# Getafix: Learning to Fix Bugs Automatically

arXiv: https://arxiv.org/abs/1902.06111 · HTML: https://mlsanity.com/paper/1902.06111/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Feb 2019.

## Built on by (most cited first, 30 of 41)

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- [Is Self-Repair a Silver Bullet for Code Generation?](https://mlsanity.com/paper/2306.09896/) (2023)
- [Break-It-Fix-It: Unsupervised Learning for Program Repair](https://mlsanity.com/paper/2106.06600/) (2021)
- [A Syntax-Guided Edit Decoder for Neural Program Repair](https://mlsanity.com/paper/2106.08253/) (2021)
- [Exploring and Unleashing the Power of Large Language Models in Automated Code Translation](https://mlsanity.com/paper/2404.14646/) (2024)
- [A New Era in Software Security: Towards Self-Healing Software via Large Language Models and Formal Verification](https://mlsanity.com/paper/2305.14752/) (2023)
- [A Survey on Machine Learning Techniques for Source Code Analysis](https://mlsanity.com/paper/2110.09610/) (2021)
- [GrACE: Generation using Associated Code Edits](https://mlsanity.com/paper/2305.14129/) (2023)
- [Neurosymbolic Repair for Low-Code Formula Languages](https://mlsanity.com/paper/2207.11765/) (2022)
- [Improving Automated Program Repair with Domain Adaptation](https://mlsanity.com/paper/2212.11414/) (2022)
- [Enhancing Automated Program Repair through Fine-tuning and Prompt Engineering](https://mlsanity.com/paper/2304.07840/) (2023)
- [From Vulnerabilities to Remediation: A Systematic Literature Review of LLMs in Code Security](https://mlsanity.com/paper/2412.15004/) (2024)
- [Fix Bugs with Transformer through a Neural-Symbolic Edit Grammar](https://mlsanity.com/paper/2204.06643/) (2022)
- [Overwatch: Learning Patterns in Code Edit Sequences](https://mlsanity.com/paper/2207.12456/) (2022)
- [Anti-unification and Generalization: A Survey](https://mlsanity.com/paper/2302.00277/) (2023)
- [DeepCode AI Fix: Fixing Security Vulnerabilities with Large Language Models](https://mlsanity.com/paper/2402.13291/) (2024)
- [ContextCov: Deriving and Enforcing Executable Constraints from Agent Instruction Files](https://mlsanity.com/paper/2603.00822/) (2026)
- [Frustrated with Code Quality Issues? LLMs can Help!](https://mlsanity.com/paper/2309.12938/) (2023)
- [Memory-Efficient Large Language Models for Program Repair with Semantic-Guided Patch Generation](https://mlsanity.com/paper/2410.16655/) (2024)
- [PredicateFix: Repairing Static Analysis Alerts with Bridging Predicates](https://mlsanity.com/paper/2503.12205/) (2025)
- [Can we learn from developer mistakes? Learning to localize and repair real bugs from real bug fixes](https://mlsanity.com/paper/2207.00301/) (2022)
- [Learning to Extend Program Graphs to Work-in-Progress Code](https://mlsanity.com/paper/2105.14038/) (2021)
- [SapientML: Synthesizing Machine Learning Pipelines by Learning from Human-Written Solutions](https://mlsanity.com/paper/2202.10451/) (2022)
- [The Impact of Fine-tuning Large Language Models on Automated Program Repair](https://mlsanity.com/paper/2507.19909/) (2025)
- [DSHGT: Dual-Supervisors Heterogeneous Graph Transformer -- A pioneer study of using heterogeneous graph learning for detecting software vulnerabilities](https://mlsanity.com/paper/2306.01376/) (2023)
- [An Empirical Study on Self-correcting Large Language Models for Data Science Code Generation](https://mlsanity.com/paper/2408.15658/) (2024)
- [SkillScriptBench: Benchmarking Self-Evolution of Executable Agent Skill Packages Beyond Markdown](https://mlsanity.com/paper/2610.04008/) (2026)
- [Automating Program Structure Classification](https://mlsanity.com/paper/2101.10087/) (2021)

## Abstract (arXiv)

Static analyzers help find bugs early by warning about recurring bug categories. While fixing these bugs still remains a mostly manual task in practice, we observe that fixes for a specific bug category often are repetitive. This paper addresses the problem of automatically fixing instances of common bugs by learning from past fixes. We present Getafix, an approach that produces human-like fixes while being fast enough to suggest fixes in time proportional to the amount of time needed to obtain static analysis results in the first place. Getafix is based on a novel hierarchical clustering algorithm that summarizes fix patterns into a hierarchy ranging from general to specific patterns. Instead of a computationally expensive exploration of a potentially large space of candidate fixes, Getafix uses a simple yet effective ranking technique that uses the context of a code change to select the most appropriate fix for a given bug. Our evaluation applies Getafix to 1,268 bug fixes for six bug categories reported by popular static analyzers for Java, including null dereferences, incorrect API calls, and misuses of particular language constructs. The approach predicts exactly the human-written fix as the top-most suggestion between 12% and 91% of the time, depending on the bug category. The top-5 suggestions contain fixes for 526 of the 1,268 bugs. Moreover, we report on deploying the approach within Facebook, where it contributes to the reliability of software used by billions of people. To the best of our knowledge, Getafix is the first industrially-deployed automated bug-fixing tool that learns fix patterns from past, human-written fixes to produce human-like fixes.