activity
20182023
most citedChallenging Machine Learning-based Clone Detectors via Semantic-preserving Code Transformations

46 citations · 117 across the 12 of their papers we have counts for

collaborators

12 papers

cs.SE2023

Eunomia: Enabling User-specified Fine-Grained Search in Symbolically Executing WebAssembly Binaries

Ningyu He, Zhehao Zhao, Jikai Wang +7

Although existing techniques have proposed automated approaches to alleviate the path explosion problem of symbolic execution, users still need to optimize symbolic execution by ap…

cs.CR2022★ 1 cited

Understanding Concurrency Vulnerabilities in Linux Kernel

Zunchen Huang, Shengjian Guo, Meng Wu +1

While there is a large body of work on analyzing concurrency related software bugs and developing techniques for detecting and patching them, little attention has been given to con…

cs.SE2022★ 6 cited

A Survey on Automated Driving System Testing: Landscapes and Trends

Shuncheng Tang, Zhenya Zhang, Yi Zhang +8

Automated Driving Systems (ADS) have made great achievements in recent years thanks to the efforts from both academia and industry. A typical ADS is composed of multiple modules, i…

cs.CR2022★ 15 cited

SoK: On the Semantic AI Security in Autonomous Driving

Junjie Shen, Ningfei Wang, Ziwen Wan +10

Autonomous Driving (AD) systems rely on AI components to make safety and correct driving decisions. Unfortunately, today's AI algorithms are known to be generally vulnerable to adv…

cs.SE2021★ 46 cited

Challenging Machine Learning-based Clone Detectors via Semantic-preserving Code Transformations

Weiwei Zhang, Shengjian Guo, Hongyu Zhang +3

Software clone detection identifies similar code snippets. It has been an active research topic that attracts extensive attention over the last two decades. In recent years, machin…

cs.RO2021

Detecting Multi-Sensor Fusion Errors in Advanced Driver-Assistance Systems

Ziyuan Zhong, Zhisheng Hu, Shengjian Guo +3

Advanced Driver-Assistance Systems (ADAS) have been thriving and widely deployed in recent years. In general, these systems receive sensor data, compute driving decisions, and outp…

cs.AI2021★ 12 cited

Coverage-based Scene Fuzzing for Virtual Autonomous Driving Testing

Zhisheng Hu, Shengjian Guo, Zhenyu Zhong +1

Simulation-based virtual testing has become an essential step to ensure the safety of autonomous driving systems. Testers need to handcraft the virtual driving scenes and configure…

cs.SE2020★ 18 cited

MUZZ: Thread-aware Grey-box Fuzzing for Effective Bug Hunting in Multithreaded Programs

Hongxu Chen, Shengjian Guo, Yinxing Xue +5

Grey-box fuzz testing has revealed thousands of vulnerabilities in real-world software owing to its lightweight instrumentation, fast coverage feedback, and dynamic adjusting strat…

cs.CR2019★ 3 cited

SpecuSym: Speculative Symbolic Execution for Cache Timing Leak Detection

Shengjian Guo, Yueqi Chen, Peng Li +4

CPU cache is a limited but crucial storage component in modern processors, whereas the cache timing side-channel may inadvertently leak information through the physically measurabl…

cs.SE2019★ 4 cited

SAVIOR: Towards Bug-Driven Hybrid Testing

Yaohui Chen, Peng Li, Jun Xu +5

Hybrid testing combines fuzz testing and concolic execution. It leverages fuzz testing to test easy-to-reach code regions and uses concolic execution to explore code blocks guarded…

cs.CR2018★ 9 cited

Eliminating Timing Side-Channel Leaks using Program Repair

Meng Wu, Shengjian Guo, Patrick Schaumont +1

We propose a method, based on program analysis and transformation, for eliminating timing side channels in software code that implements security-critical applications. Our method…

cs.CR2018★ 3 cited

Adversarial Symbolic Execution for Detecting Concurrency-Related Cache Timing Leaks

Shengjian Guo, Meng Wu, Chao Wang

The timing characteristics of cache, a high-speed storage between the fast CPU and the slowmemory, may reveal sensitive information of a program, thus allowing an adversary to cond…