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Empirically evaluating flaky test detection techniques combining test case rerunning and machine learning models.

, , , and . Empir. Softw. Eng., 28 (3): 72 (June 2023)

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Empirically evaluating flaky test detection techniques combining test case rerunning and machine learning models., , , and . Empir. Softw. Eng., 28 (3): 72 (June 2023)A genetic algorithm to improve linux kernel performance on resource-constrained devices., , and . GECCO (Companion), page 2095-2096. ACM, (2010)Database-aware test coverage monitoring., and . ISEC, page 77-86. ACM, (2008)An examination of the run-time performance of GUI creation frameworks., , and . PPPJ, volume 42 of ACM International Conference Proceeding Series, page 171-176. ACM, (2003)Empirically studying the role of selection operators duringsearch-based test suite prioritization., , and . GECCO, page 1373-1380. ACM, (2010)Using controlled numbers of real faults and mutants to empirically evaluate coverage-based test case prioritization., , , and . AST@ICSE, page 57-63. ACM, (2018)Automatically Evaluating the Efficiency of Search-Based Test Data Generation for Relational Database Schemas., , , and . SEKE, page 352-357. KSI Research Inc. and Knowledge Systems Institute Graduate School, (2015)mrstudyr: Retrospectively Studying the Effectiveness of Mutant Reduction Techniques., , and . ICSME, page 591-595. IEEE Computer Society, (2016)Regression Testing.. Encyclopedia of Software Engineering, Taylor & Francis, (2010)Dynamic invariant detection for relational databases., , , and . WODA, page 12-17. ACM, (2011)