AI coding agents now contribute to software repositories at scale, yet their documentation practices remain uncharacterised. We analyse 29,669 AI-generated pull requests from 2,585 repositories in the AIDev dataset, measuring documentation co-change: whether code-file modifications are accompanied by a documentation-fi...
William DeMichele, Kla Tantithamthavorn· 0 citations
Finding vulnerabilities early, before they can affect the production system, is important for reducing the risk of any software attacks. However, detecting vulnerabilities during secure code review is still difficult because the vulnerable code logic may depend on lines found across different repository files. Recent a...
N. Abhishek, Ping Charoenwet, Kla Tantithamthavorn· 0 citations
This work presents XAgent, an execution-guided agentic framework that analyzes dynamic behavior and additional program context to localize and validate issues, opening new opportunities for LLM-based coding agents to achieve more robust and generalizable software maintenance.
H. Huynh, Patanamon Thongtanunam, Michael Fu et al.· 0 citations
The ablation study confirms that the three context facets are mutually complementary, and that multi-agent scaffolding and base-model capacity each play an essential role for agentic vulnerability repair.
Michael Fu, Qiyue Mei, Patanamon Thongtanunam et al.· arXiv.org· 1 citation
SkillGate, a deployable security gateway that screens AI skill packages before coding agent installation is presented, and four research questions covering detection effectiveness, screening cost, runtime overhead, and false positive behavior on the SkillsBench benchmark against two existing tools are answered.
Rui Yang, Michael Fu, C. Tantithamthavorn et al.· arXiv.org· 1 citation
This paper introduces Requirements-Augmented Generation (REAG), which interprets user intentions by retrieving relevant software requirements, domain knowledge, and personas via adaptive RAG and self-reasoning to generate context-aware test oracles, and introduces a confidence-calibrated cascade judgment, which quantif...
Fan-Yu Wang, Chetan Arora, Zhen-Ping Xie et al.· 0 citations
An empirical study of agentic code reviews using CodeRabbit as a case study shows that agentic reviews receive mixed reception, and finds that lightweight learning-based methods achieve up to 76% F1 score, suggesting learnable patterns exist between code reviews and their corresponding feedback.
An adversarial skill-synthesis method using six LLMs across four families to transform 471 real-world shell commands into benign-appearing skills, released as a benchmark of 2,826 skills mapped to 11 MITRE ATT&CK tactics.
Rui Yang, Michael Fu, Kla Tantithamthavorn et al.· 0 citations
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