# A Fine-grained Data Set and Analysis of Tangling in Bug Fixing Commits

arXiv: https://arxiv.org/abs/2011.06244 · HTML: https://mlsanity.com/paper/2011.06244/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2020.

## Built on by (most cited first, 11 of 11)

- [End to End Software Engineering Research](https://mlsanity.com/paper/2112.11858/) (2021)
- [LLM-Driven Collaborative Model for Untangling Commits via Explicit and Implicit Dependency Reasoning](https://mlsanity.com/paper/2507.16395/) (2025)
- [AtomicCommitBench: Can Coding Agents Reconstruct Commit Histories from Squashed Patches?](https://mlsanity.com/paper/2607.03332/) (2026)
- [Just-In-Time Software Defect Prediction via Bi-modal Change Representation Learning](https://mlsanity.com/paper/2410.12107/) (2024)
- [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)
- [A Large Scale Survey of Motivation in Software Development and Analysis of its Validity](https://mlsanity.com/paper/2404.08303/) (2024)
- [CodeFlowLM: Incremental Just-In-Time Defect Prediction with Pretrained Language Models and Exploratory Insights into Defect Localization](https://mlsanity.com/paper/2512.00231/) (2025)
- [DepRadar: Agentic Coordination for Context Aware Defect Impact Analysis in Deep Learning Libraries](https://mlsanity.com/paper/2601.09440/) (2026)
- [Which Alert Removals are Beneficial?](https://mlsanity.com/paper/2603.21322/) (2026)

## Abstract (arXiv)

Context: Tangled commits are changes to software that address multiple concerns at once. For researchers interested in bugs, tangled commits mean that they actually study not only bugs, but also other concerns irrelevant for the study of bugs. Objective: We want to improve our understanding of the prevalence of tangling and the types of changes that are tangled within bug fixing commits. Methods: We use a crowd sourcing approach for manual labeling to validate which changes contribute to bug fixes for each line in bug fixing commits. Each line is labeled by four participants. If at least three participants agree on the same label, we have consensus. Results: We estimate that between 17% and 32% of all changes in bug fixing commits modify the source code to fix the underlying problem. However, when we only consider changes to the production code files this ratio increases to 66% to 87%. We find that about 11% of lines are hard to label leading to active disagreements between participants. Due to confirmed tangling and the uncertainty in our data, we estimate that 3% to 47% of data is noisy without manual untangling, depending on the use case. Conclusion: Tangled commits have a high prevalence in bug fixes and can lead to a large amount of noise in the data. Prior research indicates that this noise may alter results. As researchers, we should be skeptics and assume that unvalidated data is likely very noisy, until proven otherwise.