Suggesting reviewers of software artifacts using traceability graphs

Abstract

During the lifecycle of a software project, software artifacts constantly change. A change should be peer-reviewed to ensure the software quality. To maximize the benefit of review, the reviewer(s) should be chosen appropriately. However, choosing the right reviewer(s) might not be trivial especially in large projects. Researchers developed different methods to recommend reviewers. In this study, we introduce a novel approach for reviewer recommendation problem. Our approach utilizes the traceability graph of a software project and assigns a know-about score to each developer, then recommends the developers who have the maximum know-about score for an artifact. We tested our approach on an open source project and achieved top-3 recall of 0.85 with an MRR (mean reciprocal ranking) of 0.73.