# An LLM-assisted approach to designing software architectures using ADD

arXiv: https://arxiv.org/abs/2506.22688 · HTML: https://mlsanity.com/paper/2506.22688/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2025.

## Built on by (most cited first, 2 of 2)

- [Automating Domain-Driven Design: Experience with a Prompting Framework](https://mlsanity.com/paper/2603.26244/) (2026)
- [SAKE: Software Architectural Knowledge Evaluation Benchmark for Large Language Models](https://mlsanity.com/paper/2606.29520/) (2026)

## Abstract (arXiv)

Designing effective software architectures is a complex, iterative process that traditionally relies on expert judgment. This paper proposes an approach for Large Language Model (LLM)-assisted software architecture design using the Attribute-Driven Design (ADD) method. By providing an LLM with an explicit description of ADD, an architect persona, and a structured iteration plan, our method guides the LLM to collaboratively produce architecture artifacts with a human architect. We validate the approach through case studies, comparing generated designs against proven solutions and evaluating them with professional architects. Results show that our LLM-assisted ADD process can generate architectures closely aligned with established solutions and partially satisfying architectural drivers, highlighting both the promise and current limitations of using LLMs in architecture design. Our findings emphasize the importance of human oversight and iterative refinement when leveraging LLMs in this domain.