# Learning How to Mutate Source Code from Bug-Fixes

arXiv: https://arxiv.org/abs/1812.10772 · HTML: https://mlsanity.com/paper/1812.10772/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Dec 2018.

## Built on by (most cited first, 13 of 13)

- [Large Language Models of Code Fail at Completing Code with Potential Bugs](https://mlsanity.com/paper/2306.03438/) (2023)
- [A Survey on Machine Learning Techniques for Source Code Analysis](https://mlsanity.com/paper/2110.09610/) (2021)
- [CrossCodeBench: Benchmarking Cross-Task Generalization of Source Code Models](https://mlsanity.com/paper/2302.04030/) (2023)
- [Benchmarking Causal Study to Interpret Large Language Models for Source Code](https://mlsanity.com/paper/2308.12415/) (2023)
- [Toward a Theory of Causation for Interpreting Neural Code Models](https://mlsanity.com/paper/2302.03788/) (2023)
- [Improving Automated Program Repair with Domain Adaptation](https://mlsanity.com/paper/2212.11414/) (2022)
- [Can we learn from developer mistakes? Learning to localize and repair real bugs from real bug fixes](https://mlsanity.com/paper/2207.00301/) (2022)
- [Challenging Bug Prediction and Repair Models with Synthetic Bugs](https://mlsanity.com/paper/2310.02407/) (2023)
- [Jointly Learning to Repair Code and Generate Commit Message](https://mlsanity.com/paper/2109.12296/) (2021)
- [Data-Driven AI Model Signal-Awareness Enhancement and Introspection](https://mlsanity.com/paper/2111.05827/) (2021)
- [Advancing Language Models for Code-related Tasks](https://mlsanity.com/paper/2601.04526/) (2026)
- [Towards Comprehensive Benchmarking Infrastructure for LLMs In Software Engineering](https://mlsanity.com/paper/2601.21070/) (2026)
- [Phoenix: Safe GitHub Issue Resolution via Multi-Agent LLMs](https://mlsanity.com/paper/2606.20243/) (2026)

## Abstract (arXiv)

Mutation testing has been widely accepted as an approach to guide test case generation or to assess the effectiveness of test suites. Empirical studies have shown that mutants are representative of real faults; yet they also indicated a clear need for better, possibly customized, mutation operators and strategies. While methods to devise domain-specific or general-purpose mutation operators from real faults exist, they are effort- and error-prone, and do not help the tester to decide whether and how to mutate a given source code element. We propose a novel approach to automatically learn mutants from faults in real programs. First, our approach processes bug fixing changes using fine-grained differencing, code abstraction, and change clustering. Then, it learns mutation models using a deep learning strategy. We have trained and evaluated our technique on a set of ~787k bug fixes mined from GitHub. Our empirical evaluation showed that our models are able to predict mutants that resemble the actual fixed bugs in between 9% and 45% of the cases, and over 98% of the automatically generated mutants are lexically and syntactically correct.