# Using Machine Intelligence to Prioritise Code Review Requests

arXiv: https://arxiv.org/abs/2102.05916 · HTML: https://mlsanity.com/paper/2102.05916/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 43 references that are in the map.
- First posted on arXiv 11 Feb 2021.

## Builds on (most cited first, 1 of 1)

- [The Impact of Human Factors on the Participation Decision of Reviewers in Modern Code Review](https://mlsanity.com/paper/1806.10277/) (2018)

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

- [Predicting Code Review Completion Time in Modern Code Review](https://mlsanity.com/paper/2109.15141/) (2021)
- [On The Impact of Merge Request Deviations on Code Review Practices](https://mlsanity.com/paper/2506.08860/) (2025)

## Abstract (arXiv)

Modern Code Review (MCR) is the process of reviewing new code changes that need to be merged with an existing codebase. As a developer, one may receive many code review requests every day, i.e., the review requests need to be prioritised. Manually prioritising review requests is a challenging and time-consuming process. To address the above problem, we conducted an industrial case study at Ericsson aiming at developing a tool called Pineapple, which uses a Bayesian Network to prioritise code review requests. To validate our approach/tool, we deployed it in a live software development project at Ericsson, wherein more than 150 developers develop a telecommunication product. We focused on evaluating the predictive performance, feasibility, and usefulness of our approach. The results indicate that Pineapple has competent predictive performance (RMSE = 0.21 and MAE = 0.15). Furthermore, around 82.6% of Pineapple's users believe the tool can support code review request prioritisation by providing reliable results, and around 56.5% of the users believe it helps reducing code review lead time. As future work, we plan to evaluate Pineapple's predictive performance, usefulness, and feasibility through a longitudinal investigation.