Image de couverture de livre

Evaluating Pre-Trained Models for Multi-Language Vulnerability Patching

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Khan, Zanis Ali, Garg, Aayush, Guo, Yuejun, Tang, Qiang
Format:	Preprint
Publié:	2025
Sujets:	Software Engineering
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

A Multi-Dataset Evaluation of Models for Automated Vulnerability Repair
par: Khan, Zanis Ali, et autres
Publié: (2025)

Evaluating LLMs for One-Shot Patching of Real and Artificial Vulnerabilities
par: Garg, Aayush, et autres
Publié: (2025)

An Empirical Study of the Imbalance Issue in Software Vulnerability Detection
par: Guo, Yuejun, et autres
Publié: (2026)

Impact of Log Parsing on Deep Learning-Based Anomaly Detection
par: Khan, Zanis Ali, et autres
Publié: (2023)

Patch-CLIP: A Patch-Text Pre-Trained Model
par: Tang, Xunzhu, et autres
Publié: (2023)

Learning Generalizable Multimodal Representations for Software Vulnerability Detection
par: Dong, Zeming, et autres
Publié: (2026)

How the Training Procedure Impacts the Performance of Deep Learning-based Vulnerability Patching
par: Mastropaolo, Antonio, et autres
Publié: (2024)

VulnLLMEval: A Framework for Evaluating Large Language Models in Software Vulnerability Detection and Patching
par: Zibaeirad, Arastoo, et autres
Publié: (2024)

Patch Validation in Automated Vulnerability Repair
par: Yu, Zheng, et autres
Publié: (2026)

DFEPT: Data Flow Embedding for Enhancing Pre-Trained Model Based Vulnerability Detection
par: Jiang, Zhonghao, et autres
Publié: (2024)

Enhancing Pre-Trained Language Models for Vulnerability Detection via Semantic-Preserving Data Augmentation
par: Qi, Weiliang, et autres
Publié: (2024)

On the Evaluation of Large Language Models in Multilingual Vulnerability Repair
par: wang, Dong, et autres
Publié: (2025)

PATCHEVAL: A New Benchmark for Evaluating LLMs on Patching Real-World Vulnerabilities
par: Wei, Zichao, et autres
Publié: (2025)

Pre-training by Predicting Program Dependencies for Vulnerability Analysis Tasks
par: Liu, Zhongxin, et autres
Publié: (2024)

Benchmarking Large Language Models for Multi-Language Software Vulnerability Detection
par: Zhang, Ting, et autres
Publié: (2025)

Evaluating Large Language Models for Multilingual Vulnerability Detection at Dual Granularities
par: Shu, Honglin, et autres
Publié: (2025)

Ensembling Large Language Models for Code Vulnerability Detection: An Empirical Evaluation
par: Sun, Zhihong, et autres
Publié: (2025)

PatchGuru: Patch Oracle Inference from Natural Language Artifacts with Large Language Models
par: Le-Cong, Thanh, et autres
Publié: (2026)

SBAN: A Framework & Multi-Dimensional Dataset for Large Language Model Pre-Training and Software Code Mining
par: Jelodar, Hamed, et autres
Publié: (2025)

Revisiting Vulnerability Patch Localization: An Empirical Study and LLM-Based Solution
par: Xu, Haoran, et autres
Publié: (2025)

Improving Data Curation of Software Vulnerability Patches through Uncertainty Quantification
par: Chen, Hui, et autres
Publié: (2024)

APPATCH: Automated Adaptive Prompting Large Language Models for Real-World Software Vulnerability Patching
par: Nong, Yu, et autres
Publié: (2024)

Learning Graph-based Patch Representations for Identifying and Assessing Silent Vulnerability Fixes
par: Han, Mei, et autres
Publié: (2024)

Revisiting Vulnerability Patch Identification on Data in the Wild
par: Irsan, Ivana Clairine, et autres
Publié: (2026)

VulGuard: An Unified Tool for Evaluating Just-In-Time Vulnerability Prediction Models
par: Nguyen, Duong, et autres
Publié: (2025)

APPT: Boosting Automated Patch Correctness Prediction via Fine-tuning Pre-trained Models
par: Zhang, Quanjun, et autres
Publié: (2023)

Red Teaming Program Repair Agents: When Correct Patches can Hide Vulnerabilities
par: Chen, Simin, et autres
Publié: (2025)

Characterizing Multi-Hunk Patches: Divergence, Proximity, and LLM Repair Challenges
par: Nashid, Noor, et autres
Publié: (2025)

Toward Realistic Evaluations of Just-In-Time Vulnerability Prediction
par: Nguyen, Duong, et autres
Publié: (2025)

Cracks in The Stack: Hidden Vulnerabilities and Licensing Risks in LLM Pre-Training Datasets
par: Jahanshahi, Mahmoud, et autres
Publié: (2025)

Evaluating Large Language Models for Line-Level Vulnerability Localization
par: Zhang, Jian, et autres
Publié: (2024)

A Case Study of LLM for Automated Vulnerability Repair: Assessing Impact of Reasoning and Patch Validation Feedback
par: Kulsum, Ummay, et autres
Publié: (2024)

Towards Explainable Vulnerability Detection with Large Language Models
par: Mao, Qiheng, et autres
Publié: (2024)

An Empirical Evaluation of Pre-trained Large Language Models for Repairing Declarative Formal Specifications
par: Alhanahnah, Mohannad, et autres
Publié: (2024)

M2CVD: Enhancing Vulnerability Semantic through Multi-Model Collaboration for Code Vulnerability Detection
par: Wang, Ziliang, et autres
Publié: (2024)

VulnRepairEval: An Exploit-Based Evaluation Framework for Assessing Large Language Model Vulnerability Repair Capabilities
par: Wang, Weizhe, et autres
Publié: (2025)

Coding-PTMs: How to Find Optimal Code Pre-trained Models for Code Embedding in Vulnerability Detection?
par: Zhao, Yu, et autres
Publié: (2024)

StagedVulBERT: Multi-Granular Vulnerability Detection with a Novel Pre-trained Code Model
par: Jiang, Yuan, et autres
Publié: (2024)

A Survey of Code Review Benchmarks and Evaluation Practices in Pre-LLM and LLM Era
par: Khan, Taufiqul Islam, et autres
Publié: (2026)

Multi-Agent End-to-End Vulnerability Management for Mitigating Recurring Vulnerabilities
par: Zheng, Zelong, et autres
Publié: (2026)