Context: Flaky tests plague regression testing in Continuous Integration environments by slowing down change releases and wasting testing time and effort. Despite the growing interest in mitigating the burden of test flakiness, how to efficiently and effectively detect flaky tests is still an open problem. Objective: In this study, we present and evaluate FLAST, an approach designed to statically predict test flakiness. FLAST leverages vector-space modeling, similarity search, dimensionality reduction, and k -Nearest Neighbor classification in order to timely and efficiently detect test flakiness. Method: In order to gain insights into the efficiency and effectiveness of FLAST, we conduct an empirical evaluation of the approach by considering 13 real-world projects, for a total of 1,383 flaky and 26,702 non-flaky tests. We carry out a quantitative comparison of FLAST with the state-of-the-art methods to detect test flakiness, by considering a balanced dataset comprising 1,402 real-world flaky and as many non-flaky tests. Results: From the results we observe that the effectiveness of FLAST is comparable with the state-of-the-art, while providing considerable gains in terms of efficiency. In addition, the results demonstrate how by tuning the threshold of the approach FLAST can be made more conservative, so to reduce false positives, at the cost of missing more potentially flaky tests. Conclusion: The collected results demonstrate that FLAST provides a fast, low-cost and reliable approach that can be used to guide test rerunning, or to gate the inclusion of new potentially flaky tests.

Know You Neighbor: Fast Static Prediction of Test Flakiness / Verdecchia R.; Cruciani E.; Miranda B.; Bertolino A.. - In: IEEE ACCESS. - ISSN 2169-3536. - ELETTRONICO. - 9:(2021), pp. 9437181.76119-9437181.76134. [10.1109/ACCESS.2021.3082424]

Know You Neighbor: Fast Static Prediction of Test Flakiness

Verdecchia R.;Cruciani E.;
2021

Abstract

Context: Flaky tests plague regression testing in Continuous Integration environments by slowing down change releases and wasting testing time and effort. Despite the growing interest in mitigating the burden of test flakiness, how to efficiently and effectively detect flaky tests is still an open problem. Objective: In this study, we present and evaluate FLAST, an approach designed to statically predict test flakiness. FLAST leverages vector-space modeling, similarity search, dimensionality reduction, and k -Nearest Neighbor classification in order to timely and efficiently detect test flakiness. Method: In order to gain insights into the efficiency and effectiveness of FLAST, we conduct an empirical evaluation of the approach by considering 13 real-world projects, for a total of 1,383 flaky and 26,702 non-flaky tests. We carry out a quantitative comparison of FLAST with the state-of-the-art methods to detect test flakiness, by considering a balanced dataset comprising 1,402 real-world flaky and as many non-flaky tests. Results: From the results we observe that the effectiveness of FLAST is comparable with the state-of-the-art, while providing considerable gains in terms of efficiency. In addition, the results demonstrate how by tuning the threshold of the approach FLAST can be made more conservative, so to reduce false positives, at the cost of missing more potentially flaky tests. Conclusion: The collected results demonstrate that FLAST provides a fast, low-cost and reliable approach that can be used to guide test rerunning, or to gate the inclusion of new potentially flaky tests.
2021
9
76119
76134
Verdecchia R.; Cruciani E.; Miranda B.; Bertolino A.
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1346425
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 34
  • ???jsp.display-item.citation.isi??? 18
social impact