Mining and Modeling Review-Priority Labels in Gerrit for Predictive Review Analytics

Code review is a critical step in modern software development, ensuring quality, preventing defects, and coordinating collaborative work. Large ecosystems such as OpenStack, which is hosted on Gerrit, receive a high volume of code changes every day, making it challenging for reviewers to decide which contributions should be handled first.

In response to this, OpenStack developers use a special review-priority label to indicate which changes are urgent or should be reviewed ahead of others. However, little is known about how consistently this label is applied, how it influences reviewer behavior, or whether it actually helps reduce review delays.

This project aims to study how developers use the review-priority label, understand the patterns behind its application, and explore how AI techniques can support and improve its effectiveness. We will mine OpenStack’s Gerrit data, extract features related to priority signals and review outcomes, and build predictive models that help teams identify when a change should be prioritized. The goal is to provide insights and AI-assisted recommendations that make priority-driven code review more consistent, reliable, and useful in large software ecosystems.

Faculty Supervisor:

Moataz Chouchen

Student:

Partner:

National School of Computer Science (ENSI), Tunisia

Discipline:

Computer science

Sector:

Education

University:

Concordia University

Program: