  Context: Software code reviews are an important part of the development
process, leading to better software quality and reduced overall costs. However,
finding appropriate code reviewers is a complex and time-consuming task. Goals:
In this paper, we propose a large-scale study to compare performance of two
main source code reviewer recommendation algorithms (RevFinder and a Naive
Bayes-based approach) in identifying the best code reviewers for opened pull
requests. Method: We mined data from Github and Gerrit repositories, building a
large dataset of 51 projects, with more than 293K pull requests analyzed, 180K
owners and 157K reviewers. Results: Based on the large analysis, we can state
that i) no model can be generalized as best for all projects, ii) the usage of
a different repository (Gerrit, GitHub) can have impact on the the
recommendation results, iii) exploiting sub-projects information available in
Gerrit can improve the recommendation results.
