Flakify: A Black-Box, Language Model-based Predictor for Flaky Tests
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Software testing assures that code changes do not adversely affect existing functionality. However, a test case can be flaky, i.e., passing and failing across executions, even for the same version of the source code. Flaky tests introduce overhead to software development as they can lead to unnecessary attempts to debug production or testing code. Besides rerunning test cases multiple times, which is time-consuming and computationally expensive, flaky tests can be predicted using machine learning (ML) models. However, the state-of-the-art ML-based flaky test predictors rely on pre-defined sets of features that are either project-specific, i.e., inapplicable to other projects, or require access to production code, which is not always available to software test engineers. Moreover, given the non-deterministic behavior of flaky tests, it can be challenging to determine a complete set of features that could potentially be associated with test flakiness. Therefore, in this paper, we propose Flakify, a black-box, language model-based predictor for flaky tests. Flakify does not require to (a) rerun test cases, (b) pre-define features, or (c) access to production code. To this end, we employ CodeBERT, a pre-trained language model, and fine-tune it to predict flaky tests by relying exclusively on the source code of test cases. We evaluated Flakify on a publicly available dataset and compared our results with FlakeFlagger, the best state-of-the-art ML-based, white-box predictor for flaky tests. Flakify surpasses FlakeFlagger by 10 and 18 percentage points (pp) in terms of precision and recall, respectively, thus reducing the effort bound to be wasted on unnecessarily debugging test cases and production code by the same percentages, respectively. Our results further show that a black-box version of FlakeFlagger is not a viable option for predicting flaky tests.
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