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Code Repair

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이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.

CodeJudgeBench: Benchmarking LLM-as-a-Judge for Coding Tasks

Agent KB: Leveraging Cross-Domain Experience for Agentic Problem Solving

Breakpoint: Scalable evaluation of system-level reasoning in LLM code agents

CrashFixer: A crash resolution agent for the Linux kernel

How Accurately Do Large Language Models Understand Code?

Why Stop at One Error? Benchmarking LLMs as Data Science Code Debuggers for Multi-Hop and Multi-Bug Errors

RocketPPA: Code-Level Power, Performance, and Area Prediction via LLM and Mixture of Experts

SolBench: A Dataset and Benchmark for Evaluating Functional Correctness in Solidity Code Completion and Repair

AuPair: Golden Example Pairs for Code Repair

Fortran2CPP: Automating Fortran-to-C++ Translation using LLMs via Multi-Turn Dialogue and Dual-Agent Integration

LLM-Aided Efficient Hardware Design Automation

CraftRTL: High-quality Synthetic Data Generation for Verilog Code Models with Correct-by-Construction Non-Textual Representations and Targeted Code Repair

Enhancing Source Code Security with LLMs: Demystifying The Challenges and Generating Reliable Repairs

COAST: Enhancing the Code Debugging Ability of LLMs through Communicative Agent Based Data Synthesis

Is Your AI-Generated Code Really Safe? Evaluating Large Language Models on Secure Code Generation with CodeSecEval

Investigating the Transferability of Code Repair for Low-Resource Programming Languages

SWT-Bench: Testing and Validating Real-World Bug-Fixes with Code Agents

Code Repair with LLMs gives an Exploration-Exploitation Tradeoff

AutoCoder: Enhancing Code Large Language Model with \textsc{AIEV-Instruct}

DeepCode AI Fix: Fixing Security Vulnerabilities with Large Language Models

Code Security Vulnerability Repair Using Reinforcement Learning with Large Language Models

Enhanced Automated Code Vulnerability Repair using Large Language Models

INTERVENOR: Prompting the Coding Ability of Large Language Models with the Interactive Chain of Repair

Enhancing Large Language Models for Secure Code Generation: A Dataset-driven Study on Vulnerability Mitigation

OctoPack: Instruction Tuning Code Large Language Models

FlakyFix: Using Large Language Models for Predicting Flaky Test Fix Categories and Test Code Repair

Guiding Language Models of Code with Global Context using Monitors

Learning Performance-Improving Code Edits

Poison Attack and Defense on Deep Source Code Processing Models

Fix Bugs with Transformer through a Neural-Symbolic Edit Grammar

Jointly Learning to Repair Code and Generate Commit Message

Break-It-Fix-It: Unsupervised Learning for Program Repair

CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation

MACER: A Modular Framework for Accelerated Compilation Error Repair

Inferring Javascript types using Graph Neural Networks

Learning to Repair Software Vulnerabilities with Generative Adversarial Networks

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