4 citations · 6 across the 5 of their papers we have counts for
6 papers
After the Party: Growth, Governance, and Security Scanning in the OpenClaw Agent Skill Ecosystem
Yunpeng Xiong, Ting Zhang
AI agents increasingly act through agent skills, i.e., natural-language instructions, that direct a host agent toward shell, network, credential, file, and process actions, and pub…
"Go Home Copilot, You're Drunk": Understanding Developer Responses to Agent-Generated Code Review Comments
Shamse Tasnim Cynthia, Ratnadira Widyasari, Banani Roy +2
Code review is a critical quality assurance practice in software engineering development, and AI coding agents are increasingly generating review comments on pull requests. However…
Sifting the Noise: A Comparative Study of LLM Agents in Vulnerability False Positive Filtering
Yunpeng Xiong, Ting Zhang
In this paper, we present a comparative study of three state-of-the-art LLM-based agent frameworks, i.e., Aider, OpenHands, and SWE-agent, for vulnerability FP filtering. We evalua…
On Evaluating the Efficiency of Source Code Generated by LLMs
Changan Niu, Ting Zhang, Chuanyi Li +2
Recent years have seen the remarkable capabilities of large language models (LLMs) for code generation. Different from existing work that evaluate the correctness of the code gener…
Large Language Model for Vulnerability Detection: Emerging Results and Future Directions
Xin Zhou, Ting Zhang, David Lo
Previous learning-based vulnerability detection methods relied on either medium-sized pre-trained models or smaller neural networks from scratch. Recent advancements in Large Pre-T…
Explaining Explanation: An Empirical Study on Explanation in Code Reviews
Ratnadira Widyasari, Ting Zhang, Abir Bouraffa +2
Code reviews are central for software quality assurance. Ideally, reviewers should explain their feedback to enable authors of code changes to understand the feedback and act accor…