# Generating Commit Messages from Git Diffs

arXiv: https://arxiv.org/abs/1911.11690 · HTML: https://mlsanity.com/paper/1911.11690/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2019.

## Built on by (most cited first, 5 of 5)

- [CommitBERT: Commit Message Generation Using Pre-Trained Programming Language Model](https://mlsanity.com/paper/2105.14242/) (2021)
- [From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligence](https://mlsanity.com/paper/2511.18538/) (2025)
- [GitGoodBench: A Novel Benchmark For Evaluating Agentic Performance On Git](https://mlsanity.com/paper/2505.22583/) (2025)
- [Unsupervised Learning of General-Purpose Embeddings for Code Changes](https://mlsanity.com/paper/2106.02087/) (2021)
- [COMET: Generating Commit Messages using Delta Graph Context Representation](https://mlsanity.com/paper/2402.01841/) (2024)

## Abstract (arXiv)

Commit messages aid developers in their understanding of a continuously evolving codebase. However, developers not always document code changes properly. Automatically generating commit messages would relieve this burden on developers. Recently, a number of different works have demonstrated the feasibility of using methods from neural machine translation to generate commit messages. This work aims to reproduce a prominent research paper in this field, as well as attempt to improve upon their results by proposing a novel preprocessing technique. A reproduction of the reference neural machine translation model was able to achieve slightly better results on the same dataset. When applying more rigorous preprocessing, however, the performance dropped significantly. This demonstrates the inherent shortcoming of current commit message generation models, which perform well by memorizing certain constructs. Future research directions might include improving diff embeddings and focusing on specific groups of commits.