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Developer turnover is inevitable on software projects and leads to knowledgeloss, a reduction in productivity, and an increase in defects. Mitigationstrategies to deal with turnover tend to disrupt and increase workloads fordevelopers. In this work, we suggest that through code review recommendation wecan distribute knowledge and mitigate turnover while more evenly distributingreview workload.  We conduct historical analyses to understand the natural concentration ofreview workload and the degree of knowledge spreading that is inherent in codereview. Even though review workload is highly concentrated, we show that codereview natural spreads knowledge thereby reducing the files at risk toturnover.  Using simulation, we evaluate existing code review recommenders and developnovel recommenders to understand their impact on the level of expertise duringreview, the workload of reviewers, and the files at risk to turnover. Oursimulations use seeded random replacement of reviewers to allow us to comparethe reviewer recommenders without the confounding variation of differentreviewers being replaced for each recommender.  Combining recommenders, we develop the SofiaWL recommender that suggestsexperts with low active review workload when none of the files under review areknown by only one developer. In contrast, when knowledge is concentrated on onedeveloper, it sends the review to other reviewers to spread knowledge. For theprojects we study, we are able to globally increase expertise during reviews,+3%, reduce workload concentration, -12%, and reduce the files at risk, -28%.We make our scripts and data available in our replication package. Developerscan optimize for a particular outcome measure based on the needs of theirproject, or use our GitHub bot to automatically balance the outcomes.

Factoring Expertise, Workload, and Turnover into Code Review Recommendation