2 citations · 10 across the 13 of their papers we have counts for
5 papers · 1 filter
Model-Driven Discipline for Multi-Agent LLMs: Requirement-to-Verification Generation of Traceable System Models
Ran Wei, Le Zhu, Haochi Wang +6
Software complexity is a long-standing challenge for system engineers. Model-Driven Engineering (MDE) addresses it by treating models as first-class artefacts, but a typical MDE pr…
AkiraRust: Re-thinking LLM-aided Rust Repair Using a Feedback-guided Thinking Switch
Renshuang Jiang, Yichong Wang, Pan Dong +6
Eliminating undefined behaviors (UBs) in Rust programs requires a deep semantic understanding to enable accurate and reliable repair. While existing studies have demonstrated the p…
ARSP: Automated Repair of Verilog Designs via Semantic Partitioning
Bingkun Yao, Ning Wang, Xiangfeng Liu +5
Debugging functional Verilog bugs consumes a significant portion of front-end design time. While Large Language Models (LLMs) have demonstrated great potential in mitigating this e…
VeriDebug: A Unified LLM for Verilog Debugging via Contrastive Embedding and Guided Correction
Ning Wang, Bingkun Yao, Jie Zhou +4
Large Language Models (LLMs) have demonstrated remarkable potential in debugging for various programming languages. However, the application of LLMs to Verilog debugging remains in…
MaxMind: A Memory Loop Network to Enhance Software Productivity based on Large Language Models
Yuchen Dong, XiaoXiang Fang, Yuchen Hu +2
The application of large language models to facilitate automated software operations and tool generation (SOTG), thus augmenting software productivity, mirrors the early stages of…