collaborators

6 papers

cs.SE2026

TrajAudit: Automated Failure Diagnosis for Agentic Coding Systems

Minxing Wang, Xiaofei Xie, Yintong Huo

Agentic systems have been widely studied to automate coding tasks such as bug fixing and feature implementation. As these systems increasingly operate on complex codebases, underst…

cs.SE2026

Towards Demystifying and Repairing LLM-in-the-Loop Vulnerabilities

Yujie Ma, Jialin Rong, Chenxi Yang +5

Large Language Models (LLMs) have been actively integrated into modern software systems as critical components, introducing a new type of software vulnerability, LLM-in-the-Loop (L…

cs.SE2026

A11YRepair: Bridging Web Accessibility Barriers via Knowledge-Enhanced Divide-and-Conquer Repair

Kai Huang, Ling Zhu, Jian Zhang +2

Web accessibility (A11Y), which ensures web content is perceivable and usable for users with disabilities, is a critical requirement for modern web applications. Yet existing tooli…

cs.SE2026

Search-Induced Issues in Web-Augmented LLM Code Generation: Detecting and Repairing Error-Inducing Pages

Guoqing Wang, Zeyu Sun, Xiaofei Xie +4

Web-augmented large language models (LLMs) offer promising capabilities for automatic code generation. However, integrating live web search exposes models to unreliable or maliciou…

cs.CR2026

What Makes a Good LLM Agent for Real-world Penetration Testing?

Gelei Deng, Yi Liu, Yuekang Li +5

LLM-based agents show promise for automating penetration testing, yet reported performance varies widely across systems and benchmarks. We analyze 28 LLM-based penetration testing…

cs.SE2025

Bias Testing and Mitigation in LLM-based Code Generation

Dong Huang, Jie M. Zhang, Qingwen Bu +3

As the adoption of LLMs becomes more widespread in software coding ecosystems, a pressing issue has emerged: does the generated code contain social bias and unfairness, such as tho…