  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.
  Combining recommenders, we develop the SofiaWL recommender 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. 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.
