test-gap-analyzer

To find untested functions in a Python project, compare your source files against coverage data to map execution gaps. This analysis highlights uncovered functions and returns a structured report detailing exactly where test coverage is missing.

Yes, you can automatically generate pytest stubs for uncovered Python code. After detecting untested functions from coverage reports, the tool creates test skeletons for critical gaps, allowing you to quickly close the coverage deficit.

Yes, the gap analyzer works with both pytest and bats test frameworks. It applies to Python projects using either framework to detect untested functions, map coverage data, and prioritize gaps by complexity, size, and risk.

The best way to prioritize test coverage gaps by risk is to rank untested functions based on code complexity and size. This approach optimizes testing effort by targeting critical gaps first, generating a prioritized report for your Python sources.

To analyze Python test coverage gaps, you need a coverage.json file and a source directory. Running the gap detection script with these inputs aligns your source code with coverage data to generate a prioritized gaps report.