# Predicting Code Review Completion Time in Modern Code Review

arXiv: https://arxiv.org/abs/2109.15141 · HTML: https://mlsanity.com/paper/2109.15141/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 49 references that are in the map.
- First posted on arXiv 30 Sep 2021.

## Builds on (most cited first, 8 of 8)

- [Distribution-Free Predictive Inference For Regression](https://mlsanity.com/paper/1604.04173/) (2016)
- [The Impact of Automated Parameter Optimization on Defect Prediction Models](https://mlsanity.com/paper/1801.10270/) (2018)
- [The Impact of Human Factors on the Participation Decision of Reviewers in Modern Code Review](https://mlsanity.com/paper/1806.10277/) (2018)
- [Categorizing Bugs with Social Networks: A Case Study on Four Open Source Software Communities](https://mlsanity.com/paper/1302.6764/) (2013)
- [Refactoring Practices in the Context of Modern Code Review: An Industrial Case Study at Xerox](https://mlsanity.com/paper/2102.05201/) (2021)
- [Using Machine Intelligence to Prioritise Code Review Requests](https://mlsanity.com/paper/2102.05916/) (2021)
- [Code Reviews with Divergent Review Scores: An Empirical Study of the OpenStack and Qt Communities](https://mlsanity.com/paper/2104.01537/) (2021)
- [State of the Practice in Software Effort Estimation: A Survey and Literature Review](https://mlsanity.com/paper/1401.5878/) (2014)

## Abstract (arXiv)

Context. Modern Code Review (MCR) is being adopted in both open source and commercial projects as a common practice. MCR is a widely acknowledged quality assurance practice that allows early detection of defects as well as poor coding practices. It also brings several other benefits such as knowledge sharing, team awareness, and collaboration. Problem. In practice, code reviews can experience significant delays to be completed due to various socio-technical factors which can affect the project quality and cost. For a successful review process, peer reviewers should perform their review tasks in a timely manner while providing relevant feedback about the code change being reviewed. However, there is a lack of tool support to help developers estimating the time required to complete a code review prior to accepting or declining a review request. Objective. Our objective is to build and validate an effective approach to predict the code review completion time in the context of MCR and help developers better manage and prioritize their code review tasks. Method. We formulate the prediction of the code review completion time as a learning problem. In particular, we propose a framework based on regression models to (i) effectively estimate the code review completion time, and (ii) understand the main factors influencing code review completion time.