2 citations · 3 across the 10 of their papers we have counts for
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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…
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…
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…
Uncovering Systematic Failures of LLMs in Verifying Code Against Natural Language Specifications
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…
Towards Advancing Code Generation with Large Language Models: A Research Roadmap
Haolin Jin, Huaming Chen, Qinghua Lu +1
Recently, we have witnessed the rapid development of large language models, which have demonstrated excellent capabilities in the downstream task of code generation. However, despi…
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…
RGD: Multi-LLM Based Agent Debugger via Refinement and Generation Guidance
Haolin Jin, Zechao Sun, Huaming Chen
Large Language Models (LLMs) have shown incredible potential in code generation tasks, and recent research in prompt engineering have enhanced LLMs' understanding of textual inform…