Predicting Test Flakiness
Effective machine learning predictions rely on both static and dynamic features extracted from the source code and test suite. Static features, such as the complexity of the abstract syntax tree, can indicate potential flakiness, while dynamic features like memory overhead and code coverage provide real-time insights. By combining these elements, a robust model can be built to identify flaky tests with greater accuracy.In this clip
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SE Radio 572: Gregory Kapfhammer on Flaky Tests
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