# Practical Program Repair via Bytecode Mutation

arXiv: https://arxiv.org/abs/1807.03512 · HTML: https://mlsanity.com/paper/1807.03512/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2018.

## Built on by (most cited first, 19 of 19)

- [Agentless: Demystifying LLM-based Software Engineering Agents](https://mlsanity.com/paper/2407.01489/) (2024)
- [CURE: Code-Aware Neural Machine Translation for Automatic Program Repair](https://mlsanity.com/paper/2103.00073/) (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)
- [Copiloting the Copilots: Fusing Large Language Models with Completion Engines for Automated Program Repair](https://mlsanity.com/paper/2309.00608/) (2023)
- [Conversational Automated Program Repair](https://mlsanity.com/paper/2301.13246/) (2023)
- [ITER: Iterative Neural Repair for Multi-Location Patches](https://mlsanity.com/paper/2304.12015/) (2023)
- [Code Search based on Context-aware Code Translation](https://mlsanity.com/paper/2202.08029/) (2022)
- [KNOD: Domain Knowledge Distilled Tree Decoder for Automated Program Repair](https://mlsanity.com/paper/2302.01857/) (2023)
- [Revisiting the Plastic Surgery Hypothesis via Large Language Models](https://mlsanity.com/paper/2303.10494/) (2023)
- [Exploring the Limits of ChatGPT in Software Security Applications](https://mlsanity.com/paper/2312.05275/) (2023)
- [ReCode: Improving LLM-based Code Repair with Fine-Grained Retrieval-Augmented Generation](https://mlsanity.com/paper/2509.02330/) (2025)
- [Mutation-based Fault Localization of Deep Neural Networks](https://mlsanity.com/paper/2309.05067/) (2023)
- [Memory-Efficient Large Language Models for Program Repair with Semantic-Guided Patch Generation](https://mlsanity.com/paper/2410.16655/) (2024)
- [Predicting Patch Correctness Based on the Similarity of Failing Test Cases](https://mlsanity.com/paper/2107.13296/) (2021)
- [Integrating Various Software Artifacts for Better LLM-based Bug Localization and Program Repair](https://mlsanity.com/paper/2412.03905/) (2024)
- [DynaFix: Iterative Automated Program Repair Driven by Execution-Level Dynamic Information](https://mlsanity.com/paper/2512.24635/) (2025)
- [MILE: A Mutation Testing Framework of In-Context Learning Systems](https://mlsanity.com/paper/2409.04831/) (2024)
- [PatUntrack: Automated Generating Patch Examples for Issue Reports without Tracked Insecure Code](https://mlsanity.com/paper/2408.08619/) (2024)

## Abstract (arXiv)

Software debugging is tedious, time-consuming, and even error-prone by itself. So, various automated debugging techniques have been proposed in the literature to facilitate the debugging process. Automated Program Repair (APR) is one of the most recent advances in automated debugging, and can directly produce patches for buggy programs with minimal human intervention. Although various advanced APR techniques (including those that are either search-based or semantic-based) have been proposed, the simplistic mutation-based APR technique, which simply uses pre-defined mutation operators (e.g., changing a>=b into a>b) to mutate programs for finding patches, has not yet been thoroughly studied. In this paper, we implement the first practical bytecode-level APR technique, PraPR, and present the first extensive study on fixing real-world bugs (e.g., Defects4J bugs) using bytecode mutation. The experimental results show that surprisingly even PraPR with only the basic traditional mutators can produce genuine patches for 18 bugs. Furthermore, with our augmented mutators, PraPR is able to produce genuine patches for 43 bugs, significantly outperforming state-of-the-art APR. It is also an order of magnitude faster, indicating a promising future for bytecode-mutation-based APR.