CLEVER: Combining Code Metrics with Clone Detection for Just-In-Time Fault Prevention and Resolution in Large Industrial Projects

Automatic prevention and resolution of faults is an important research topic in the field of software maintenance and evolution. Existing approaches leverage code and process metrics to build metric-based models that can effectively prevent defect insertion in a software project. Metrics, however, may vary from one project to another, hindering the reuse of these models. Moreover, they tend to generate high false positive rates by classifying healthy commits as risky. Finally, they do not provide sufficient insights to developers on how to fix the detected risky commits. In this paper, we propose an approach, called CLEVER (Combining Levels of Bug Prevention and Resolution techniques), which relies on a two-phases process for intercepting risky commits before they reach the central repository. CLEVER was developed in collaboration with Ubisoft developers. When applied to 12 Ubisoft systems, the results show that CLEVER can detect risky commits with 79% precision and 65% recall, which outperforms the performance of Commit-guru, a recent approach that was proposed in the literature. In addition, CLEVER is able to recommend qualitative fixes to developers on how to fix risky commits in 66.7% of the cases.


Mathieu Nayrolles (La Forge Research Lab, Ubisoft), Abdelwahab Hamou-Lhadj (ISLM Research Lab, ECE, Concordia)