2 citations · 2 across the 6 of their papers we have counts for
5 papers · 1 filter
LLM-Powered Silent Bug Fuzzing in Deep Learning Libraries via Versatile and Controlled Bug Transfer
Kunpeng Zhang, Dongwei Xiao, Daoyuan Wu +5
Deep learning (DL) libraries are widely used in critical applications, where even subtle silent bugs can lead to serious consequences. While existing DL fuzzing techniques have mad…
Scheduzz: Constraint-based Fuzz Driver Generation with Dual Scheduling
Yan Li, Wenzhang Yang, Yuekun Wang +4
Fuzzing a library requires experts to understand the library usage well and craft high-quality fuzz drivers, which is tricky and tedious. Therefore, many techniques have been propo…
Ensemble Fuzzing with Dynamic Resource Scheduling and Multidimensional Seed Evaluation
Yukai Zhao, Shaohua Wang, Jue Wang +2
Fuzzing is widely used for detecting bugs and vulnerabilities, with various techniques proposed to enhance its effectiveness. To combine the advantages of multiple technologies, re…
SelfPiCo: Self-Guided Partial Code Execution with LLMs
Zhipeng Xue, Zhipeng Gao, Shaohua Wang +3
Code executability plays a vital role in software debugging and testing (e.g., detecting runtime exceptions or assertion violations). However, code execution, especially partial or…
Back to the Future! Studying Data Cleanness in Defects4J and its Impact on Fault Localization
Md Nakhla Rafi, An Ran Chen, Tse-Hsun Chen +1
For software testing research, Defects4J stands out as the primary benchmark dataset, offering a controlled environment to study real bugs from prominent open-source systems. Howev…