# Mining Fix Patterns for FindBugs Violations

arXiv: https://arxiv.org/abs/1712.03201 · HTML: https://mlsanity.com/paper/1712.03201/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2017.

## Built on by (most cited first, 9 of 9)

- [VUDENC: Vulnerability Detection with Deep Learning on a Natural Codebase for Python](https://mlsanity.com/paper/2201.08441/) (2022)
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- [PredicateFix: Repairing Static Analysis Alerts with Bridging Predicates](https://mlsanity.com/paper/2503.12205/) (2025)
- [Predicting Patch Correctness Based on the Similarity of Failing Test Cases](https://mlsanity.com/paper/2107.13296/) (2021)
- [The Role of Transformer Models in Advancing Blockchain Technology: A Systematic Survey](https://mlsanity.com/paper/2409.02139/) (2024)
- [The Impact of Fine-tuning Large Language Models on Automated Program Repair](https://mlsanity.com/paper/2507.19909/) (2025)
- [Machine Learning Techniques for Python Source Code Vulnerability Detection](https://mlsanity.com/paper/2404.09537/) (2024)
- [NARRepair: Non-Autoregressive Code Generation Model for Automatic Program Repair](https://mlsanity.com/paper/2406.16526/) (2024)

## Abstract (arXiv)

In this paper, we first collect and track a large number of fixed and unfixed violations across revisions of software. The empirical analyses reveal that there are discrepancies in the distributions of violations that are detected and those that are fixed, in terms of occurrences, spread and categories, which can provide insights into prioritizing violations. To automatically identify patterns in violations and their fixes, we propose an approach that utilizes convolutional neural networks to learn features and clustering to regroup similar instances. We then evaluate the usefulness of the identified fix patterns by applying them to unfixed violations. The results show that developers will accept and merge a majority (69/116) of fixes generated from the inferred fix patterns. It is also noteworthy that the yielded patterns are applicable to four real bugs in the Defects4J major benchmark for software testing and automated repair.