9 papers
Requirements-Augmented Generation for Trustworthy Acceptance Testing of LLM-Based Software
Fanyu Wang, Chetan Arora, Zhenping Xie +4
LLM-based software (LBS) integrates large language models as core components to deliver flexible, personalised responses. Unlike traditional software with deterministic outputs, LB…
Towards a Risk Assessment of Malicious Skill Files in Coding Agents
Rui Yang, Michael Fu, Kla Tantithamthavorn +2
Autonomous coding agents are increasingly embedded in enterprise software workflows with delegated authority over connected systems. Central to this architecture is the agent skill…
AgenticRepair: Multi-Faceted Program Context Engineering for Agentic Vulnerability Repair
Michael Fu, Qiyue Mei, Patanamon Thongtanunam +1
Automated vulnerability repair aims to reduce the time and effort required to patch security flaws from a vulnerability triage report. Recent agentic AI approaches have shown promi…
SkillGate: Cost Efficient Runtime Malicious Skill File Detection in Coding Agents
Rui Yang, Michael Fu, Kla Tantithamthavorn +2
Software engineering teams now deploy AI coding agents (Cursor, Claude Code, GitHub Copilot) as first-class productivity tools, installing domain-specific skill files to tailor age…
Agentic Code Review in the Terminal: A Trajectory-Level Analysis of Behavior, Cost, and Human-Alignment
Wachiraphan Charoenwet, Kla Tantithamthavorn, Patanamon Thongtanunam +3
Agentic code review in terminal-based environments enables early feedback during local development before pull request creation. However, existing evaluations remain performance-ce…
Is Agentic Code Review Helpful? Mining Developers' Feedback to CodeRabbit Reviews in the Wild
Hong Yi Lin, Mingzhao Liang, Kla Tantithamthavorn +1
Agentic code review, where autonomous agents provide code review comments on pull requests, is increasingly integrated into development workflows, yet there is limited empirical ev…