A Full-fledged Commit Message Quality Checker Based on Machine Learning

TL;DR

This paper presents an open-source machine learning framework that automatically assesses commit message quality based on established guidelines, improving software maintenance and developer practices.

Contribution

It introduces a comprehensive, practical tool that evaluates commit messages against quality rules using state-of-the-art machine learning models, filling a gap in existing tools.

Findings

Achieved a lowest F1 score of 82.9% on challenging tasks

Developed a full framework for commit message quality checking

Supports research and practical software development improvements

Abstract

Commit messages (CMs) are an essential part of version control. By providing important context in regard to what has changed and why, they strongly support software maintenance and evolution. But writing good CMs is difficult and often neglected by developers. So far, there is no tool suitable for practice that automatically assesses how well a CM is written, including its meaning and context. Since this task is challenging, we ask the research question: how well can the CM quality, including semantics and context, be measured with machine learning methods? By considering all rules from the most popular CM quality guideline, creating datasets for those rules, and training and evaluating state-of-the-art machine learning models to check those rules, we can answer the research question with: sufficiently well for practice, with the lowest F$_1$ score of 82.9\%, for the most challenging task. We develop a full-fledged open-source framework that checks all these CM quality rules. It is useful for research, e.g., automatic CM generation, but most importantly for software practitioners to raise the quality of CMs and thus the maintainability and evolution speed of their software.