12 papers
Characterizing Large Language Model Agentic Workflows: A Study on N8n Ecosystem
Yutian Tang, Yuming Zhou, Huaming Chen
Large Language Models (LLMs) are rapidly being adopted in low-code and no-code automation platforms, where non-expert users design workflows that combine natural language understan…
Human-aligned AI Model Cards with Weighted Hierarchy Architecture
Pengyue Yang, Haolin Jin, Qingwen Zeng +3
The proliferation of Large Language Models (LLMs) has led to a burgeoning ecosystem of specialized, domain-specific models. While this rapid growth accelerates innovation, it has s…
Are LLMs Reliable Code Reviewers? Systematic Overcorrection in Requirement Conformance Judgement
Haolin Jin, Huaming Chen
Large language models (LLMs) have become essential tools in software development, widely used for requirements engineering, code generation and review tasks. Software engineers oft…
Trust in One Round: Confidence Estimation for Large Language Models via Structural Signals
Pengyue Yang, Jiawen Wen, Haolin Jin +3
Large language models (LLMs) are increasingly deployed in domains where errors carry high social, scientific, or safety costs. Yet standard confidence estimators, such as token lik…
What You See Is Not Always What You Get: Evaluating GPT's Comprehension of Source Code
Jiawen Wen, Bangshuo Zhu, Huaming Chen
Recent studies have demonstrated outstanding capabilities of large language models (LLMs) in software engineering tasks, including code generation and comprehension. While LLMs hav…
LLMs are All You Need? Improving Fuzz Testing for MOJO with Large Language Models
Linghan Huang, Peizhou Zhao, Huaming Chen
The rapid development of large language models (LLMs) has revolutionized software testing, particularly fuzz testing, by automating the generation of diverse and effective test inp…