Alperen Yildiz, Sin G Teo, Yiling Lou, Yebo Feng, Chong Wang, Dinil Mon Divakaran. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Software developers often rely on regression testing to ensure that recent changes made to the source code do not introduce bugs. Flaky tests, which non-deterministically pass or fail regardless of any change to the code, can negatively impact the effectiveness of the regression testing. While state-of-the-art is advancing the techniques for test-flakiness detection and mitigation, the community is missing a systematic approach for generating high-quality benchmarks of flaky tests to compare the effectiveness of such techniques. Inspired by the power of mutation testing in evaluating the fault-detection ability of tests, this paper proposes Croissant, a framework for injecting flakiness into the test suites to assess the effectiveness of test-flakiness detection tools in finding these tests. Croissant implements 18 flakiness-inducing mutation operators. We designed these operators to allow controlling the non-determinism involved in flakiness, i.e., making many mutants deterministically pass or fail to observe flaky behavior. Our extensive empirical evaluation of Croissant on the test suites of 15 real-world projects confirms the ability of designed mutation operators to generate high-quality mutants, and their effectiveness in challenging test-flakiness detection tools in revealing flaky tests.