Large Language Models as Assistants in Software Architecture Design

To achieve desired software quality attributes, architects must evaluate and choose from a myriad of design concepts, patterns, and technologies. This paper explores how Large Language Models (LLMs) can assist in this decision-making process. We compare design decisions made by LLMs and human architects across several design problems. Our results show that while LLMs suggest relevant options, human architects often disagree with or distrust those suggestions because the models frequently lack context and are inconsistent. Humans still need to make the final decisions in design, but LLMs are valuable as assistants: for generating design options, serving as a sounding board, acting as a creative catalyst and an education aid. We conclude with a discussion of the implications for architectural practice, education, and future research directions.