<ul><li>Running irrelevant tests wastes time, energy, and resources.</li><li>Test-impact analysis (TIA) helps in skipping irrelevant tests based on code dependencies.</li><li>Automatic TIA methods use static code analysis or run-time coverage to identify dependencies.</li><li>File dependencies, program dependency graphs, and coverage are key methods used in TIA.</li><li>Manual specification of dependencies can also be done for TIA, especially for non-code changes.</li><li>TIA can be complemented with other speed-up methods like test suites, parallelization, and predictive test selection.</li><li>Predictive selection involves choosing tests likely to fail and can be sophisticated using machine learning models.</li><li>Test-impact analysis can be beneficial for speeding up test suites and improving efficiency in testing processes.</li><li>Users are encouraged to try out test-impact analysis tools like spdr for Python to enhance their testing strategies.</li><li>Efficient testing practices like TIA can improve development productivity and reduce unnecessary testing overhead.</li></ul>

You Should Test Less - Skip Irrelevant Tests With Impact Analysis

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