Home>Programming related>Other source code
Download

Awesome LLM for APR

Contents

  • Citation

  • Repair Scenarios

    • Semantic Bug

    • Security Vulnerability

    • Syntax Error

    • Programming Problem

    • Static Warning

    • Self-Debug

    • Type Error

    • Web UI Test

    • Smart Contract

    • Hardware Bug

    • Performance Bug

    • API Misuse

    • Crash Bug

    • Test Case

    • Formal Proof

    • Translation Bug

    • GitHub Issue

    • Code Review

    • Motion Planner

  • ? Human Study

  • ? Patch Correctness Assessment

  • Benchmark

  • ? Related APR Surveys

Citation

@article{zhang2024survey,  title={A Systematic Literature Review on Large Language Models for Automated Program Repair},  author={Zhang, Quanjun and Fang, Chunrong and Xie, Yang and Ma, Yuxiang and Sun, Weisong and Yang, Yun and Chen, Zhenyu},  journal={arXiv preprint arXiv:2405.01466}
  year={2024}}

Todo List

  • add SE agent-based studies for GitHub Issues

  • add ISSTA 2024 Papers

  • release a new version of this paper on arXiv

New Papers

  1. CORE: Resolving Code Quality Issues using LLMs [2024-FSE]

  2. Prompt Fix: Vulnerability Automatic Repair Technology Based on Prompt Engineering [2024-ICNC]

  3. Evaluating Large Language Models for Real-World Vulnerability Repair in C/C++ Code[2024-IWSPA]

  4. Investigating large language models capabilities for automatic code repair in Python[2024-Cluster Computing]

  5. LPR: Large Language Models-Aided Program Reduction[2024-ISSTA]

  6. A Case Study of LLM for Automated Vulnerability Repair: Assessing Impact of Reasoning and Patch Validation Feedback (2024年7月) AIware 2024

  7. When Large Language Models Confront Repository-Level Automatic Program Repair: How Well They Done? [2024-ICSE]

  8. Exploring Parameter-Efficient Fine-Tuning of Large Language Model on Automated Program Repair[2024-ASE]

  9. Exploring the Potential of Conversational Test Suite Based Program Repair on SWE-bench [2024-arXiv]

  10. Exploring and Lifting the Robustness of LLM-powered Automated Program Repair with Metamorphic Testing[2024-arXiv] [paper]

  11. Divide-and-Conquer: Automating Code Revisions via Localization-and-Revision [2024-TOSEM]

  12. From Code to Correctness: Closing the Last Mile of Code Generation with Hierarchical Debugging [2024-arXiv] [paper] [repo]

  13. Automated Program Repair for Introductory Programming Assignments [2024-TLT] [paper]

  14. Automated Repair of AI Code with Large Language Models and Formal Verification [2024-arXiv] [paper]

  15. CraftRTL: High-quality Synthetic Data Generation for Verilog Code Models with Correct-by-Construction Non-Textual Representations and Targeted Code Repair [2024-arXiv-NVIDIA] [paper]

  16. Benchmarking Automated Program Repair: An Extensive Study on Both Real-World and Artificial Bugs [2024-ISSTA]  [paper]

  17. Automated program repair via conversation: Fixing 162 out of 337 bugs for $0.42 each using chatgpt[2024-ISSTA] [paper]

  18. Leveraging Large Language Model for Automatic Patch Correctness Assessment[2024-TSE] [paper]

  19. Automated program repair for variability bugs in software product line systems[2024-JSS] [paper]

  20. PyBugHive: A Comprehensive Database of Manually Validated, Reproducible Python Bugs[2024-IEEE Access] [paper]

  21. How to Understand Whole Software Repository? [2024-arXiv] [paper]

Repair Scenarios

Semantic Bug

  1. Automated program repair for variability bugs in software product line systems[2024-JSS] [paper]

  2. A Unified Debugging Approach via LLM-Based Multi-Agent Synergy [2024-arxiv] [paper] [repo]

  3. How Far Can We Go with Practical Function-Level Program Repair? [2024-arxiv] [paper] [repo]

  4. Automated program repair via conversation: Fixing 162 out of 337 bugs for $0.42 each using chatgpt[2024-ISSTA] [paper]
    Old Version: Keep the Conversation Going: Fixing 162 out of 337 bugs for $0.42 each using ChatGPT [2023-arxiv] [paper]

  5. A Novel Approach for Automatic Program Repair using Round-Trip Translation with Large Language Models [2024-arxiv] [paper] [repo]

  6. Out of Context: How important is Local Context in Neural Program Repair? [2024-ICSE] [paper] [repo]

  7. Multi-Objective Fine-Tuning for Enhanced Program Repair with LLMs [2024-arxiv] [paper]

  8. Aligning LLMs for FL-free Program Repair [2024-arxiv] [paper]

  9. ContrastRepair: Enhancing Conversation-Based Automated Program Repair via Contrastive Test Case Pairs [2024-arxiv] [paper]

  10. Exploring the Potential of Pre-Trained Language Models of Code for Automated Program Repair [2024-Electronics] [paper]

  11. CigaR: Cost-efficient Program Repair with LLMs [2024-arxiv] [paper] [repo]

  12. The Fact Selection Problem in LLM-Based Program Repair [2024-arxiv] [paper] [repo]

  13. A Novel Approach for Automated Program Repair using Round-Trip Translation with Large Language Models [2024-arxiv] [paper] [repo]

  14. RepairAgent: An Autonomous, LLM-Based Agent for Program Repair [2024-arxiv] [paper]

  15. A Deep Dive into Large Language Models for Automated Bug Localization and Repair [2024-FSE/ESEC] [paper]

  16. Automated Program Repair in the Era of Large Pre-trained Language Models [2023-ICSE] [paper] [repo]

  17. Repair Is Nearly Generation: Multilingual Program Repair with LLMs [2023-AAAI] [paper]

  18. Retrieval-based prompt selection for code-related few-shot learning [2023-ICSE] [paper] [repo]

  19. What makes good in-context demonstrations for code intelligence tasks with llms? [2023-ASE] [paper] [repo]

  20. Fully Autonomous Programming with Large Language Models [2023-GECCO] [paper] [repo]

  21. Automated Program Repair Using Generative Models for Code Infilling [2023-AIED] [paper] [repo]

  22. STEAM: Simulating the InTeractive BEhavior of ProgrAMmers for Automatic Bug Fixing [2023-arxiv] [paper]

  23. Conversational automated program repair [2023-arxiv] [paper]

  24. Is ChatGPT the Ultimate Programming Assistant--How far is it? [2023-arxiv] [paper] [repo]

  25. Using Large Language Models for Bug Localization and Fixing [2023-iCAST] [paper]

  26. An Empirical Study on Fine-Tuning Large Language Models of Code for Automated Program Repair [2023-ASE] [paper] [repo]

  27. An Evaluation of the Effectiveness of OpenAI's ChatGPT for Automated Python Program Bug Fixing using QuixBugs [2023-iSEMANTIC] [paper]

  28. Explainable Automated Debugging via Large Language Model-driven Scientific Debugging [2023-arxiv] [paper]

  29. The Right Prompts for the Job: Repair Code-Review Defects with Large Language Model [2023-arxiv] [paper]

  30. Impact of Code Language Models on Automated Program Repair [2023-ICSE] [paper] [repo]

  31. Towards Generating Functionally Correct Code Edits from Natural Language Issue Descriptions [2023-arxiv] [paper]

  32. The Plastic Surgery Hypothesis in the Era of Large Language Models [2023-ASE] [paper] [repo]

  33. Exploring the Limits of ChatGPT in Software Security Applications [2023-arxiv] [paper]

  34. CodeScope: An Execution-based Multilingual Multitask Multidimensional Benchmark for Evaluating LLMs on Code Understanding and Generation [2023-arxiv] [paper] [repo]

  35. Enhancing Automated Program Repair through Fine-tuning and Prompt Engineering [2023-arxiv] [paper] [repo]

  36. Training Language Models for Programming Feedback Using Automated Repair Tools [2023-AIED] [paper] [repo]

  37. RepairLLaMA: Efficient Representations and Fine-Tuned Adapters for Program Repair [2023-arxiv] [paper] [repo]

  38. Automated Code Editing with Search-Generate-Modify [2023-arxiv] [paper] [repo]

  39. RAP-Gen: Retrieval-Augmented Patch Generation with CodeT5 for Automatic Program Repair [2023-FSE/ESEC] [paper] [repo]

  40. Neural Program Repair with Program Dependence Analysis and Effective Filter Mechanism [2023-arxiv] [paper]

  41. Coffee: Boost Your Code LLMs by Fixing Bugs with Feedback [2023-arxiv] [paper] [repo]

  42. A study on Prompt Design, Advantages and Limitations of ChatGPT for Deep Learning Program Repair [2023-arxiv]  [paper]

  43. Copiloting the Copilots: Fusing Large Language Models with Completion Engines for Automated Program Repair [2023-FSE/ESEC] [paper] [repo]

  44. Gamma: Revisiting Template-Based Automated Program Repair Via Mask Prediction [2023-ASE] [paper] [repo]

  45. An Extensive Study on Model Architecture and Program Representation in the Domain of Learning-based Automated Program Repair [2023-APR] [paper] [repo]

  46. Improving Automated Program Repair with Domain Adaptation [2023-TOSEM] [paper] [repo]

  47. Enhancing Code Language Models for Program Repair by Curricular Fine-tuning Framework [2023-ICSME] [paper]

  48. The potential use of ChatGPT for debugging and bug fixing [2023-] [paper]

  49. CIRCLE: Continual Repair across Programming Languages [2022-ISSTA] [paper] [repo]

  50. Towards JavaScript program repair with Generative Pre-trained Transformer (GPT-2) [2022-APR] [paper] [repo]

  51. Fix Bugs with Transformer through a Neural-Symbolic Edit Grammar [2022-ICLR] [paper]

  52. Patch Generation with Language Models: Feasibility and Scaling Behavior [2022-ICLR] [paper]

  53. Can OpenAI's codex fix bugs?: an evaluation on QuixBugs [2022-APR] [paper]

  54. An Analysis of the Automatic Bug Fixing Performance of ChatGPT [2022-APR] [paper] [repo]

  55. Less training, more repairing please: revisiting automated program repair via zero-shot learning [2022-FSE/ESEC] [paer] [repo]

  56. Framing Program Repair as Code Completion [2022-APR] [paper] [repo]

  57. DEAR A Novel Deep Learning-based Approach for Automated Program Repair [2022-ICSE] [paper] [repo]

  58. Generating Bug-Fixes Using Pretrained Transformers [2021-PLDI] [paper]

  59. Applying CodeBERT for Automated Program Repair of Java Simple Bugs [2021-MSR] [paper] [repo]

  60. CURE Code-Aware Neural Machine Translation for Automatic Program Repair [2021-ICSE] [paper] [repo]

  61. How to Understand Whole Software Repository? [2024-arXiv] [paper]

Security Vulnerability

  1. Automated Repair of AI Code with Large Language Models and Formal Verification [2024-arXiv] [paper]

  2. NAVRepair: Node-type Aware C/C++ Code Vulnerability Repair [2024-arxiv] [paper]

  3. Enhanced Automated Code Vulnerability Repair using Large Language Models [2024-arxiv] [paper]

  4. Out of Sight, Out of Mind: Better Automatic Vulnerability Repair by Broadening Input Ranges and Sources [2024-ICSE] [paper] [repo]

  5. A Study of Vulnerability Repair in JavaScript Programs with Large Language Models [2024-arxiv] [paper] [repo]

  6. Chain-of-Thought Prompting of Large Language Models for Discovering and Fixing Software Vulnerabilities [2024-arxiv] [paper]

  7. Pre-trained Model-based Automated Software Vulnerability Repair: How Far are We? [2023-TDSC] [paper] [repo]

  8. Examining zero-shot vulnerability repair with large language models [2023-S&P] [paper] [repo]

  9. An Empirical Study on Fine-Tuning Large Language Models of Code for Automated Program Repair [2023-ASE] [paper] [repo]

  10. A New Era in Software Security: Towards Self-Healing Software via Large Language Models and Formal Verification [2023-arxiv] [paper]

  11. Exploring the Limits of ChatGPT in Software Security Applications [2023-arxiv] [paper]

  12. ZeroLeak: Using LLMs for Scalable and Cost Effective Side-Channel Patching [2023-arxiv] [paper]

  13. How ChatGPT is Solving Vulnerability Management Problem [2023-arxiv] [paper] [repo]

  14. How Effective Are Neural Networks for Fixing Security Vulnerabilities [2023-ISSTA] [paper] [repo]

  15. Vision Transformer-Inspired Automated Vulnerability Repair [2023-TOSEM] [paper] [repo]

  16. Can large language models find and fix vulnerable software? [2023-arxiv] [paper]

  17. VulRepair: A T5-Based Automated Software Vulnerability Repair [2022-FSE/ESEC] [paper] [repo]

Syntax Error

  1. A Novel Approach for Automated Program Repair using Round-Trip Translation with Large Language Models [2024-arxiv] [paper] [repo]

  2. Repair Is Nearly Generation: Multilingual Program Repair with LLMs [2023-AAAI] [paper]

  3. Fixing Rust Compilation Errors using LLMs [2023-arxiv] [paper]

  4. An Empirical Study on Fine-Tuning Large Language Models of Code for Automated Program Repair [2023-ASE] [paper] [repo]

  5. A Chain of AI-based Solutions for Resolving FQNs and Fixing Syntax Errors in Partial Code [2023-arxiv] [paper] [repo]

  6. The Right Prompts for the Job: Repair Code-Review Defects with Large Language Model [2023-arxiv] [paper]

  7. SYNSHINE: improved fixing of Syntax Errors [2022-TSE] [paper] [repo]

Programming Problem

  1. CraftRTL: High-quality Synthetic Data Generation for Verilog Code Models with Correct-by-Construction Non-Textual Representations and Targeted Code Repair [2024-arXiv-NVIDIA] [paper]

  2. A Unified Debugging Approach via LLM-Based Multi-Agent Synergy [2024-arXiv] [paper] [repo]

  3. PyDex: Repairing Bugs in Introductory Python Assignments using LLMs [2024-OOPSLA] [paper] [repo]

  4. DebugBench: Evaluating Debugging Capability of Large Language Models [2024-arxiv] [paper] [repo]

  5. ContrastRepair: Enhancing Conversation-Based Automated Program Repair via Contrastive Test Case Pairs [2024-arxiv] [paper]

  6. ConDefects: A New Dataset to Address the Data Leakage Concern for LLM-based Fault Localization and Program Repair [2024-arxiv] [paper] [repo]

  7. Peer-aided Repairer: Empowering Large Language Models to Repair Advanced Student Assignments [2024-arxiv] [paper]

  8. Improved Program Repair Methods using Refactoring with GPT Models [2024-SIGCSE TS] [paper] [repo]

  9. A critical review of large language model on software engineering: An example from chatgpt and automated program repair [2023-arxiv] [paper] [repo]

  10. Automated Repair of Programs from Large Language Models [2023-ICSE] [paper] [repo]

  11. FixEval: Execution-based Evaluation of Program Fixes for Programming Problems [2023-APR] [paper] [repo]

  12. Refining ChatGPT-Generated Code: Characterizing and Mitigating Code Quality Issues [2023-TOSEM] [paper] [repo]

  13. Repairing bugs in python assignments using large language models [2022-arixv] [paper]

Static Warning

  1. Frustrated with Code Quality Issues? LLMs can Help! [2024-FSE/ESEC] [paper] [repo]

  2. SkipAnalyzer: An Embodied Agent for Code Analysis with Large Language Models [2023-arxiv] [paper] [repo]

  3. RAP-Gen: Retrieval-Augmented Patch Generation with CodeT5 for Automatic Program Repair [2023-FSE/ESEC] [paper] [repo]

  4. InferFix: End-to-End Program Repair with LLMs over Retrieval-Augmented Prompts [2023-FSE/ESEC] [paper] [repo]

  5. Can LLMs Patch Security Issues [2023-arxiv] [paper] [repo]

  6. Improving Automated Program Repair with Domain Adaptation [2023-TOSEM] [paper] [repo]

  7. An empirical study of deep transfer learning-based program repair for Kotlin projects [2022-FSE/ESEC] [paper]

  8. TFix-Learning to Fix Coding Errors with a Text-to-Text Transformer [2021-PMLR] [paper] [repo]

Self-Debug

  1. From Code to Correctness: Closing the Last Mile of Code Generation with Hierarchical Debugging [2024-arXiv] [paper] [repo]

  2. Teaching Large Language Models to Self-Debug [2024-ICLR] [paper]

  3. OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement [2024-arxiv] [paper] [repo]

  4. CYCLE: Learning to Self-Refine the Code Generation [2024-OOPSLA] [paper] [repo]

  5. LDB: A Large Language Model Debugger via Verifying Runtime Execution Step by Step [2024-arxiv] [paper] [repo]

  6. Leveraging Print Debugging to Improve Code Generation in Large Language Models [2024-arxiv] [paper]

  7. SelfEvolve: A Code Evolution Framework via Large Language Models [2023-arxiv] [paper]

  8. Self-Refine: Iterative Refinement with Self-Feedback [2023-NeurIPS] [paper] [repo]

  9. AgentCoder: Multi Agent-Code Generation with Iterative Testing and Optimisation [2023-arxiv] [paper]

  10. Self-Edit: Fault-Aware Code Editor for Code Generation [2023-ACL] [paper] [repo]

  11. Is Self-Repair a Silver Bullet for Code Generation? [2023-ICLR] [paper] [repo]

Type Error

  1. Domain Knowledge Matters: Improving Prompts with Fix Templates for Repairing Python Type Errors [2024-ICSE] [paper] [repo]

  2. PyTy: Repairing Static Type Errors in Python [2024-ICSE] [paper] [repo]

  3. GPT-3-Powered Type Error Debugging: Investigating the Use of Large Language Models for Code Repair [2023-SLE] [paper] [repo]

Web UI Test

  1. Guiding ChatGPT to Fix Web UI Tests via Explanation-Consistency Checking [2023-arxiv] [paper]

Smart Contract

  1. ACFIX: Guiding LLMs with Mined Common RBAC Practices for Context-Aware Repair of Access Control Vulnerabilities in Smart Contracts [2024-arxiv] [paper]

  2. Evaluating ChatGPT for Smart Contracts Vulnerability Correction [2023-COMPSAC] [paper] [repo]

Hardware Bug

  1. On Hardware Security Bug Code Fixes By Prompting Large Language Models [2024-TIFS] [paper] [repo]
    Its pre-print: Fixing Hardware Security Bugs with Large Language Models [2022-arXiv] [paper]

  2. HDLdebugger: Streamlining HDL debugging with Large Language Models [2024-arxiv] [paper]

  3. RTLFixer: Automatically Fixing RTL Syntax Errors with Large Language Models [2023-arxiv] [paper]

  4. LLM4SecHW: Leveraging domain-specific large language model for hardware debugging [2023-AsianHOST] [paper]

Performance Bug

  1. RAPGen: An Approach for Fixing Code Inefficiencies in Zero-Shot [2023-arxiv] [paper]

  2. DeepDev-PERF: A Deep Learning-Based Approach for Improving Software Performance [2022-FSE/ESEC] [paper] [repo]

API Misuse

  1. Evaluating Pre-trained Language Models for Repairing API Misuses [2023-arxiv] [paper] [repo]

Crash Bug

  1. Resolving Crash Bugs via Large Language Models: An Empirical Study [2023-arxiv] [paper] [repo]

Test Case

  1. Automated Test Case Repair Using Language Models [2024-arxiv] [paper]

  2. Identify and Update Test Cases when Production Code Changes: A Transformer-based Approach [2023-ASE]

Formal Proof

  1. Baldur: Whole-Proof Generation and Repair with Large Language Models [2023-FSE/ESEC] [paper]

Translation Bug

  1. Lost in Translation: A Study of Bugs Introduced by Large Language Models while Translating Code [2024-ICSE] [paper] [repo]

GitHub Issue

  1. SWE-bench: Can Language Models Resolve Real-World GitHub Issues? [2024-ICLR] [paper] [repo]

Code Review

  1. Exploring the Potential of ChatGPT in Automated Code Refinement: An Empirical Study [2024-ICSE] [paper] [repo]

Motion Planner

  1. DrPlanner: Diagnosis and Repair of Motion Planners Using Large Language Models [2024-arxiv] [paper] [repo]

Human Study

  1. Exploring Experiences with Automated Program Repair in Practice [2024-ICSE] [paper]

  2. Revisiting Unnaturalness for Automated Program Repair in the Era of Large Language Models [2024-arxiv] [papper] [repo]

  3. An Empirical Study of Adoption of ChatGPT for Bug Fixing among Professional Developers [2023-ITA] [paper]

Patch Correctness Assessment

  1. Leveraging Large Language Model for Automatic Patch Correctness Assessment[2024-TSE] [paper]

  2. APPT Boosting Automated Patch Correctness Prediction via Pre-trained Language Model [2024-TSE] [paper] [repo]

  3. The Best of Both Worlds: Combining Learned Embeddings with Engineered Features for Accurate Prediction of Correct Patches [2023-TOSME] [paper] [repo]

  4. Invalidator: Automated Patch Correctness Assessment via Semantic and Syntactic Reasoning [2023-TSE] [paper] [repo]

  5. PatchZero: Zero-Shot Automatic Patch Correctness Assessment [2023-arxiv] [paper]

  6. Is this Change the Answer to that Problem? Correlating Descriptions of Bug and Code Changes for Evaluating Patch Correctness [2021-ASE] [paper] [repo]

  7. Evaluating representation learning of code changes for predicting patch correctness in program repair [2020-ASE] [paper] [repo]

Benchmark

  1. Exploring Parameter-Efficient Fine-Tuning of Large Language Model on Automated Program Repair[2024-ASE] [paper]

  2. MuBench: Benchmarking Automated Program Repair: An Extensive Study on Both Real-World and Artificial Bugs [2024-ISSTA]  [paper]

  3. CodeEditorBench: Evaluating Code Editing Capability of Large Language Models [2024-arxiv] [paper] [repo]

  4. GitBug-Java: A Reproducible Benchmark of Recent Java Bugs [2024-arxiv] [paper] [repo]

  5. SWE-bench: Can Language Models Resolve Real-World GitHub Issues? [2024-ICLR] [paper] [repo]

  6. DebugBench: Evaluating Debugging Capability of Large Language Models [2024-arxiv] [paper] [repo]

  7. ConDefects: A New Dataset to Address the Data Leakage Concern for LLM-based Fault Localization and Program Repair [2024-arxiv] [paper] [repo]

  8. A critical review of large language model on software engineering: An example from chatgpt and automated program repair [2023-arxiv] [paper] [repo]

  9. CodeScope: An Execution-based Multilingual Multitask Multidimensional Benchmark for Evaluating LLMs on Code Understanding and Generation [2023-arxiv] [paper] [repo]

  10. FixEval: Execution-based Evaluation of Program Fixes for Programming Problems [2023-APR] [paper] [repo]

Related APR Surveys

  1. A Survey of Learning-based Automated Program Repair [2023-TOSEM] [paper] [repo]

  2. Automatic Software Repair: A Bibliography [2018-CSUR] paper]

  3. Automatic Software Repair: A Survey [2017-TSE] paper]

Expand
Additional Information
Related Applications
Recommended for You
Related Information All
User Comments