Selecting Initial Seeds for Better JVM Fuzzing
Fuente:
arXiv
Saved in:
| Main Authors: | Gao, Tianchang, Chen, Junjie, Wang, Dong, Guo, Yile, Zhao, Yingquan, Wang, Zan |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
ISC4DGF: Enhancing Directed Grey-box Fuzzing with LLM-Driven Initial Seed Corpus Generation
by: Xu, Yijiang, et al.
Published: (2024)
by: Xu, Yijiang, et al.
Published: (2024)
Ensemble Fuzzing with Dynamic Resource Scheduling and Multidimensional Seed Evaluation
by: Zhao, Yukai, et al.
Published: (2025)
by: Zhao, Yukai, et al.
Published: (2025)
Characterizing and Mitigating False-Positive Bug Reports in the Linux Kernel
by: Tian, Jiashuo, et al.
Published: (2026)
by: Tian, Jiashuo, et al.
Published: (2026)
PatchFuzz: Patch Fuzzing for JavaScript Engines
by: Wang, Junjie, et al.
Published: (2025)
by: Wang, Junjie, et al.
Published: (2025)
TransferFuzz: Fuzzing with Historical Trace for Verifying Propagated Vulnerability Code
by: Li, Siyuan, et al.
Published: (2024)
by: Li, Siyuan, et al.
Published: (2024)
Peeling Off the Cocoon: Unveiling Suppressed Golden Seeds for Mutational Greybox Fuzzing
by: Qian, Ruixiang, et al.
Published: (2026)
by: Qian, Ruixiang, et al.
Published: (2026)
RandSet: Randomized Corpus Reduction for Fuzzing Seed Scheduling
by: Xie, Yuchong, et al.
Published: (2026)
by: Xie, Yuchong, et al.
Published: (2026)
Issue-Oriented Agent-Based Framework for Automated Review Comment Generation
by: Li, Shuochuan, et al.
Published: (2025)
by: Li, Shuochuan, et al.
Published: (2025)
MicroFuzz: An Efficient Fuzzing Framework for Microservices
by: Di, Peng, et al.
Published: (2024)
by: Di, Peng, et al.
Published: (2024)
Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR
by: Sun, Zeyu, et al.
Published: (2025)
by: Sun, Zeyu, et al.
Published: (2025)
Fixing Large Language Models' Specification Misunderstanding for Better Code Generation
by: Tian, Zhao, et al.
Published: (2023)
by: Tian, Zhao, et al.
Published: (2023)
Fuzzwise: Intelligent Initial Corpus Generation for Fuzzing
by: Dhulipala, Hridya, et al.
Published: (2025)
by: Dhulipala, Hridya, et al.
Published: (2025)
A Comprehensive Study of Deep Learning Model Fixing Approaches
by: You, Hanmo, et al.
Published: (2025)
by: You, Hanmo, et al.
Published: (2025)
The Seeds of the FUTURE Sprout from History: Fuzzing for Unveiling Vulnerabilities in Prospective Deep-Learning Libraries
by: Li, Zhiyuan, et al.
Published: (2024)
by: Li, Zhiyuan, et al.
Published: (2024)
Harnessing Large Language Models for Seed Generation in Greybox Fuzzing
by: Shi, Wenxuan, et al.
Published: (2024)
by: Shi, Wenxuan, et al.
Published: (2024)
Mut4All: Fuzzing Compilers via LLM-Synthesized Mutators Learned from Bug Reports
by: Wang, Bo, et al.
Published: (2025)
by: Wang, Bo, et al.
Published: (2025)
A Survey of Reinforcement Learning for Software Engineering
by: Wang, Dong, et al.
Published: (2025)
by: Wang, Dong, et al.
Published: (2025)
PathFuzzing: Worst Case Analysis by Fuzzing Symbolic-Execution Paths
by: Chen, Zimu, et al.
Published: (2025)
by: Chen, Zimu, et al.
Published: (2025)
Prompt Fuzzing for Fuzz Driver Generation
by: Lyu, Yunlong, et al.
Published: (2023)
by: Lyu, Yunlong, et al.
Published: (2023)
Optimizing Knowledge Utilization for Multi-Intent Comment Generation with Large Language Models
by: Li, Shuochuan, et al.
Published: (2025)
by: Li, Shuochuan, et al.
Published: (2025)
Refining Fuzzed Crashing Inputs for Better Fault Diagnosis
by: Kim, Kieun, et al.
Published: (2025)
by: Kim, Kieun, et al.
Published: (2025)
FuzzAug: Data Augmentation by Coverage-guided Fuzzing for Neural Test Generation
by: He, Yifeng, et al.
Published: (2024)
by: He, Yifeng, et al.
Published: (2024)
SoK: Where to Fuzz? Assessing Target Selection Methods in Directed Fuzzing
by: Weissberg, Felix, et al.
Published: (2025)
by: Weissberg, Felix, et al.
Published: (2025)
S$^2$F: Principled Hybrid Testing With Fuzzing, Symbolic Execution, and Sampling
by: Wang, Lianjing, et al.
Published: (2026)
by: Wang, Lianjing, et al.
Published: (2026)
Testing Question Answering Software with Context-Driven Question Generation
by: Liu, Shuang, et al.
Published: (2025)
by: Liu, Shuang, et al.
Published: (2025)
Fuzz Smarter, Not Harder: Towards Greener Fuzzing with GreenAFL
by: Ercevik, Ayse Irmak, et al.
Published: (2025)
by: Ercevik, Ayse Irmak, et al.
Published: (2025)
WuppieFuzz: Coverage-Guided, Stateful REST API Fuzzing
by: Rooijakkers, Thomas, et al.
Published: (2025)
by: Rooijakkers, Thomas, et al.
Published: (2025)
BandFuzz: An ML-powered Collaborative Fuzzing Framework
by: Shi, Wenxuan, et al.
Published: (2025)
by: Shi, Wenxuan, et al.
Published: (2025)
LLAMA: Multi-Feedback Smart Contract Fuzzing Framework with LLM-Guided Seed Generation
by: Gai, Keke, et al.
Published: (2025)
by: Gai, Keke, et al.
Published: (2025)
Fuzzing MLIR Compilers with Custom Mutation Synthesis
by: Limpanukorn, Ben, et al.
Published: (2024)
by: Limpanukorn, Ben, et al.
Published: (2024)
Tracking the Evolution of Static Code Warnings: the State-of-the-Art and a Better Approach
by: Li, Junjie, et al.
Published: (2022)
by: Li, Junjie, et al.
Published: (2022)
Program Environment Fuzzing
by: Meng, Ruijie, et al.
Published: (2024)
by: Meng, Ruijie, et al.
Published: (2024)
LLM for Mobile: An Initial Roadmap
by: Chen, Daihang, et al.
Published: (2024)
by: Chen, Daihang, et al.
Published: (2024)
FuzzAgent: Multi-Agent System for Evolutionary Library Fuzzing
by: Lyu, Yunlong, et al.
Published: (2026)
by: Lyu, Yunlong, et al.
Published: (2026)
INSTILLER: Towards Efficient and Realistic RTL Fuzzing
by: Zhang, Gen, et al.
Published: (2024)
by: Zhang, Gen, et al.
Published: (2024)
On the Effectiveness of Training Data Optimization for LLM-based Code Generation: An Empirical Study
by: Kuang, Shiqi, et al.
Published: (2025)
by: Kuang, Shiqi, et al.
Published: (2025)
MoCo: Fuzzing Deep Learning Libraries via Assembling Code
by: Ji, Pin, et al.
Published: (2024)
by: Ji, Pin, et al.
Published: (2024)
Context-Aware Fuzzing for Robustness Enhancement of Deep Learning Models
by: Wang, Haipeng, et al.
Published: (2024)
by: Wang, Haipeng, et al.
Published: (2024)
BiFuzz: A Two-Stage Fuzzing Tool for Open-World Video Games
by: Kato, Yusaku, et al.
Published: (2025)
by: Kato, Yusaku, et al.
Published: (2025)
GraphFuzz: Automated Testing of Graph Algorithm Implementations with Differential Fuzzing and Lightweight Feedback
by: Yan, Wenqi, et al.
Published: (2025)
by: Yan, Wenqi, et al.
Published: (2025)
Similar Items
-
ISC4DGF: Enhancing Directed Grey-box Fuzzing with LLM-Driven Initial Seed Corpus Generation
by: Xu, Yijiang, et al.
Published: (2024) -
Ensemble Fuzzing with Dynamic Resource Scheduling and Multidimensional Seed Evaluation
by: Zhao, Yukai, et al.
Published: (2025) -
Characterizing and Mitigating False-Positive Bug Reports in the Linux Kernel
by: Tian, Jiashuo, et al.
Published: (2026) -
PatchFuzz: Patch Fuzzing for JavaScript Engines
by: Wang, Junjie, et al.
Published: (2025) -
TransferFuzz: Fuzzing with Historical Trace for Verifying Propagated Vulnerability Code
by: Li, Siyuan, et al.
Published: (2024)