4 citations · 6 across the 6 of their papers we have counts for
8 papers
A Large-Scale Empirical Study of Quality Assurance Practices and Gaps in AI Agents
Wuyang Dai, Moses Openja, Jiho Shin +2
Large language model (LLM)-based agents are increasingly used across software engineering, web automation, research, and productivity applications. Their integration of planning, m…
Adversarial Testing of Automated Program Repair Agents for Security Vulnerabilities
Fares Trad, Simin Chen, Hung Viet Pham +2
Software agents with Large Language Models (LLMs) are designed for Automated Program Repair (APR) tasks, raising the possibility that, in the near future, APR agents will fix bugs…
ABTest: Behavior-Driven Testing for AI Coding Agents
Wuyang Dai, Moses Openja, Hung Viet Pham +3
AI coding agents are increasingly integrated into real-world software development workflows, yet their robustness under diverse and adversarial scenarios remains poorly understood.…
Engineering Pitfalls in AI Coding Tools: An Empirical Study of Bugs in Claude Code, Codex, and Gemini CLI
Ruixin Zhang, Wuyang Dai, Hung Viet Pham +3
The rapid integration of Large Language Models (LLMs) into software development workflows has given rise to a new class of AI-assisted coding tools, such as Claude-Code, Codex, and…
SecVulEval: Benchmarking LLMs for Real-World C/C++ Vulnerability Detection
Md Basim Uddin Ahmed, Nima Shiri Harzevili, Jiho Shin +2
Large Language Models (LLMs) have shown promise in software engineering tasks, but evaluating their effectiveness in vulnerability detection is challenging due to the lack of high-…
Automated Prompt Engineering for Cost-Effective Code Generation Using Evolutionary Algorithm
Hamed Taherkhani, Melika Sepindband, Hung Viet Pham +2
Large Language Models have seen increasing use in various software development tasks, especially in code generation. The most advanced recent methods attempt to incorporate feedbac…
Benchmarking Deep Learning Fuzzers
Nima Shiri Harzevili, Hung Viet Pham, Song Wang
In this work, we set out to conduct the first ground-truth empirical evaluation of state-of-the-art DL fuzzers. Specifically, we first manually created an extensive DL bug benchmar…
SkipAnalyzer: A Tool for Static Code Analysis with Large Language Models
Mohammad Mahdi Mohajer, Reem Aleithan, Nima Shiri Harzevili +4
We introduce SkipAnalyzer, a large language model (LLM)-powered tool for static code analysis. SkipAnalyzer has three components: 1) an LLM-based static bug detector that scans sou…