33 citations · 70 across the 9 of their papers we have counts for
10 papers
From Component Manipulation to System Compromise: Understanding and Detecting Malicious MCP Servers
Yiheng Huang, Zhijia Zhao, Bihuan Chen +5
The model context protocol (MCP) standardizes how LLMs connect to external tools and data sources, enabling faster integration but introducing new attack vectors. Despite the growi…
Bridging Generation and Training: A Systematic Review of Quality Issues in LLMs for Code
Kaifeng He, Xiaojun Zhang, Peiliang Cai +7
Large language models (LLMs) frequently generate defective code, ranging from logical bugs to security vulnerabilities. Training-data quality is one contributor to these outcomes a…
A general approach to enhance the survivability of backdoor attacks by decision path coupling
Yufei Zhao, Dingji Wang, Bihuan Chen +2
Backdoor attacks have been one of the emerging security threats to deep neural networks (DNNs), leading to serious consequences. One of the mainstream backdoor defenses is model re…
Exploring the Potential of ChatGPT in Automated Code Refinement: An Empirical Study
Qi Guo, Junming Cao, Xiaofei Xie +4
Code review is an essential activity for ensuring the quality and maintainability of software projects. However, it is a time-consuming and often error-prone task that can signific…
Killing Two Birds with One Stone: Malicious Package Detection in NPM and PyPI using a Single Model of Malicious Behavior Sequence
Junan Zhang, Kaifeng Huang, Yiheng Huang +4
Open-source software (OSS) supply chain enlarges the attack surface, which makes package registries attractive targets for attacks. Recently, package registries NPM and PyPI have b…
Scalable and Precise Application-Centered Call Graph Construction for Python
Kaifeng Huang, Yixuan Yan, Bihuan Chen +2
Call graph construction is the foundation of inter-procedural static analysis. PYCG is the state-of-the-art approach for constructing call graphs for Python programs. Unfortunately…
Understanding the Complexity and Its Impact on Testing in ML-Enabled Systems
Junming Cao, Bihuan Chen, Longjie Hu +3
Machine learning (ML) enabled systems are emerging with recent breakthroughs in ML. A model-centric view is widely taken by the literature to focus only on the analysis of ML model…
Demystifying Dependency Bugs in Deep Learning Stack
Kaifeng Huang, Bihuan Chen, Susheng Wu +3
Deep learning (DL) applications, built upon a heterogeneous and complex DL stack (e.g., Nvidia GPU, Linux, CUDA driver, Python runtime, and TensorFlow), are subject to software and…
Understanding Performance Problems in Deep Learning Systems
Junming Cao, Bihuan Chen, Chao Sun +3
Deep learning (DL) has been widely applied to many domains. Unique challenges in engineering DL systems are posed by the programming paradigm shift from traditional systems to DL s…
RegMiner: Towards Constructing a Large Regression Dataset from Code Evolution History
Xuezhi Song, Yun Lin, Siang Hwee Ng +4
Bug datasets consisting of real-world bugs are important artifacts for researchers and programmers, which lay empirical and experimental foundation for various SE/PL research such…