Cover Image

Harnessing LLMs for Document-Guided Fuzzing of OpenCV Library

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Duan, Bin, Mahmud, Tarek, Che, Meiru, Yan, Yan, Dong, Naipeng, Kim, Dan Dongseong, Yang, Guowei
Format:	Preprint
Published:	2025
Subjects:	Software Engineering
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

XAMT: Cross-Framework API Matching for Testing Deep Learning Libraries
by: Duan, Bin, et al.
Published: (2025)

Automated Update of Android Deprecated API Usages with Large Language Models
by: Mahmud, Tarek, et al.
Published: (2024)

Enhancing LLM Code Generation with Ensembles: A Similarity-Based Selection Approach
by: Mahmud, Tarek, et al.
Published: (2025)

CONCUR: Benchmarking LLMs for Concurrent Code Generation
by: Huang, Jue, et al.
Published: (2026)

Coverage-Guided Multi-Agent Harness Generation for Java Library Fuzzing
by: Loose, Nils, et al.
Published: (2026)

ClozeMaster: Fuzzing Rust Compiler by Harnessing LLMs for Infilling Masked Real Programs
by: Gao, Hongyan, et al.
Published: (2026)

MoCo: Fuzzing Deep Learning Libraries via Assembling Code
by: Ji, Pin, et al.
Published: (2024)

WuppieFuzz: Coverage-Guided, Stateful REST API Fuzzing
by: Rooijakkers, Thomas, et al.
Published: (2025)

GraphFuzz: Automated Testing of Graph Algorithm Implementations with Differential Fuzzing and Lightweight Feedback
by: Yan, Wenqi, et al.
Published: (2025)

Hunting CUDA Bugs at Scale with cuFuzz
by: ziad, Mohamed Tarek Ibn, et al.
Published: (2026)

HarnessAgent: Scaling Automatic Fuzzing Harness Construction with Tool-Augmented LLM Pipelines
by: Yang, Kang, et al.
Published: (2025)

BiFuzz: A Two-Stage Fuzzing Tool for Open-World Video Games
by: Kato, Yusaku, et al.
Published: (2025)

DocTer: Documentation Guided Fuzzing for Testing Deep Learning API Functions
by: Xie, Danning, et al.
Published: (2021)

Evaluating the Effectiveness of Coverage-Guided Fuzzing for Testing Deep Learning Library APIs
by: Qin, Feiran, et al.
Published: (2025)

Generating Realistic, Diverse, and Fault-Revealing Inputs with Latent Space Interpolation for Testing Deep Neural Networks
by: Duan, Bin, et al.
Published: (2025)

Fuzzing Automatic Differentiation in Deep-Learning Libraries
by: Yang, Chenyuan, et al.
Published: (2023)

Perception-Guided Fuzzing for Simulated Scenario-Based Testing of Autonomous Driving Systems
by: Pham, Tri Minh Triet, et al.
Published: (2024)

Learning-Guided Fuzzing for Testing Stateful SDN Controllers
by: Ollando, Raphaël, et al.
Published: (2024)

TransferFuzz: Fuzzing with Historical Trace for Verifying Propagated Vulnerability Code
by: Li, Siyuan, et al.
Published: (2024)

Harnessing Large Language Models for Seed Generation in Greybox Fuzzing
by: Shi, Wenxuan, et al.
Published: (2024)

Fuzzing REST APIs in Industry: Necessary Features and Open Problems
by: Arcuri, Andrea, et al.
Published: (2026)

AFLNet Five Years Later: On Coverage-Guided Protocol Fuzzing
by: Meng, Ruijie, et al.
Published: (2024)

Towards Large Language Model Guided Kernel Direct Fuzzing
by: Li, Xie, et al.
Published: (2025)

MicroFuzz: An Efficient Fuzzing Framework for Microservices
by: Di, Peng, et al.
Published: (2024)

Quality-Assured Fuzz Harness Generation via the Four Principles Framework
by: Sheng, Ze, et al.
Published: (2026)

An Empirical Study of Fuzz Harness Degradation
by: Görz, Philipp, et al.
Published: (2025)

LLM4Fuzz: Guided Fuzzing of Smart Contracts with Large Language Models
by: Shou, Chaofan, et al.
Published: (2024)

FuzzAgent: Multi-Agent System for Evolutionary Library Fuzzing
by: Lyu, Yunlong, et al.
Published: (2026)

DynamiQ: Unlocking the Potential of Dynamic Task Allocation in Parallel Fuzzing
by: Yan, Wenqi, et al.
Published: (2025)

Attention Distance: A Novel Metric for Directed Fuzzing with Large Language Models
by: Bin, Wang, et al.
Published: (2025)

HF-DGF: Hybrid Feedback Guided Directed Grey-box Fuzzing
by: Lyu, Guangfa, et al.
Published: (2025)

Fuzz Smarter, Not Harder: Towards Greener Fuzzing with GreenAFL
by: Ercevik, Ayse Irmak, et al.
Published: (2025)

May the Feedback Be with You! Unlocking the Power of Feedback-Driven Deep Learning Framework Fuzzing via LLMs
by: Yang, Shaoyu, et al.
Published: (2025)

Selecting Initial Seeds for Better JVM Fuzzing
by: Gao, Tianchang, et al.
Published: (2024)

When Fuzzing Meets LLMs: Challenges and Opportunities
by: Jiang, Yu, et al.
Published: (2024)

FLARE: Agentic Coverage-Guided Fuzzing for LLM-Based Multi-Agent Systems
by: Hui, Mingxuan, et al.
Published: (2026)

SAFuzz: Semantic-Guided Adaptive Fuzzing for LLM-Generated Code
by: Yang, Ziyi, et al.
Published: (2026)

Fuzzing MLIR Compilers with Custom Mutation Synthesis
by: Limpanukorn, Ben, et al.
Published: (2024)

Following Dragons: Code Review-Guided Fuzzing
by: Luu, Viet Hoang, et al.
Published: (2026)

BASFuzz: Towards Robustness Evaluation of LLM-based NLP Software via Automated Fuzz Testing
by: Xiao, Mingxuan, et al.
Published: (2025)