一句话：提出构建可复用 Simulink 模型语料库，并利用大语言模型测试 Simulink 工具链以发现关键缺陷。
MATLAB/Simulink 广泛应用于汽车、航空航天、医疗与工业自动化等安全关键领域，但其工具链缺陷可能传播到生成产物中。现有工作受限于工具链测试难以高效发现关键缺陷，以及缺乏可复用的公开 Simulink 模型语料库。该论文计划构建大规模、多样化的开源 Simulink 模型语料库，以支持建模实践、度量与模型演化分析；并基于该语料库，利用深度学习语言模型对 Simulink 工具链进行测试。目标是提升工具链缺陷发现能力，同时为 CPS 模型的设计、仿真与验证提供数据与理解基础。
MATLAB/Simulink is a de-facto standard tool in several safety-critical industries such as automotive, aerospace, healthcare, and industrial automation for system modeling and analysis, compiling models to code, and deploying code to embedded hardware. On one hand, testing cyber-physical system (CPS) development tools such as MathWorks’ Simulink is important as a bug in the toolchain may propagate to the artifacts they produce. On the other hand, it is equally important to understand modeling practices and model evolution to support engineers and scientists as they are widely used in design, simulation, and verification of CPS models. Existing work in this area is limited by two main factors, i.e., (1) inefficiencies of state-of-the-art testing schemes in finding critical tool-chain bugs and (2) the lack of a reusable corpus of public Simulink models. In my thesis, I propose to (1) curate a large reusable corpus of Simulink models to help understand modeling practices and model evolution and (2) leverage such a corpus with deep-learning based language models to test the toolchain.
@inproceedings{Shrestha23,
title = {Harnessing Large Language Models for Simulink Toolchain Testing and Developing Diverse Open-Source Corpora of Simulink Models for Metric and Evolution Analysis},
author = {Sohil Lal Shrestha},
booktitle = {ISSTA},
year = {2023},
doi = {10.1145/3597926.3605233}
}