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 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. CCS CONCEPTS• Software and its engineering → Search-based software engineering; Collaboration in software development.

Background: Resolving issues is central to modern agile software development where a software is developed and evolved incrementally through series of issue resolutions. An issue could represent a requirement for a new functionality, a report of a software bug or a description of a project task. Aims: Knowing how long an issue will be resolved is thus important to different stakeholders including end-users, bug reporters, bug triagers, developers and managers. This paper aims to propose a multi-objective search-based approach to estimate the time required for resolving an issue. Methods: Using genetic programming (a meta-heuristic optimization method), we iteratively generate candidate estimate models and search for the optimal model in estimating issue resolution time. The search is guided simultaneously by two objectives: maximizing the accuracy of the estimation model while minimizing its complexity. Results: Our evaluation on 8,260 issues from five large open source projects demonstrate that our approach significantly (p < 0.001) outperforms both the baselines and state-of-the-art techniques. Conclusions: Evolutionary search-based approaches offer an effective alternative to build estimation models for issue resolution time. Using multiple objectives, one for measuring the accuracy and the other for the complexity, helps produce accurate and simple estimation models. Resolving issues is central to modern agile software development where a software is developed and evolved incrementally through series of issue resolutions. An issue could represent a requirement for a new functionality, a report of a software bug or a description of a project task.Aims: Knowing how long an issue will be resolved is thus important to di↵erent stakeholders including end-users, bug reporters, bug triagers, developers and managers. This paper aims to propose a multi-objective search-based approach to estimate the time required for resolving an issue.Methods: Using genetic programming (a meta-heuristic optimization method), we iteratively generate candidate estimate models and search for the optimal model in estimating issue resolution time. The search is guided simultaneously by two objectives: maximizing the accuracy of the estimation model while minimizing its complexity.Results: Our evaluation on 8,260 issues from five large open source projects demonstrate that our approach significantly (p < 0.001) outperforms both the baselines and state-of-the-art techniques.Conclusions: Evolutionary search-based approaches o↵er an e↵ective alternative to build estimation models for issue resolution time. Using multiple objectives, one for measuring the accuracy and the other for the complexity, helps produce accurate and simple estimation models.

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