11 citations · 28 across the 14 of their papers we have counts for
15 papers
Is Agentic Code Review Helpful? Mining Developers' Feedback to CodeRabbit Reviews in the Wild
Hong Yi Lin, Mingzhao Liang, Patanamon Thongtanunam +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…
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…
AgenticSCR: An Autonomous Agentic Secure Code Review for Immature Vulnerabilities Detection
Wachiraphan Charoenwet, Kla Tantithamthavorn, Patanamon Thongtanunam +3
Secure code review is critical during pre-integration, where Atlassian developers rely on lightweight analysis tools, while deep security assessment is deferred to later stages, de…
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…
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.…
Hallucinations in Code Change to Natural Language Generation: Prevalence and Evaluation of Detection Metrics
Chunhua Liu, Hong Yi Lin, Patanamon Thongtanunam
Language models have shown strong capabilities across a wide range of tasks in software engineering, such as code generation, yet they suffer from hallucinations. While hallucinati…
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…
CodeReviewQA: The Code Review Comprehension Assessment for Large Language Models
Hong Yi Lin, Chunhua Liu, Haoyu Gao +2
State-of-the-art large language models (LLMs) have demonstrated impressive code generation capabilities but struggle with real-world software engineering tasks, such as revising so…
Should Code Models Learn Pedagogically? A Preliminary Evaluation of Curriculum Learning for Real-World Software Engineering Tasks
Kyi Shin Khant, Hong Yi Lin, Patanamon Thongtanunam
Learning-based techniques, especially advanced pre-trained models for code have demonstrated capabilities in code understanding and generation, solving diverse software engineering…
Too Noisy To Learn: Enhancing Data Quality for Code Review Comment Generation
Chunhua Liu, Hong Yi Lin, Patanamon Thongtanunam
Code review is an important practice in software development, yet it is time-consuming and requires substantial effort. While open-source datasets have been used to train neural mo…
Leveraging Reviewer Experience in Code Review Comment Generation
Hong Yi Lin, Patanamon Thongtanunam, Christoph Treude +3
Modern code review is a ubiquitous software quality assurance process aimed at identifying potential issues within newly written code. Despite its effectiveness, the process demand…
Transferable Embedding Inversion Attack: Uncovering Privacy Risks in Text Embeddings without Model Queries
Yu-Hsiang Huang, Yuche Tsai, Hsiang Hsiao +2
This study investigates the privacy risks associated with text embeddings, focusing on the scenario where attackers cannot access the original embedding model. Contrary to previous…
Improving Automated Code Reviews: Learning from Experience
Hong Yi Lin, Patanamon Thongtanunam, Christoph Treude +1
Modern code review is a critical quality assurance process that is widely adopted in both industry and open source software environments. This process can help newcomers learn from…
How Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Humanizing LLMs
Yi Zeng, Hongpeng Lin, Jingwen Zhang +3
Most traditional AI safety research has approached AI models as machines and centered on algorithm-focused attacks developed by security experts. As large language models (LLMs) be…