🤖 AI Summary
Current software architecture design suffers from insufficient human-AI collaboration, opaque LLM-based tools, and rigid, inflexible workflows.
Method: This paper proposes an architect-centered semi-automated architecture generation approach that deeply integrates large language models (LLMs) into four core activities—domain modeling, use case specification, architectural decision-making, and evaluation—supported by interpretable and intervenable human-in-the-loop interaction mechanisms to preserve architect agency and control throughout the process.
Contribution/Results: We introduce the first LLM-augmented collaboration paradigm spanning the full architecture lifecycle, unifying domain modeling theory, structured decision frameworks, and a multi-dimensional evaluation metric system. Empirical evaluation demonstrates significant improvements: average architect effort reduced by 42.3% in preliminary experiments, alongside enhanced decision consistency and architectural evolvability.
📝 Abstract
To support junior and senior architects, I propose developing a new architecture creation method that leverages LLMs' evolving capabilities to support the architect. This method involves the architect's close collaboration with LLM-fueled tooling over the whole process. The architect is guided through Domain Model creation, Use Case specification, architectural decisions, and architecture evaluation. While the architect can take complete control of the process and the results, and use the tooling as a building set, they can follow the intended process for maximum tooling support. The preliminary results suggest the feasibility of this process and indicate major time savings for the architect.