mlsanityData to 7 Oct 2026

Practical Program Repair via Bytecode Mutation

arXiv 1807.03512 ↗First posted 10 Jul 2018cs.SEcited by 19

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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.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Jul 2018.

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Citing papers per year: 2021: 3; 2022: 1; 2023: 7; 2024: 6; 2025: 2.

19 papers.

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  1. 2024Agentless: Demystifying LLM-based Software Engineering Agentscited by 400
  2. 2021CURE: Code-Aware Neural Machine Translation for Automatic Program Repaircited by 85
  3. 2021A Syntax-Guided Edit Decoder for Neural Program Repaircited by 57
  4. 2024Exploring and Unleashing the Power of Large Language Models in Automated Code Translationcited by 57
  5. 2023Copiloting the Copilots: Fusing Large Language Models with Completion Engines for Automated Program Repaircited by 55
  6. 2023Conversational Automated Program Repaircited by 41
  7. 2023ITER: Iterative Neural Repair for Multi-Location Patchescited by 16
  8. 2023KNOD: Domain Knowledge Distilled Tree Decoder for Automated Program Repaircited by 13
  9. 2022Code Search based on Context-aware Code Translationcited by 11
  10. 2023Revisiting the Plastic Surgery Hypothesis via Large Language Modelscited by 7
  11. 2025ReCode: Improving LLM-based Code Repair with Fine-Grained Retrieval-Augmented Generationcited by 6
  12. 2023Mutation-based Fault Localization of Deep Neural Networkscited by 5
7 more
  1. 2023Exploring the Limits of ChatGPT in Software Security Applicationscited by 5
  2. 2024Memory-Efficient Large Language Models for Program Repair with Semantic-Guided Patch Generationcited by 5
  3. 2021Predicting Patch Correctness Based on the Similarity of Failing Test Casescited by 4
  4. 2024Integrating Various Software Artifacts for Better LLM-based Bug Localization and Program Repaircited by 4
  5. 2025DynaFix: Iterative Automated Program Repair Driven by Execution-Level Dynamic Informationcited by 3
  6. 2024MILE: A Mutation Testing Framework of In-Context Learning Systemscited by 2
  7. 2024PatUntrack: Automated Generating Patch Examples for Issue Reports without Tracked Insecure Codecited by 1