@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}
}
- Automated Program Repair for Introductory Programming Assignments [2024-TLT] [Paper]
- 🔥A Unified Debugging Approach via LLM-Based Multi-Agent Synergy [2024-arxiv] [paper] [repo]
- 🔥How Far Can We Go with Practical Function-Level Program Repair? [2024-arxiv] [paper] [repo]
- A Novel Approach for Automatic Program Repair using Round-Trip Translation with Large Language Models [2024-arxiv] [paper] [repo]
- Out of Context: How important is Local Context in Neural Program Repair? [2024-ICSE] [paper] [repo]
- Multi-Objective Fine-Tuning for Enhanced Program Repair with LLMs [2024-arxiv] [paper]
- Aligning LLMs for FL-free Program Repair [2024-arxiv] [paper]
- ContrastRepair: Enhancing Conversation-Based Automated Program Repair via Contrastive Test Case Pairs [2024-arxiv] [paper]
- Exploring the Potential of Pre-Trained Language Models of Code for Automated Program Repair [2024-Electronics] [paper]
- CigaR: Cost-efficient Program Repair with LLMs [2024-arxiv] [paper] [repo]
- The Fact Selection Problem in LLM-Based Program Repair [2024-arxiv] [paper] [repo]
- A Novel Approach for Automated Program Repair using Round-Trip Translation with Large Language Models [2024-arxiv] [paper] [repo]
- RepairAgent: An Autonomous, LLM-Based Agent for Program Repair [2024-arxiv] [paper]
- A Deep Dive into Large Language Models for Automated Bug Localization and Repair [2024-FSE/ESEC] [paper]
- Automated Program Repair in the Era of Large Pre-trained Language Models [2023-ICSE] [paper] [repo]
- Repair Is Nearly Generation: Multilingual Program Repair with LLMs [2023-AAAI] [paper]
- Retrieval-based prompt selection for code-related few-shot learning [2023-ICSE] [paper] [repo]
- What makes good in-context demonstrations for code intelligence tasks with llms? [2023-ASE] [paper] [repo]
- Fully Autonomous Programming with Large Language Models [2023-GECCO] [paper] [repo]
- Automated Program Repair Using Generative Models for Code Infilling [2023-AIED] [paper] [repo]
- STEAM: Simulating the InTeractive BEhavior of ProgrAMmers for Automatic Bug Fixing [2023-arxiv] [paper]
- Conversational automated program repair [2023-arxiv] [paper]
- Is ChatGPT the Ultimate Programming Assistant--How far is it? [2023-arxiv] [paper] [repo]
- Using Large Language Models for Bug Localization and Fixing [2023-iCAST] [paper]
- An Empirical Study on Fine-Tuning Large Language Models of Code for Automated Program Repair [2023-ASE] [paper] [repo]
- An Evaluation of the Effectiveness of OpenAI's ChatGPT for Automated Python Program Bug Fixing using QuixBugs [2023-iSEMANTIC] [paper]
- Keep the Conversation Going: Fixing 162 out of 337 bugs for $0.42 each using ChatGPT [2023-arxiv] [paper]
- Explainable Automated Debugging via Large Language Model-driven Scientific Debugging [2023-arxiv] [paper]
- The Right Prompts for the Job: Repair Code-Review Defects with Large Language Model [2023-arxiv] [paper]
- Impact of Code Language Models on Automated Program Repair [2023-ICSE] [paper] [repo]
- Towards Generating Functionally Correct Code Edits from Natural Language Issue Descriptions [2023-arxiv] [paper]
- The Plastic Surgery Hypothesis in the Era of Large Language Models [2023-ASE] [paper] [repo]
- Exploring the Limits of ChatGPT in Software Security Applications [2023-arxiv] [paper]
- CodeScope: An Execution-based Multilingual Multitask Multidimensional Benchmark for Evaluating LLMs on Code Understanding and Generation [2023-arxiv] [paper] [repo]
- Enhancing Automated Program Repair through Fine-tuning and Prompt Engineering [2023-arxiv] [paper] [repo]
- Training Language Models for Programming Feedback Using Automated Repair Tools [2023-AIED] [paper] [repo]
- RepairLLaMA: Efficient Representations and Fine-Tuned Adapters for Program Repair [2023-arxiv] [paper] [repo]
- Automated Code Editing with Search-Generate-Modify [2023-arxiv] [paper] [repo]
- RAP-Gen: Retrieval-Augmented Patch Generation with CodeT5 for Automatic Program Repair [2023-FSE/ESEC] [paper] [repo]
- Neural Program Repair with Program Dependence Analysis and Effective Filter Mechanism [2023-arxiv] [paper]
- Coffee: Boost Your Code LLMs by Fixing Bugs with Feedback [2023-arxiv] [paper] [repo]
- A study on Prompt Design, Advantages and Limitations of ChatGPT for Deep Learning Program Repair [2023-arxiv] [paper]
- Copiloting the Copilots: Fusing Large Language Models with Completion Engines for Automated Program Repair [2023-FSE/ESEC] [paper] [repo]
- Gamma: Revisiting Template-Based Automated Program Repair Via Mask Prediction [2023-ASE] [paper] [repo]
- An Extensive Study on Model Architecture and Program Representation in the Domain of Learning-based Automated Program Repair [2023-APR] [paper] [repo]
- Improving Automated Program Repair with Domain Adaptation [2023-TOSEM] [paper] [repo]
- Enhancing Code Language Models for Program Repair by Curricular Fine-tuning Framework [2023-ICSME] [paper]
- The potential use of ChatGPT for debugging and bug fixing [2023-] [paper]
- CIRCLE: Continual Repair across Programming Languages [2022-ISSTA] [paper] [repo]
- Towards JavaScript program repair with Generative Pre-trained Transformer (GPT-2) [2022-APR] [paper] [repo]
- Fix Bugs with Transformer through a Neural-Symbolic Edit Grammar [2022-ICLR] [paper]
- Patch Generation with Language Models: Feasibility and Scaling Behavior [2022-ICLR] [paper]
- Can OpenAI's codex fix bugs?: an evaluation on QuixBugs [2022-APR] [paper]
- An Analysis of the Automatic Bug Fixing Performance of ChatGPT [2022-APR] [paper] [repo]
- Less training, more repairing please: revisiting automated program repair via zero-shot learning [2022-FSE/ESEC] [paer] [repo]
- Framing Program Repair as Code Completion [2022-APR] [paper] [repo]
- DEAR A Novel Deep Learning-based Approach for Automated Program Repair [2022-ICSE] [paper] [repo]
- Generating Bug-Fixes Using Pretrained Transformers [2021-PLDI] [paper]
- Applying CodeBERT for Automated Program Repair of Java Simple Bugs [2021-MSR] [paper] [repo]
- CURE Code-Aware Neural Machine Translation for Automatic Program Repair [2021-ICSE] [paper] [repo]
-
🔥NAVRepair: Node-type Aware C/C++ Code Vulnerability Repair [2024-arxiv] [paper]
-
Enhanced Automated Code Vulnerability Repair using Large Language Models [2024-arxiv] [paper]
-
Out of Sight, Out of Mind: Better Automatic Vulnerability Repair by Broadening Input Ranges and Sources [2024-ICSE] [paper] [repo]
-
A Study of Vulnerability Repair in JavaScript Programs with Large Language Models [2024-arxiv] [paper] [repo]
-
Chain-of-Thought Prompting of Large Language Models for Discovering and Fixing Software Vulnerabilities [2024-arxiv] [paper]
-
Pre-trained Model-based Automated Software Vulnerability Repair: How Far are We? [2023-TDSC] [paper] [repo]
-
Examining zero-shot vulnerability repair with large language models [2023-S&P] [paper] [repo]
-
An Empirical Study on Fine-Tuning Large Language Models of Code for Automated Program Repair [2023-ASE] [paper] [repo]
-
A New Era in Software Security: Towards Self-Healing Software via Large Language Models and Formal Verification [2023-arxiv] [paper]
-
Exploring the Limits of ChatGPT in Software Security Applications [2023-arxiv] [paper]
-
ZeroLeak: Using LLMs for Scalable and Cost Effective Side-Channel Patching [2023-arxiv] [paper]
-
How ChatGPT is Solving Vulnerability Management Problem [2023-arxiv] [paper] [repo]
-
How Effective Are Neural Networks for Fixing Security Vulnerabilities [2023-ISSTA] [paper] [repo]
-
Vision Transformer-Inspired Automated Vulnerability Repair [2023-TOSEM] [paper] [repo]
-
Can large language models find and fix vulnerable software? [2023-arxiv] [paper]
-
VulRepair: A T5-Based Automated Software Vulnerability Repair [2022-FSE/ESEC] [paper] [repo]
- A Novel Approach for Automated Program Repair using Round-Trip Translation with Large Language Models [2024-arxiv] [paper] [repo]
- Repair Is Nearly Generation: Multilingual Program Repair with LLMs [2023-AAAI] [paper]
- Fixing Rust Compilation Errors using LLMs [2023-arxiv] [paper]
- An Empirical Study on Fine-Tuning Large Language Models of Code for Automated Program Repair [2023-ASE] [paper] [repo]
- A Chain of AI-based Solutions for Resolving FQNs and Fixing Syntax Errors in Partial Code [2023-arxiv] [paper] [repo]
- The Right Prompts for the Job: Repair Code-Review Defects with Large Language Model [2023-arxiv] [paper]
- SYNSHINE: improved fixing of Syntax Errors [2022-TSE] [paper] [repo]
- A Unified Debugging Approach via LLM-Based Multi-Agent Synergy [2024-arXiv] [paper] [repo]
- PyDex: Repairing Bugs in Introductory Python Assignments using LLMs [2024-OOPSLA] [paper] [repo]
- DebugBench: Evaluating Debugging Capability of Large Language Models [2024-arxiv] [paper] [repo]
- ContrastRepair: Enhancing Conversation-Based Automated Program Repair via Contrastive Test Case Pairs [2024-arxiv] [paper]
- ConDefects: A New Dataset to Address the Data Leakage Concern for LLM-based Fault Localization and Program Repair [2024-arxiv] [paper] [repo]
- Peer-aided Repairer: Empowering Large Language Models to Repair Advanced Student Assignments [2024-arxiv] [paper]
- Improved Program Repair Methods using Refactoring with GPT Models [2024-SIGCSE TS] [paper] [repo]
- A critical review of large language model on software engineering: An example from chatgpt and automated program repair [2023-arxiv] [paper] [repo]
- Automated Repair of Programs from Large Language Models [2023-ICSE] [paper] [repo]
- FixEval: Execution-based Evaluation of Program Fixes for Programming Problems [2023-APR] [paper] [repo]
- Refining ChatGPT-Generated Code: Characterizing and Mitigating Code Quality Issues [2023-TOSEM] [paper] [repo]
- Repairing bugs in python assignments using large language models [2022-arixv] [paper]
- Frustrated with Code Quality Issues? LLMs can Help! [2024-FSE/ESEC] [paper] [repo]
- SkipAnalyzer: An Embodied Agent for Code Analysis with Large Language Models [2023-arxiv] [paper] [repo]
- RAP-Gen: Retrieval-Augmented Patch Generation with CodeT5 for Automatic Program Repair [2023-FSE/ESEC] [paper] [repo]
- InferFix: End-to-End Program Repair with LLMs over Retrieval-Augmented Prompts [2023-FSE/ESEC] [paper] [repo]
- Can LLMs Patch Security Issues [2023-arxiv] [paper] [repo]
- Improving Automated Program Repair with Domain Adaptation [2023-TOSEM] [paper] [repo]
- An empirical study of deep transfer learning-based program repair for Kotlin projects [2022-FSE/ESEC] [paper]
- TFix-Learning to Fix Coding Errors with a Text-to-Text Transformer [2021-PMLR] [paper] [repo]
- Teaching Large Language Models to Self-Debug [2024-ICLR] [paper]
- OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement [2024-arxiv] [paper] [repo]
- CYCLE: Learning to Self-Refine the Code Generation [2024-OOPSLA] [paper] [repo]
- LDB: A Large Language Model Debugger via Verifying Runtime Execution Step by Step [2024-arxiv] [paper] [repo]
- Leveraging Print Debugging to Improve Code Generation in Large Language Models [2024-arxiv] [paper]
- SelfEvolve: A Code Evolution Framework via Large Language Models [2023-arxiv] [paper]
- Self-Refine: Iterative Refinement with Self-Feedback [2023-NeurIPS] [paper] [repo]
- AgentCoder: Multi Agent-Code Generation with Iterative Testing and Optimisation [2023-arxiv] [paper]
- Self-Edit: Fault-Aware Code Editor for Code Generation [2023-ACL] [paper] [repo]
- Is Self-Repair a Silver Bullet for Code Generation? [2023-ICLR] [paper] [repo]
- Domain Knowledge Matters: Improving Prompts with Fix Templates for Repairing Python Type Errors [2024-ICSE] [paper] [repo]
- PyTy: Repairing Static Type Errors in Python [2024-ICSE] [paper] [repo]
- GPT-3-Powered Type Error Debugging: Investigating the Use of Large Language Models for Code Repair [2023-SLE] [paper] [repo]
- Guiding ChatGPT to Fix Web UI Tests via Explanation-Consistency Checking [2023-arxiv] [paper]
- ACFIX: Guiding LLMs with Mined Common RBAC Practices for Context-Aware Repair of Access Control Vulnerabilities in Smart Contracts [2024-arxiv] [paper]
- Evaluating ChatGPT for Smart Contracts Vulnerability Correction [2023-COMPSAC] [paper] [repo]
- 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] - HDLdebugger: Streamlining HDL debugging with Large Language Models [2024-arxiv] [paper]
- RTLFixer: Automatically Fixing RTL Syntax Errors with Large Language Models [2023-arxiv] [paper]
- LLM4SecHW: Leveraging domain-specific large language model for hardware debugging [2023-AsianHOST] [paper]
- RAPGen: An Approach for Fixing Code Inefficiencies in Zero-Shot [2023-arxiv] [paper]
- DeepDev-PERF: A Deep Learning-Based Approach for Improving Software Performance [2022-FSE/ESEC] [paper] [repo]
- Automated Test Case Repair Using Language Models [2024-arxiv] [paper]
- Baldur: Whole-Proof Generation and Repair with Large Language Models [2023-FSE/ESEC] [paper]
- Lost in Translation: A Study of Bugs Introduced by Large Language Models while Translating Code [2024-ICSE] [paper] [repo]
- Exploring the Potential of ChatGPT in Automated Code Refinement: An Empirical Study [2024-ICSE] [paper] [repo]
- DrPlanner: Diagnosis and Repair of Motion Planners Using Large Language Models [2024-arxiv] [paper] [repo]
- Exploring Experiences with Automated Program Repair in Practice [2024-ICSE] [paper]
- Revisiting Unnaturalness for Automated Program Repair in the Era of Large Language Models [2024-arxiv] [papper] [repo]
- An Empirical Study of Adoption of ChatGPT for Bug Fixing among Professional Developers [2023-ITA] [paper]
- APPT Boosting Automated Patch Correctness Prediction via Pre-trained Language Model [2024-TSE] [paper] [repo]
- The Best of Both Worlds: Combining Learned Embeddings with Engineered Features for Accurate Prediction of Correct Patches [2023-TOSME] [paper] [repo]
- Invalidator: Automated Patch Correctness Assessment via Semantic and Syntactic Reasoning [2023-TSE] [paper] [repo]
- PatchZero: Zero-Shot Automatic Patch Correctness Assessment [2023-arxiv] [paper]
- Is this Change the Answer to that Problem? Correlating Descriptions of Bug and Code Changes for Evaluating Patch Correctness [2021-ASE] [paper] [repo]
- Evaluating representation learning of code changes for predicting patch correctness in program repair [2020-ASE] [paper] [repo]
- CodeEditorBench: Evaluating Code Editing Capability of Large Language Models [2024-arxiv] [paper] [repo]
- GitBug-Java: A Reproducible Benchmark of Recent Java Bugs [2024-arxiv] [paper] [repo]
- SWE-bench: Can Language Models Resolve Real-World GitHub Issues? [2024-ICLR] [paper] [repo]
- DebugBench: Evaluating Debugging Capability of Large Language Models [2024-arxiv] [paper] [repo]
- ConDefects: A New Dataset to Address the Data Leakage Concern for LLM-based Fault Localization and Program Repair [2024-arxiv] [paper] [repo]
- A critical review of large language model on software engineering: An example from chatgpt and automated program repair [2023-arxiv] [paper] [repo]
- CodeScope: An Execution-based Multilingual Multitask Multidimensional Benchmark for Evaluating LLMs on Code Understanding and Generation [2023-arxiv] [paper] [repo]
- FixEval: Execution-based Evaluation of Program Fixes for Programming Problems [2023-APR] [paper] [repo]