2 citations · 2 across the 7 of their papers we have counts for
7 papers
Unsaid, Unsafe? Implicit Security Obligations in LLM-Based RTL Code Generation
Guang Yang, Xing Hu, Xiang Chen +1
Large Language Models (LLMs) generate register-transfer-level (RTL) code with rapidly improving functional correctness. Security of LLM-generated code, however, has been studied ma…
Execution-Anchored Hallucination Calibration Reranking for Verilog Code Generation
Guang Yang, Xing Hu, Xiang Chen +2
Large Language Models (LLMs) have demonstrated remarkable capabilities in code generation, yet their performance degrades significantly on low-resource Hardware Description Languag…
AgentExecutor: Partial Code Execution via Agentic Context Generation
Junkai Chen, Chengran Yang, Xing Hu +3
Executing code snippets is essential for dynamic program analysis, but it remains challenging to execute an arbitrary code snippet due to issues like missing context and incomplete…
From Mirage to Grounding: Towards Reliable Multimodal Circuit-to-Verilog Code Generation
Guang Yang, Xing Hu, Xiang Chen +1
Multimodal large language models (MLLMs) are increasingly used to translate visual artifacts into code, from UI mockups into HTML to scientific plots into Python scripts. A circuit…
Large Language Model for Verilog Code Generation: Literature Review and the Road Ahead
Guang Yang, Wei Zheng, Xiang Chen +14
Code generation has emerged as a critical research area at the intersection of Software Engineering (SE) and Artificial Intelligence (AI), attracting significant attention from bot…
SecureVibeBench: Benchmarking Secure Vibe Coding of AI Agents via Reconstructing Vulnerability-Introducing Scenarios
Junkai Chen, Huihui Huang, Yunbo Lyu +10
Large language model-powered code agents are rapidly transforming software engineering, yet the security risks of their generated code have become a critical concern. Existing benc…
CODE-DITING: A Reasoning-Based Metric for Functional Alignment in Code Evaluation
Guang Yang, Yu Zhou, Xiang Chen +5
Trustworthy evaluation methods for code snippets play a crucial role in neural code generation. Traditional methods, which either rely on reference solutions or require executable…