Image de couverture de livre

MoCo: Fuzzing Deep Learning Libraries via Assembling Code

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Ji, Pin, Feng, Yang, Wu, Duo, Yan, Lingyue, Chen, Pengling, Liu, Jia, Zhao, Zhihong
Format:	Preprint
Publié:	2024
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

Fuzzing Automatic Differentiation in Deep-Learning Libraries
par: Yang, Chenyuan, et autres
Publié: (2023)

May the Feedback Be with You! Unlocking the Power of Feedback-Driven Deep Learning Framework Fuzzing via LLMs
par: Yang, Shaoyu, et autres
Publié: (2025)

LLM-Powered Silent Bug Fuzzing in Deep Learning Libraries via Versatile and Controlled Bug Transfer
par: Zhang, Kunpeng, et autres
Publié: (2026)

Txt2Sce: Scenario Generation for Autonomous Driving System Testing Based on Textual Reports
par: Ji, Pin, et autres
Publié: (2025)

TransferFuzz: Fuzzing with Historical Trace for Verifying Propagated Vulnerability Code
par: Li, Siyuan, et autres
Publié: (2024)

The Seeds of the FUTURE Sprout from History: Fuzzing for Unveiling Vulnerabilities in Prospective Deep-Learning Libraries
par: Li, Zhiyuan, et autres
Publié: (2024)

XMutant: XAI-based Fuzzing for Deep Learning Systems
par: Chen, Xingcheng, et autres
Publié: (2025)

Evaluating the Effectiveness of Coverage-Guided Fuzzing for Testing Deep Learning Library APIs
par: Qin, Feiran, et autres
Publié: (2025)

FuzzAgent: Multi-Agent System for Evolutionary Library Fuzzing
par: Lyu, Yunlong, et autres
Publié: (2026)

Harnessing LLMs for Document-Guided Fuzzing of OpenCV Library
par: Duan, Bin, et autres
Publié: (2025)

Vulseye: Detect Smart Contract Vulnerabilities via Stateful Directed Graybox Fuzzing
par: Liang, Ruichao, et autres
Publié: (2024)

IRFuzzer: Specialized Fuzzing for LLVM Backend Code Generation
par: Rong, Yuyang, et autres
Publié: (2024)

MirrorFuzz: Leveraging LLM and Shared Bugs for Deep Learning Framework APIs Fuzzing
par: Ou, Shiwen, et autres
Publié: (2025)

Context-Aware Fuzzing for Robustness Enhancement of Deep Learning Models
par: Wang, Haipeng, et autres
Publié: (2024)

BASFuzz: Towards Robustness Evaluation of LLM-based NLP Software via Automated Fuzz Testing
par: Xiao, Mingxuan, et autres
Publié: (2025)

IRCoCo: Immediate Rewards-Guided Deep Reinforcement Learning for Code Completion
par: Li, Bolun, et autres
Publié: (2024)

Enhancing Code Review through Fuzzing and Likely Invariants
par: Charoenwet, Wachiraphan, et autres
Publié: (2025)

Mut4All: Fuzzing Compilers via LLM-Synthesized Mutators Learned from Bug Reports
par: Wang, Bo, et autres
Publié: (2025)

GraphFuzz: Automated Testing of Graph Algorithm Implementations with Differential Fuzzing and Lightweight Feedback
par: Yan, Wenqi, et autres
Publié: (2025)

CKGFuzzer: LLM-Based Fuzz Driver Generation Enhanced By Code Knowledge Graph
par: Xu, Hanxiang, et autres
Publié: (2024)

Scalpel: Automotive Deep Learning Framework Testing via Assembling Model Components
par: Zou, Yinglong, et autres
Publié: (2025)

MicroFuzz: An Efficient Fuzzing Framework for Microservices
par: Di, Peng, et autres
Publié: (2024)

Subgraph-Oriented Testing for Deep Learning Libraries
par: Xie, Xiaoyuan, et autres
Publié: (2024)

Prompt Fuzzing for Fuzz Driver Generation
par: Lyu, Yunlong, et autres
Publié: (2023)

On the Challenges of Fuzzing Techniques via Large Language Models
par: Huang, Linghan, et autres
Publié: (2024)

Selecting Initial Seeds for Better JVM Fuzzing
par: Gao, Tianchang, et autres
Publié: (2024)

CodeImprove: Program Adaptation for Deep Code Models
par: Rathnasuriya, Ravishka, et autres
Publié: (2025)

SAFuzz: Semantic-Guided Adaptive Fuzzing for LLM-Generated Code
par: Yang, Ziyi, et autres
Publié: (2026)

Fuzz Smarter, Not Harder: Towards Greener Fuzzing with GreenAFL
par: Ercevik, Ayse Irmak, et autres
Publié: (2025)

WuppieFuzz: Coverage-Guided, Stateful REST API Fuzzing
par: Rooijakkers, Thomas, et autres
Publié: (2025)

Following Dragons: Code Review-Guided Fuzzing
par: Luu, Viet Hoang, et autres
Publié: (2026)

Fuzz4All: Universal Fuzzing with Large Language Models
par: Xia, Chunqiu Steven, et autres
Publié: (2023)

Uses of Active and Passive Learning in Stateful Fuzzing
par: Daniele, Cristian, et autres
Publié: (2024)

Triggering and Detecting Exploitable Library Vulnerability from the Client by Directed Greybox Fuzzing
par: Zhao, Yukai, et autres
Publié: (2026)

CC2Vec: Combining Typed Tokens with Contrastive Learning for Effective Code Clone Detection
par: Dou, Shihan, et autres
Publié: (2024)

S$^2$F: Principled Hybrid Testing With Fuzzing, Symbolic Execution, and Sampling
par: Wang, Lianjing, et autres
Publié: (2026)

Sifting through the Chaff: On Utilizing Execution Feedback for Ranking the Generated Code Candidates
par: Sun, Zhihong, et autres
Publié: (2024)

A Differential Fuzzing-Based Evaluation of Functional Equivalence in LLM-Generated Code Refactorings
par: Dristi, Simantika Bhattacharjee, et autres
Publié: (2026)

Learning-Guided Fuzzing for Testing Stateful SDN Controllers
par: Ollando, Raphaël, et autres
Publié: (2024)

Program Environment Fuzzing
par: Meng, Ruijie, et autres
Publié: (2024)