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

arXiv: https://arxiv.org/abs/2005.13904 · HTML: https://mlsanity.com/paper/2005.13904/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2020.

## Built on by (most cited first, 1 of 1)

- [On the Maintenance and Co-evolution of Agent Plugins: An Empirical Study of Claude Code Plugin Marketplaces](https://mlsanity.com/paper/2608.28497/) (2026)

## Abstract (arXiv)

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.