Papers Code Repair
“Code Repair” 태그가 달린 논문 70편 · 필터 해제
Fortran2CPP: Automating Fortran-to-C++ Translation using LLMs via Multi-Turn Dialogue and Dual-Agent Integration
Translating legacy Fortran code into C++ is a crucial step in modernizing high-performance computing (HPC) applications. However, the scarcity of high-quality, parallel Fortran-to-C++ datasets and the limited domain-spec…
C++ codeCode RepairCode TranslationTranslationLLM-Aided Efficient Hardware Design Automation
With the rapidly increasing complexity of modern chips, hardware engineers are required to invest more effort in tasks such as circuit design, verification, and physical implementation. These workflows often involve cont…
Code RepairLogical ReasoningResponse GenerationCraftRTL: High-quality Synthetic Data Generation for Verilog Code Models with Correct-by-Construction Non-Textual Representations and Targeted Code Repair
Despite the significant progress made in code generation with large language models, challenges persist, especially with hardware description languages such as Verilog. This paper first presents an analysis of fine-tuned…
Code GenerationCode RepairSynthetic Data GenerationEnhancing Source Code Security with LLMs: Demystifying The Challenges and Generating Reliable Repairs
With the recent unprecedented advancements in Artificial Intelligence (AI) computing, progress in Large Language Models (LLMs) is accelerating rapidly, presenting challenges in establishing clear guidelines, particularly…
Code RepairCOAST: Enhancing the Code Debugging Ability of LLMs through Communicative Agent Based Data Synthesis
Code debugging is a vital stage of software development, essential for ensuring the reliability and performance of Large Language Models (LLMs) in the code generation task. Human debugging typically follows a multi-stage…
Code GenerationCode RepairIs Your AI-Generated Code Really Safe? Evaluating Large Language Models on Secure Code Generation with CodeSecEval
Large language models (LLMs) have brought significant advancements to code generation and code repair, benefiting both novice and experienced developers. However, their training using unsanitized data from open-source re…
Code GenerationCode RepairInvestigating the Transferability of Code Repair for Low-Resource Programming Languages
Large language models (LLMs) have shown remarkable performance on code generation tasks. A recent use case is iterative code repair, where an LLM fixes an incorrect program by rationalizing about errors and generating ne…
Code GenerationCode RepairSWT-Bench: Testing and Validating Real-World Bug-Fixes with Code Agents
Rigorous software testing is crucial for developing and maintaining high-quality code, making automated test generation a promising avenue for both improving software quality and boosting the effectiveness of code genera…
Code GenerationCode Repairsoftware testingCode Repair with LLMs gives an Exploration-Exploitation Tradeoff
Iteratively improving and repairing source code with large language models (LLMs), known as refinement, has emerged as a popular way of generating programs that would be too complex to construct in one shot. Given a bank…
Code RepairLanguage ModelingLanguage ModellingProgram Synthesis+2AutoCoder: Enhancing Code Large Language Model with \textsc{AIEV-Instruct}
We introduce AutoCoder, the first Large Language Model to surpass GPT-4 Turbo (April 2024) and GPT-4o in pass@1 on the Human Eval benchmark test ($\mathbf{90.9\%}$ vs. $\mathbf{90.2\%}$). In addition, AutoCoder offers a …
Class-level Code GenerationCode CompletionCode GenerationCode Repair+8DeepCode AI Fix: Fixing Security Vulnerabilities with Large Language Models
The automated program repair field has attracted substantial interest over the years, but despite significant research efforts, creating a system that works well for complex semantic bugs such as security vulnerabilities…
Code RepairFew-Shot LearningProgram RepairCode Security Vulnerability Repair Using Reinforcement Learning with Large Language Models
With the recent advancement of Large Language Models (LLMs), generating functionally correct code has become less complicated for a wide array of developers. While using LLMs has sped up the functional development proces…
Code GenerationCode Repairreinforcement-learningEnhanced Automated Code Vulnerability Repair using Large Language Models
This research addresses the complex challenge of automated repair of code vulnerabilities, vital for enhancing digital security in an increasingly technology-driven world. The study introduces a novel and efficient forma…
C++ codeCode RepairINTERVENOR: Prompting the Coding Ability of Large Language Models with the Interactive Chain of Repair
This paper introduces INTERVENOR (INTERactiVE chaiN Of Repair), a system designed to emulate the interactive code repair processes observed in humans, encompassing both code diagnosis and code repair. INTERVENOR prompts …
Code GenerationCode RepairCode TranslationEnhancing Large Language Models for Secure Code Generation: A Dataset-driven Study on Vulnerability Mitigation
Large language models (LLMs) have brought significant advancements to code generation, benefiting both novice and experienced developers. However, their training using unsanitized data from open-source repositories, like…
Code GenerationCode RepairOctoPack: Instruction Tuning Code Large Language Models
Finetuning large language models (LLMs) on instructions leads to vast performance improvements on natural language tasks. We apply instruction tuning using code, leveraging the natural structure of Git commits, which pai…
Code GenerationCode RepairHumanEvalFlakyFix: Using Large Language Models for Predicting Flaky Test Fix Categories and Test Code Repair
Flaky tests are problematic because they non-deterministically pass or fail for the same software version under test, causing confusion and wasting development effort. While machine learning models have been used to pred…
Code RepairFew-Shot LearningIn-Context LearningLanguage Modelling+1Guiding Language Models of Code with Global Context using Monitors
Language models of code (LMs) work well when the surrounding code provides sufficient context. This is not true when it becomes necessary to use types, functionality or APIs defined elsewhere in the repository or a linke…
Code CompletionCode GenerationCode RepairCode Translation+1Learning Performance-Improving Code Edits
With the decline of Moore's law, optimizing program performance has become a major focus of software research. However, high-level optimizations such as API and algorithm changes remain elusive due to the difficulty of u…
Code GenerationCode RepairData AugmentationLanguage ModellingPoison Attack and Defense on Deep Source Code Processing Models
In the software engineering community, deep learning (DL) has recently been applied to many source code processing tasks. Due to the poor interpretability of DL models, their security vulnerabilities require scrutiny. Re…
Clone DetectionCode RepairDefect Detection