Retrieval Augmented (Knowledge Graph), and Large Language Model-Driven Design Structure Matrix (DSM) Generation of Cyber-Physical Systems

TL;DR

This paper investigates how Large Language Models, Retrieval-Augmented Generation, and graph-based methods can automate the creation of Design Structure Matrices for cyber-physical systems, tested on real-world examples like a power screwdriver and CubeSat.

Contribution

It introduces a novel approach combining LLMs and graph-based RAG techniques for automated DSM generation in cyber-physical systems.

Findings

Methods show promise in automating DSM creation

Performance varies with system complexity

Code is publicly available for reproducibility

Abstract

We explore the potential of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Graph-based RAG (GraphRAG) for generating Design Structure Matrices (DSMs). We test these methods on two distinct use cases -- a power screwdriver and a CubeSat with known architectural references -- evaluating their performance on two key tasks: determining relationships between predefined components, and the more complex challenge of identifying components and their subsequent relationships. We measure the performance by assessing each element of the DSM and overall architecture. Despite design and computational challenges, we identify opportunities for automated DSM generation, with all code publicly available for reproducibility and further feedback from the domain experts.