Using Source Code Density to Improve the Accuracy of Automatic Commit Classification into Maintenance Activities

TL;DR

This paper introduces source code density as a new feature to enhance the accuracy of automatic commit classification, demonstrating significant improvements across multiple projects and scenarios.

Contribution

It proposes source code density as a novel feature and explores how previous commits' code density influences classification accuracy.

Findings

Achieved up to 89% accuracy in cross-project classification.

Improved classification accuracy by incorporating code density.

Models trained on single projects reached up to 93% accuracy.

Abstract

Source code is changed for a reason, e.g., to adapt, correct, or adapt it. This reason can provide valuable insight into the development process but is rarely explicitly documented when the change is committed to a source code repository. Automatic commit classification uses features extracted from commits to estimate this reason. We introduce source code density, a measure of the net size of a commit, and show how it improves the accuracy of automatic commit classification compared to previous size-based classifications. We also investigate how preceding generations of commits affect the class of a commit, and whether taking the code density of previous commits into account can improve the accuracy further. We achieve up to 89% accuracy and a Kappa of 0.82 for the cross-project commit classification where the model is trained on one project and applied to other projects. Models trained on single projects yield accuracies of up to 93% with a Kappa approaching 0.90. The accuracy of the automatic commit classification has a direct impact on software (process) quality analyses that exploit the classification, so our improvements to the accuracy will also improve the confidence in such analyses.