# CORRECT: Code Reviewer Recommendation at GitHub for Vendasta Technologies

arXiv: https://arxiv.org/abs/1807.04130 · HTML: https://mlsanity.com/paper/1807.04130/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2018.

## Built on by (most cited first, 2 of 2)

- [Team-related Features in Code Review Prediction Models](https://mlsanity.com/paper/2312.06244/) (2023)
- [Fine-Tuning Multilingual Language Models for Code Review: An Empirical Study on Industrial C# Projects](https://mlsanity.com/paper/2507.19271/) (2025)

## Abstract (arXiv)

Peer code review locates common coding standard violations and simple logical errors in the early phases of software development, and thus, reduces overall cost. Unfortunately, at GitHub, identifying an appropriate code reviewer for a pull request is challenging given that reliable information for reviewer identification is often not readily available. In this paper, we propose a code reviewer recommendation tool--CORRECT--that considers not only the relevant cross-project work experience (e.g., external library experience) of a developer but also her experience in certain specialized technologies (e.g., Google App Engine) associated with a pull request for determining her expertise as a potential code reviewer. We design our tool using client-server architecture, and then package the solution as a Google Chrome plug-in. Once the developer initiates a new pull request at GitHub, our tool automatically analyzes the request, mines two relevant histories, and then returns a ranked list of appropriate code reviewers for the request within the browser's context. Demo: https://www.youtube.com/watch?v=rXU1wTD6QQ0