3min chapter

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SE Radio 572: Gregory Kapfhammer on Flaky Tests

Software Engineering Radio - the podcast for professional software developers

CHAPTER

The Importance of Static Features in Machine Learning

When it comes to a static feature, some of the things that we might extract are related to what's known as the abstract syntax tree or the AST of either the program or the test suite. That would be again an example of what we would call a static feature. A concrete example of a dynamic feature might be things like the memory overhead of a test case and utilization of the file system on behalf of the test. Then for every test, you give it the label from rerunning, which would indicate whether the test case is flaky or is not flaky. You then train a model, like for example, a neural network or a random forest or other kinds of machine learning algorithms.

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