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Workload-aware reviewer recommendation using a multi-objective search-based approach

Reviewer recommendation approaches have been proposed to provide automated support in finding suitable reviewers to review a given patch. However, they mainly focused on reviewer experience, and did not take into account the review workload, which is another important factor for a reviewer to decide if they will accept a review invitation. We set out to empirically investigate the feasibility of automatically recommending reviewers while considering the review workload amongst other factors. We develop a novel approach that leverages a multi-objective meta-heuristic algorithm to search for reviewers guided by two objectives, i.e., (1) maximizing the chance of participating in a review, and (2) minimizing the skewness of the review workload distribution among reviewers. Through an empirical study of 230,090 patches with 7,431 reviewers spread across four open source projects, we find that our approach can recommend reviewers who are potentially suitable for a newly-submitted patch with 19% - 260% higher F-measure than the five benchmarks. Our empirical results demonstrate that the review workload and other important information should be taken into consideration in find-ing reviewers who are potentially suitable for a newly-submitted patch. In addition, the results show the effectiveness of realizing this approach using a multi-objective search-based approach.