Title: CORRECT: Code Reviewer Recommendation in GitHub Based on Cross-Project and Technology Experience,
Abstract: Peer code review locates common coding rule violations and simple logical
errors in the early phases of software development, and thus reduces overall
cost. However, in GitHub, identifying an appropriate code reviewer for a pull
request is a non-trivial task given that reliable information for reviewer
identification is often not readily available. In this paper, we propose a code
reviewer recommendation technique that considers not only the relevant
cross-project work history (e.g., external library experience) but also the
experience of a developer in certain specialized technologies associated with a
pull request for determining her expertise as a potential code reviewer. We
first motivate our technique using an exploratory study with 10 commercial
projects and 10 associated libraries external to those projects. Experiments
using 17,115 pull requests from 10 commercial projects and six open source
projects show that our technique provides 85%--92% recommendation accuracy,
about 86% precision and 79%--81% recall in code reviewer recommendation, which
are highly promising. Comparison with the state-of-the-art technique also
validates the empirical findings and the superiority of our recommendation
technique.