# CORRECT: Code Reviewer Recommendation in GitHub Based on Cross-Project and Technology Experience

arXiv: https://arxiv.org/abs/1807.02965 · HTML: https://mlsanity.com/paper/1807.02965/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2018.

## Built on by (most cited first, 4 of 4)

- [Evaluating Large Language Models for Code Review](https://mlsanity.com/paper/2505.20206/) (2025)
- [Benchmarking LLMs for Fine-Grained Code Review with Enriched Context in Practice](https://mlsanity.com/paper/2511.07017/) (2025)
- [Using Large-scale Heterogeneous Graph Representation Learning for Code Review Recommendations at Microsoft](https://mlsanity.com/paper/2202.02385/) (2022)
- [Rethinking Code Review in the Age of AI: A Vision for Agentic Code Review](https://mlsanity.com/paper/2605.17548/) (2026)

## Abstract (arXiv)

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.