1 citations · 2 across the 5 of their papers we have counts for
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Answer Is Cheap, Show Me the Evidence! Augmenting Automated Vulnerability Assessment with Evidence
Shengyi Pan, Zelong Zheng, Jiayuan Zhou +3
Software vulnerability (SV) assessment helps prioritize remediation by characterizing reported vulnerabilities. Existing automated methods predict assessment results from SV report…
Mitigating Package Hallucinations in Large Language Models via Model Editing
Shuhan Liu, Yukai Zhao, Xing Hu +3
Large language models (LLMs) have demonstrated strong capabilities in software engineering tasks, such as code generation, library recommendation, and dependency configuration. How…
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…
An Empirical Study of Retrieval-Augmented Code Generation: Challenges and Opportunities
Zezhou Yang, Sirong Chen, Cuiyun Gao +4
Code generation aims to automatically generate code snippets of specific programming language according to natural language descriptions. The continuous advancements in deep learni…
Similar but Patched Code Considered Harmful -- The Impact of Similar but Patched Code on Recurring Vulnerability Detection and How to Remove Them
Zixuan Tan, Jiayuan Zhou, Xing Hu +3
Identifying recurring vulnerabilities is crucial for ensuring software security. Clone-based techniques, while widely used, often generate many false alarms due to the existence of…
Towards Explainable Vulnerability Detection with Large Language Models
Qiheng Mao, Zhenhao Li, Xing Hu +3
Software vulnerabilities pose significant risks to the security and integrity of software systems. Although prior studies have explored vulnerability detection using deep learning…