# Untangling Fine-Grained Code Changes

arXiv: https://arxiv.org/abs/1502.06757 · HTML: https://mlsanity.com/paper/1502.06757/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2015.

## Built on by (most cited first, 10 of 10)

- [RACE: Retrieval-Augmented Commit Message Generation](https://mlsanity.com/paper/2203.02700/) (2022)
- [Are Coding Agents Generating Over-Mocked Tests? An Empirical Study](https://mlsanity.com/paper/2602.00409/) (2026)
- [CCRep: Learning Code Change Representations via Pre-Trained Code Model and Query Back](https://mlsanity.com/paper/2302.03924/) (2023)
- [LLM-Driven Collaborative Model for Untangling Commits via Explicit and Implicit Dependency Reasoning](https://mlsanity.com/paper/2507.16395/) (2025)
- [CodeFuse-CommitEval: Towards Benchmarking LLM's Power on Commit Message and Code Change Inconsistency Detection](https://mlsanity.com/paper/2511.19875/) (2025)
- [Rethinking Code Review in the Age of AI: A Vision for Agentic Code Review](https://mlsanity.com/paper/2605.17548/) (2026)
- ["Refactoring Runaway": Understanding and Mitigating Tangled Refactorings in Coding Agents for Issue Resolution](https://mlsanity.com/paper/2605.22526/) (2026)
- [SemaDiff: Identifying Semantic-Changing Commits with Generated Code and Tests](https://mlsanity.com/paper/2607.13111/) (2026)
- [ActiveClean: Generating Line-Level Vulnerability Data via Active Learning](https://mlsanity.com/paper/2312.01588/) (2023)
- [DepRadar: Agentic Coordination for Context Aware Defect Impact Analysis in Deep Learning Libraries](https://mlsanity.com/paper/2601.09440/) (2026)

## Abstract (arXiv)

After working for some time, developers commit their code changes to a version control system. When doing so, they often bundle unrelated changes (e.g., bug fix and refactoring) in a single commit, thus creating a so-called tangled commit. Sharing tangled commits is problematic because it makes review, reversion, and integration of these commits harder and historical analyses of the project less reliable. Researchers have worked at untangling existing commits, i.e., finding which part of a commit relates to which task. In this paper, we contribute to this line of work in two ways: (1) A publicly available dataset of untangled code changes, created with the help of two developers who accurately split their code changes into self contained tasks over a period of four months; (2) a novel approach, EpiceaUntangler, to help developers share untangled commits (aka. atomic commits) by using fine-grained code change information. EpiceaUntangler is based and tested on the publicly available dataset, and further evaluated by deploying it to 7 developers, who used it for 2 weeks. We recorded a median success rate of 91% and average one of 75%, in automatically creating clusters of untangled fine-grained code changes.