SemGuard: Real-Time Semantic Evaluator for Correcting LLM-Generated Code
ASE 2025 CCF A
Associate Professor, School of Information Science & Engineering
Shandong Normal University
From explainable to auditable code — guided by rules, measured by evidence. (不以规矩,不能成方圆)
I work at the intersection of intelligent software engineering and software security, with a current focus on trustworthy code generation. My group studies white-box, auditable generation pipelines that unify correctness × efficiency × security × auditability, embedding decision & evidence into the generation process to move from explainable to auditable code. We also build repo-level analyses and benchmarks for LLM→CodeQL generation/evaluation and study LLM security for code.
🚀 I’m open to motivated students and collaborators in trustworthy code generation, repo-level security, and LLM security for code.
* corresponding author. A full list is on Google Scholar.
ASE 2025 CCF A
arXiv:2509.12629, 2025
ASE 2024 CCF A
FSE 2024 CCF A
ICSE 2024 CCF A
arXiv:2312.14852, 2023
IEEE Transactions on Services Computing CCF A, 2018
ICSE 2014 CCF A
Email: lvchen@sdnu.edu.cn
Google Scholar: OZ_NVPUAAAAJ