13 papers · 1 filter
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…
From Registry to Repository: How AI Agent Skills Are Written, Adapted, and Maintained
Haoyu Gao, Jai Lal Lulla, Hong Yi Lin +3
AI coding agents increasingly rely on skills: structured context bundles, typically a SKILLmd file with a YAML header and Markdown body, loaded on demand for domain knowledge, w…
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…
HalluJudge: A Reference-Free Hallucination Detection for Context Misalignment in Code Review Automation
Kla Tantithamthavorn, Hong Yi Lin, Patanamon Thongtanunam +3
Large Language models (LLMs) have shown strong capabilities in code review automation, such as review comment generation, yet they suffer from hallucinations -- where the generated…
AI Failures in the Eyes of the Downstream Developer: A First Look at Concerns, Practices, and Challenges
Haoyu Gao, Mansooreh Zahedi, Wenxin Jiang +3
With the advancement of AI models, more software systems are adopting AI as a component to facilitate automation. Pre-trained models (PTMs) have become a cornerstone of AI-based so…
What Types of Code Review Comments Do Developers Most Frequently Resolve?
Saul Goldman, Hong Yi Lin, Jirat Pasuksmit +11
Large language model (LLM)-powered code review automation tools have been introduced to generate code review comments. However, not all generated comments will drive code changes.…