9 citations · 10 across the 5 of their papers we have counts for
6 papers
DepRadar: Agentic Coordination for Context Aware Defect Impact Analysis in Deep Learning Libraries
Yi Gao, Xing Hu, Tongtong Xu +3
Deep learning libraries like Transformers and Megatron are now widely adopted in modern AI programs. However, when these libraries introduce defects, ranging from silent computatio…
An Empirical Study of Vulnerable Package Dependencies in LLM Repositories
Shuhan Liu, Xing Hu, Xin Xia +2
Large language models (LLMs) have developed rapidly in recent years, revolutionizing various fields. Despite their widespread success, LLMs heavily rely on external code dependenci…
CREME: Robustness Enhancement of Code LLMs via Layer-Aware Model Editing
Shuhan Liu, Xing Hu, Kerui Huang +3
Large language models (LLMs) have demonstrated impressive capabilities in code generation, where the natural language prompt plays a crucial role in conveying user intent to the mo…
Re-Evaluating Code LLM Benchmarks Under Semantic Mutation
Zhiyuan Pan, Xing Hu, Xin Xia +1
In the era of large language models (LLMs), code benchmarks have become an important research area in software engineering and are widely used by practitioners. These benchmarks ev…
Generating Mitigations for Downstream Projects to Neutralize Upstream Library Vulnerability
Zirui Chen, Xing Hu, Puhua Sun +2
Third-party libraries are essential in software development as they prevent the need for developers to recreate existing functionalities. However, vulnerabilities within these libr…
What Makes a Good TODO Comment?
Haoye Wang, Zhipeng Gao, Tingting Bi +4
Software development is a collaborative process that involves various interactions among individuals and teams. TODO comments in source code play a critical role in managing and co…