Abstract
Continuous Integration (CI) aims at supporting developers in integrating code changes quickly through automated building. However, there is a consensus that CI build failure is a major barrier that developers face, which prevents them from proceeding further with development. In this paper, we introduce BF-Detector, an automated tool to detect CI build failure. Based on the adaptation of Non-dominated Sorting Genetic Algorithm (NSGA-II), our tool aims at finding the best prediction rules based on two conflicting objective functions to deal with both minority and majority classes. We evaluated the effectiveness of our tool on a benchmark of 56,019 CI builds. The results reveal that our technique outperforms state-of-the-art approaches by providing a better balance between both failed and passed builds. BF-Detector tool is publicly available, with a demo video, at: https://github.com/stilab-ets/BF-Detector
Islem Saidani
Ph.D. Candidate at ETS Montreal
Moataz Chouchen
Ph.D. Candidate at ETS Montreal
Mohamed Wiem Mkaouer
Assistant Professor of Software Engineering
Research interests software refactoring and quality.