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Numrering	Referens	Omslagsbild	Hitta
1.	Al Sabbagh, Khaled, 1987, et al. (författare) Predicting Test Case Verdicts Using TextualAnalysis of Commited Code Churns 2019 Ingår i: CEUR Workshop Proceedings. - 1613-0073. ; 2476, s. 138-153 Konferensbidrag (refereegranskat)abstract Background: Continuous Integration (CI) is an agile software development practice that involves producing several clean builds of the software per day. The creation of these builds involve running excessive executions of automated tests, which is hampered by high hardware cost and reduced development velocity. Goal: The goal of our research is to develop a method that reduces the number of executed test cases at each CI cycle.Method: We adopt a design research approach with an infrastructure provider company to develop a method that exploits Ma-chine Learning (ML) to predict test case verdicts for committed sourcecode. We train five different ML models on two data sets and evaluate their performance using two simple retrieval measures: precision and recall. Results: While the results from training the ML models on the first data-set of test executions revealed low performance, the curated data-set for training showed an improvement on performance with respect to precision and recall. Conclusion: Our results indicate that the method is applicable when training the ML model on churns of small sizes

Al Sabbagh, Khaled, 1987, et al. (författare)
Predicting Test Case Verdicts Using TextualAnalysis of Commited Code Churns
2019
Ingår i: CEUR Workshop Proceedings. - 1613-0073. ; 2476, s. 138-153
Konferensbidrag (refereegranskat)abstract
- Background: Continuous Integration (CI) is an agile software development practice that involves producing several clean builds of the software per day. The creation of these builds involve running excessive executions of automated tests, which is hampered by high hardware cost and reduced development velocity. Goal: The goal of our research is to develop a method that reduces the number of executed test cases at each CI cycle.Method: We adopt a design research approach with an infrastructure provider company to develop a method that exploits Ma-chine Learning (ML) to predict test case verdicts for committed sourcecode. We train five different ML models on two data sets and evaluate their performance using two simple retrieval measures: precision and recall. Results: While the results from training the ML models on the first data-set of test executions revealed low performance, the curated data-set for training showed an improvement on performance with respect to precision and recall. Conclusion: Our results indicate that the method is applicable when training the ML model on churns of small sizes

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