Developer turnover is inevitable on software projects and leads to knowledge loss, a reduction in productivity, and an increase in defects. Mitigation strategies to deal with turnover tend to disrupt and increase workloads for developers. In this work, we suggest that through code review recommendation we can distribute knowledge and mitigate turnover while more evenly distributing review workload. We conduct historical analyses to understand the natural concentration of review workload and the degree of knowledge spreading that is inherent in code review. Even though review workload is highly concentrated, we show that code review natural spreads knowledge thereby reducing the files at risk to turnover. Using simulation, we evaluate existing code review recommenders and develop novel recommenders to understand their impact on the level of expertise during review, the workload of reviewers, and the files at risk to turnover. Our simulations use seeded random replacement of reviewers to allow us to compare the reviewer recommenders without the confounding variation of different reviewers being replaced for each recommender. We find that prior work that assigns reviewers based on file ownership concentrates knowledge on a small group of core developers increasing the risk of knowledge loss from turnover. Recent work, WhoDo, that considers developer workload, assigns developers that are not sufficiently committed to the project and we see an increase in files at risk to turnover. We propose learning and retention aware review recommenders that when combined are effective at reducing the risk of turnover, but they unacceptably reduce the overall expertise during reviews. Combining recommenders, we develop theSofiaWLrecommender that suggests experts with low active review workload when none of the files under review are known by only one developer. In contrast, when knowledge is concentrated on one developer, it sends the review to other reviewers to spread knowledge. For the projects 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[1]. Developers can optimize for a particular outcome measure based on the needs of their project, or use our GitHub bot to automatically balance the outcomes[2].

Developer turnover is inevitable on software projects and leads to knowledge loss, a reduction in productivity, and an increase in defects. Mitigation strategies to deal with turnover tend to disrupt and increase workloads for developers. In this work, we suggest that through code review recommendation we can distribute knowledge and mitigate turnover with minimal impacton the development process. We evaluate review recommenders in the context of ensuring expertise during review, Expertise, reducing the review workload of the core team, CoreWorkload, and reducing the Files at Risk to turnover, FaR. We find that prior work that assigns reviewers based on file ownership concentrates knowledge on a small group of core developers increasing risk of knowledge loss from turnover by up to 65%. We propose learning and retention aware review recommenders that when combined are effective at reducing the risk of turnover by -29% but they unacceptably reduce the overall expertise during reviews by -26%. We develop the Sofia recommender that suggests experts when none of the files under review are hoarded by developers, but distributes knowledge when files are at risk. In this way, we are able to simultaneously increase expertise during review with a ΔExpertise of 6%, with a negligible impact on workload of ΔCoreWorkload of 0.09%, and reduce the files at risk by ΔFaR -28%. Sofia is integrated into GitHub pull requests allowing developers to select an appropriate expert or "learner" based on the context of the review. We release the Sofia bot as well as the code and data for replication purposes.