2 citations · 6 across the 35 of their papers we have counts for
11 papers · 1 filter
Actionable Warning Is Not Enough: Recommending Valid Actionable Warnings with Weak Supervision
Zhipeng Xue, Zhipeng Gao, Tongtong Xu +3
The use of static analysis tools has gained increasing popularity among developers in the last few years. However, the widespread adoption of static analysis tools is hindered by t…
Don't Use a Cannon to Kill a Fly: Lightweight Model Editing for LLMs to Correct Deprecated API Recommendations
Guancheng Lin, Xiao Yu, Jacky Keung +3
Pre-trained or fine-tuned on large code corpora, Large Language Models (LLMs) have demonstrated strong performance in code completion tasks. However, their embedded knowledge is co…
Diffploit: Facilitating Cross-Version Exploit Migration for Open Source Library Vulnerabilities
Zirui Chen, Zhipeng Xue, Jiayuan Zhou +3
Exploits are commonly used to demonstrate the presence of library vulnerabilities and validate their impact across different versions. However, their direct application to alternat…
HFuzzer: Testing Large Language Models for Package Hallucinations via Phrase-based Fuzzing
Yukai Zhao, Menghan Wu, Xing Hu +1
Large Language Models (LLMs) are widely used for code generation, but they face critical security risks when applied to practical production due to package hallucinations, in which…
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…
Clean Code, Better Models: Enhancing LLM Performance with Smell-Cleaned Dataset
Zhipeng Xue, Xiaoting Zhang, Zhipeng Gao +4
The Large Language Models (LLMs) have demonstrated great potential in code-related tasks. However, most research focuses on improving the output quality of LLMs (e.g., correctness)…
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…
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…
Towards Understanding Bugs in Distributed Training and Inference Frameworks for Large Language Models
Xiao Yu, Haoxuan Chen, Feifei Niu +3
With the rapid development of large language models (LLMs), distributed training and inference frameworks like DeepSpeed have become essential for scaling model training and infere…
ActRef: Enhancing the Understanding of Python Code Refactoring with Action-Based Analysis
Siqi Wang, Xing Hu, Xin Xia +1
Refactoring, the process of improving the code structure of a software system without altering its behavior, is crucial for managing code evolution in software development. Identif…