Using Machine Intelligence to Prioritise Code Review Requests

TL;DR

This paper presents Pineapple, a Bayesian Network-based tool tested in an Ericsson case study, which effectively prioritizes code review requests, improves review efficiency, and is well-received by developers.

Contribution

It introduces a novel Bayesian Network approach for prioritizing code reviews and validates its effectiveness in a real industrial setting.

Findings

Pineapple achieved RMSE = 0.21 and MAE = 0.15 in predictions.

82.6% of users found Pineapple useful for prioritization.

56.5% of users reported reduced code review lead time.

Abstract

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.