4 papers
Detect--Repair--Verify for LLM-Generated Code: A Multi-Language, Multi-Granularity Empirical Study
Cheng Cheng
Large language models can generate runnable software artifacts, but their security remains difficult to evaluate end to end. This study examines that problem through a Detect--Repa…
Detect Repair Verify for Securing LLM Generated Code: A Multi-Language Empirical Study
Cheng Cheng
Large language models are increasingly used to produce runnable software. In practice, security is often addressed through a Detect--Repair--Verify (DRV) loop that detects issues,…
CFCEval: Evaluating Security Aspects in Code Generated by Large Language Models
Cheng Cheng, Jinqiu Yang
Code-focused Large Language Models (LLMs), such as CodeX and Star-Coder, have demonstrated remarkable capabilities in enhancing developer productivity through context-aware code ge…
An Exploratory Study on Fine-Tuning Large Language Models for Secure Code Generation
Junjie Li, Fazle Rabbi, Cheng Cheng +3
AI-powered coding assistants such as GitHub's Copilot and OpenAI's ChatGPT have achieved notable success in automating code generation. However, these tools rely on pre-trained Lar…