On the prediction of continuous integration build failures using search-based software engineering

Continuous Integration (CI) aims at supporting developers in integrating code changes quickly through automated building. However, in such context, the build process is typically time and resource-consuming. As a response, the use of machine learning (ML) techniques has been proposed to cut the expenses of CI build time by predicting its outcome. Nevertheless, the existing ML-based solutions are challenged by problems related mainly to the imbalanced distribution of successful and failed builds. To deal with this issue, we introduce a novel approach based on Multi-Objective Genetic Programming (MOGP) to build a prediction model. Our approach aims at finding the best prediction rules based on two conflicting objective functions to deal with both minority and majority classes. We evaluated our approach on a benchmark of 15,383 builds. The results reveal that our technique outperforms state-of-the-art approaches by providing a better balance between both failed and passed builds.