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Non-deterministically behaving test cases cause developers to lose trust intheir regression test suites and to eventually ignore failures. Detecting flakytests is therefore a crucial task in maintaining code quality, as it builds thenecessary foundation for any form of systematic response to flakiness, such astest quarantining or automated debugging. Previous research has proposedvarious methods to detect flakiness, but when trying to deploy these in anindustrial context, their reliance on instrumentation, test reruns, orlanguage-specific artifacts was inhibitive. In this paper, we thereforeinvestigate the prediction of flaky tests without such requirements on theunderlying programming language, CI, build or test execution framework.Instead, we rely only on the most commonly available artifacts, namely thetests' outcomes and durations, as well as basic information about the codeevolution to build predictive models capable of detecting flakiness.Furthermore, our approach does not require additional reruns, since it gathersthis data from existing test executions. We trained several establishedclassifiers on the suggested features and evaluated their performance on alarge-scale industrial software system, from which we collected a data set of100 flaky and 100 non-flaky test- and code-histories. The best model was ableto achieve an F1-score of 95.5% using only 3 features: the tests' flip rates,the number of changes to source files in the last 54 days, as well as thenumber of changed files in the most recent pull request.

Practical Flaky Test Prediction using Common Code Evolution and Test History Data