Imagen de Portada

Improving MPI Error Detection and Repair with Large Language Models and Bug References

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Piersall, Scott, Gao, Yang, Liu, Shenyang, Wang, Liqiang
Formato:	Preprint
Publicado:	2026
Materias:	Software Engineering Artificial Intelligence
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Exploring Large Language Models in Resolving Environment-Related Crash Bugs: Localizing and Repairing
por: Du, Xueying, et al.
Publicado: (2023)

Fine-Tuning Code Language Models to Detect Cross-Language Bugs
por: Li, Zengyang, et al.
Publicado: (2025)

Automated Duplicate Bug Report Detection in Large Open Bug Repositories
por: Laney, Clare E., et al.
Publicado: (2025)

Peer-aided Repairer: Empowering Large Language Models to Repair Advanced Student Assignments
por: Zhao, Qianhui, et al.
Publicado: (2024)

Bugs in Large Language Models Generated Code: An Empirical Study
por: Tambon, Florian, et al.
Publicado: (2024)

SWE-Synth: Synthesizing Verifiable Bug-Fix Data to Enable Large Language Models in Resolving Real-World Bugs
por: Pham, Minh V. T., et al.
Publicado: (2025)

INTERVENOR: Prompting the Coding Ability of Large Language Models with the Interactive Chain of Repair
por: Wang, Hanbin, et al.
Publicado: (2023)

Dynamic Cogeneration of Bug Reproduction Test in Agentic Program Repair
por: Cheng, Runxiang, et al.
Publicado: (2026)

Agentic Bug Reproduction for Effective Automated Program Repair at Google
por: Cheng, Runxiang, et al.
Publicado: (2025)

Are Large Language Models Memorizing Bug Benchmarks?
por: Ramos, Daniel, et al.
Publicado: (2024)

One Bug, Hundreds Behind: LLMs for Large-Scale Bug Discovery
por: Wu, Qiushi, et al.
Publicado: (2025)

Self-Bootstrapping Automated Program Repair: Using LLMs to Generate and Evaluate Synthetic Training Data for Bug Repair
por: de-Fitero-Dominguez, David, et al.
Publicado: (2025)

Repeton: Structured Bug Repair with ReAct-Guided Patch-and-Test Cycles
por: Vinh, Nguyen Phu, et al.
Publicado: (2025)

RFCAudit: An LLM Agent for Functional Bug Detection in Network Protocols
por: Zheng, Mingwei, et al.
Publicado: (2025)

IRepair: An Intent-Aware Approach to Repair Data-Driven Errors in Large Language Models
por: Imtiaz, Sayem Mohammad, et al.
Publicado: (2025)

Integrating Various Software Artifacts for Better LLM-based Bug Localization and Program Repair
por: Feng, Qiong, et al.
Publicado: (2024)

$T^3$: Multi-level Tree-based Automatic Program Repair with Large Language Models
por: Liu, Quanming, et al.
Publicado: (2025)

MarsCode Agent: AI-native Automated Bug Fixing
por: Liu, Yizhou, et al.
Publicado: (2024)

Automated Repair of AI Code with Large Language Models and Formal Verification
por: Charalambous, Yiannis, et al.
Publicado: (2024)

PyResBugs: A Dataset of Residual Python Bugs for Natural Language-Driven Fault Injection
por: Cotroneo, Domenico, et al.
Publicado: (2025)

Bug Analysis Towards Bug Resolution Time Prediction
por: Ozkan, Hasan Yagiz, et al.
Publicado: (2024)

Multi-Task Program Error Repair and Explanatory Diagnosis
por: Xu, Zhenyu, et al.
Publicado: (2024)

BugBlitz-AI: An Intelligent QA Assistant
por: Yao, Yi, et al.
Publicado: (2024)

A Survey of Bugs in AI-Generated Code
por: Gao, Ruofan, et al.
Publicado: (2025)

AEGIS: An Agent-based Framework for General Bug Reproduction from Issue Descriptions
por: Wang, Xinchen, et al.
Publicado: (2024)

Memory-Efficient Large Language Models for Program Repair with Semantic-Guided Patch Generation
por: Le-Cong, Thanh, et al.
Publicado: (2024)

Reducing False Positives in Static Bug Detection with LLMs: An Empirical Study in Industry
por: Du, Xueying, et al.
Publicado: (2026)

Automated Bug Report Prioritization in Large Open-Source Projects
por: Pierson, Riley, et al.
Publicado: (2025)

LogLLM: Log-based Anomaly Detection Using Large Language Models
por: Guan, Wei, et al.
Publicado: (2024)

Agora: Toward Autonomous Bug Detection in Production-Level Consensus Protocols with LLM Agents
por: Liu, Xiang, et al.
Publicado: (2026)

An Empirical Study on LLM-based Agents for Automated Bug Fixing
por: Meng, Xiangxin, et al.
Publicado: (2024)

Empirical Analysis and Detection of Hallucinations in LLM-Generated Bug Report Summaries
por: Nirujan, Hinduja, et al.
Publicado: (2026)

HLSDebugger: Identification and Correction of Logic Bugs in HLS Code with LLM Solutions
por: Wang, Jing, et al.
Publicado: (2025)

VulDetectBench: Evaluating the Deep Capability of Vulnerability Detection with Large Language Models
por: Liu, Yu, et al.
Publicado: (2024)

Bug Destiny Prediction in Large Open-Source Software Repositories through Sentiment Analysis and BERT Topic Modeling
por: Pope, Sophie C., et al.
Publicado: (2025)

Adaptive Confidence Gating in Multi-Agent Collaboration for Efficient and Optimized Code Generation
por: Zhang, Haoji, et al.
Publicado: (2026)

From Empirical Evaluation to Context-Aware Enhancement: Repairing Regression Errors with LLMs
por: Ho, Anh, et al.
Publicado: (2025)

Can GPT-O1 Kill All Bugs? An Evaluation of GPT-Family LLMs on QuixBugs
por: Hu, Haichuan, et al.
Publicado: (2024)

ComBench: A Repo-level Real-world Benchmark for Compilation Error Repair
por: Li, Jia, et al.
Publicado: (2026)

Go-Oracle: Automated Test Oracle for Go Concurrency Bugs
por: Tsimpourlas, Foivos, et al.
Publicado: (2024)