Predicting Code Review Completion Time in Modern Code Review

TL;DR

This paper presents a machine learning framework to predict the time required for completing code reviews in Modern Code Review, aiming to assist developers in managing review tasks more effectively.

Contribution

It introduces a regression-based approach to accurately estimate code review completion times and identify key influencing factors.

Findings

Effective prediction of review times achieved

Key socio-technical factors influencing review duration identified

Framework supports better review task management

Abstract

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.