Semantic unit testing: test code without executing it
a year ago
- #semantic-testing
- #LLM
- #python
- Semantic unit testing evaluates if a function's implementation matches its documented behavior using LLMs.
- The `suite` Python library enables semantic unit testing by analyzing code and docstrings with LLMs.
- Example usage: `tester = suite(model_name="openai/o3-mini")` tests functions like `multiply` for semantic correctness.
- How it works: Extracts function info, dependencies, builds a prompt, and queries an LLM for evaluation.
- Reasons to avoid: LLMs can be unreliable, expensive for large codebases, and traditional tests are more dependable.
- Reasons to use: Broad coverage, catches edge cases early, integrates with pytest, and supports async/local models.
- Conclusion: Semantic testing complements but doesn't replace traditional tests; explore it as a supplementary tool.