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Jiho Shin

York University

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#software testing Preprint Sep 2026

A Large-Scale Empirical Study of Quality Assurance Practices and Gaps in AI Agents

A large-scale empirical study of quality assurance (QA) practices in 157 open-source LLM-based agent projects with at least 100 GitHub stars highlights the need to move beyond feature-level testing toward systematic end-to-end validation that ensures agent workflows remain within intended boundaries when interacting with untrusted inputs, tools, persistent state, and external APIs.

Wu-Yang Dai, Moses Openja, Jiho Shin et al. · 0 citations
Book Open access Jul 2026

SecVulEval: Context-Aware Benchmarking of LLMs for Vulnerability Detection

This paper introduces SecVulEval, a context-aware benchmark designed to evaluate LLMs on vulnerability detection with rich contextual information, and believes it can serve as a foundation for advancing context-aware vulnerability detection with LLMs.

Md Basim Uddin Ahmed, Nima Shiri Harzevili, Jiho Shin et al. · 2 citations

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