracy for nanosecond-resolution, real-time profiling via Tracy. Use --tracy --report to auto-generate PDF performance reports from captured data.

  • Automated data validation: The @data decorator orchestrates round-trip serialization, schema validation, and reference management for Pydantic models.

  • Async-first support: All decorators and test flows are designed for asynchronous code, with seamless integration into pytest-asyncio.

  • Reference and artifact management: Automatic creation and management of reference data, temporary directories, and profiling outputs.

  • Pytest marker integration: Use @pytest.mark.bundle_cprofile and @pytest.mark.bundle_data to declaratively apply decorators and pass parameters from your test suite.

  • Comprehensive error logging: All test steps are logged with detailed context, and errors are captured and written to disk for post-mortem analysis.

  • Minimal test code: Write concise tests that focus on what matters—your data and logic—while the decorators handle the rest.