# Mining Code Review Data to Understand Waiting Times Between Acceptance and Merging: An Empirical Analysis

arXiv: https://arxiv.org/abs/2203.05048 · HTML: https://mlsanity.com/paper/2203.05048/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2022.

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

- [Predicting the First Response Latency of Maintainers and Contributors in Pull Requests](https://mlsanity.com/paper/2311.07786/) (2023)
- [Knowledge-Guided Prompt Learning for Request Quality Assurance in Public Code Review](https://mlsanity.com/paper/2410.21673/) (2024)
- [Improving Automated Secure Code Reviews: A Synthetic Dataset for Code Vulnerability Flaws](https://mlsanity.com/paper/2504.16310/) (2025)
- [Using Agentic AI for contextualized and multifaceted code review at Ericsson](https://mlsanity.com/paper/2609.15877/) (2026)

## Abstract (arXiv)

Increasing code velocity (or the speed with which code changes are reviewed and merged) is integral to speeding up development and contributes to the work satisfaction of engineers. While factors affecting code change acceptance have been investigated in the past, solutions to decrease the code review lifetime are less understood. This study investigates the code review process to quantify delays and investigate opportunities to potentially increase code velocity. We study the temporal characteristics of half a million code reviews hosted on Gerrit and Phabricator, starting from the first response, to a decision to accept or reject the changes, and until the changes are merged into a target branch. We identified two types of time delays: (a) the wait time from the proposal of code changes until first response, and (b) the wait time between acceptance and merging. Our study indicates that reducing the time between acceptance and merging has the potential to speed up Phabricator code reviews by 29-63%. Small code changes and changes made by authors with a large number of previously accepted code reviews have a higher chance of being immediately accepted, without code review iterations. Our analysis suggests that switching from manual to automatic merges can help increase code velocity.