  Code review ensures that a peer engineer manually examines the code before it
is integrated and released into production. At Meta, we develop a wide range of
software at scale, from social networking to software development
infrastructure, such as calendar and meeting tools to continuous integration.
We are constantly improving our code review system, and in this work we
describe a series of experiments that were conducted across 10's of thousands
of engineers and 100's of thousands of reviews.
  We build upon the recommender that has been in production since 2018,
RevRecV1. We found that reviewers were being assigned based on prior authorship
of files. We reviewed the literature for successful features and experimented
with them with RevRecV2 in production. The most important feature in our new
model was the familiarity of the author and reviewer, we saw an overall
improvement in accuracy of 14 percentage points.
  Prior research has shown that reviewer workload is skewed. To balance
workload, we divide the reviewer score from RevRecV2 by each candidate
reviewers workload. We experimented with multiple types of workload to develop
RevRecWL. We find that reranking candidate reviewers by workload often leads to
a reviewers with lower workload being selected by authors.
  The bystander effect can occur when a team of reviewers is assigned the
review. We mitigate the bystander effect by randomly assigning one of the
recommended reviewers. Having an individual who is responsible for the review,
reduces the time take for reviews by -11%.
