21 citations · 50 across the 9 of their papers we have counts for
12 papers
Muffin: Testing Deep Learning Libraries via Neural Architecture Fuzzing
Jiazhen Gu, Xuchuan Luo, Yangfan Zhou +1
Deep learning (DL) techniques are proven effective in many challenging tasks, and become widely-adopted in practice. However, previous work has shown that DL libraries, the basis o…
SafeDrop: Detecting Memory Deallocation Bugs of Rust Programs via Static Data-Flow Analysis
Mohan Cui, Chengjun Chen, Hui Xu +1
Rust is an emerging programming language that aims to prevent memory-safety bugs. However, the current design of Rust also brings side effects which may increase the risk of memory…
RULF: Rust Library Fuzzing via API Dependency Graph Traversal
Jianfeng Jiang, Hui Xu, Yangfan Zhou
Robustness is a key concern for Rust library development because Rust promises no risks of undefined behaviors if developers use safe APIs only. Fuzzing is a practical approach for…
Fast Outage Analysis of Large-scale Production Clouds with Service Correlation Mining
Yaohui Wang, Guozheng Li, Zijian Wang +13
Cloud-based services are surging into popularity in recent years. However, outages, i.e., severe incidents that always impact multiple services, can dramatically affect user experi…
Memory-Safety Challenge Considered Solved? An In-Depth Study with All Rust CVEs
Hui Xu, Zhuangbin Chen, Mingshen Sun +2
Rust is an emerging programing language that aims at preventing memory-safety bugs without sacrificing much efficiency. The claimed property is very attractive to developers, and m…
Detecting Deep Neural Network Defects with Data Flow Analysis
Jiazhen Gu, Huanlin Xu, Yangfan Zhou +3
Deep neural networks (DNNs) are shown to be promising solutions in many challenging artificial intelligence tasks. However, it is very hard to figure out whether the low precision…