# A Large-Scale Study on Source Code Reviewer Recommendation

arXiv: https://arxiv.org/abs/1806.07619 · HTML: https://mlsanity.com/paper/1806.07619/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2018.

## Built on by (most cited first, 1 of 1)

- [Using Large-scale Heterogeneous Graph Representation Learning for Code Review Recommendations at Microsoft](https://mlsanity.com/paper/2202.02385/) (2022)

## Abstract (arXiv)

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.