Workload-Aware Reviewer Recommendation using a Multi-Objective Search-based Approach
Background: 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. Aim: We set out to empirically investigate the feasibility of automatically recommending reviewers while considering the review workload amongst other factors. Method: 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.Results: 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. Conclusion: Our empirical results demonstrate that the review workload and other important information should be taken into consideration in finding 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.
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Evaluating Hyper-Parameter Tuning using Random Search in Support Vector Machines for Software Effort Estimation