coverage-analysis

To prioritize untested code, run pytest with coverage support to generate a report identifying uncovered lines and branches. This analysis highlights high-risk coverage gaps to guide test planning and risk reduction efforts.

Identifying high-risk coverage gaps involves analyzing coverage reports to highlight untested code paths. By interpreting uncovered line and branch data, you can prioritize tests based on risk and edge cases to improve quality.

Yes, coverage analysis applies to software projects of various sizes, specifically focusing on Python codebases. It interprets coverage gap data to provide actionable recommendations for risk-informed test planning across different project scales.

Generate a coverage report by running pytest with the coverage flags, such as --cov and --cov-report=term-missing. This produces output detailing uncovered lines and branches to guide your test planning.

You need a test runner with coverage support, such as pytest with the coverage.py plugin, to generate the necessary coverage data. The analysis then interprets this uncovered line and branch data to recommend actionable test planning.

Use coverage analysis when you need to reduce risk and improve quality by identifying untested code paths. It guides teams on where to focus testing efforts by prioritizing tests based on uncovered lines and branches.