# From Requirements to Architecture: An AI-Based Journey to Semi-Automatically Generate Software Architectures

arXiv: https://arxiv.org/abs/2401.14079 · HTML: https://mlsanity.com/paper/2401.14079/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 16 references that are in the map.
- First posted on arXiv 25 Jan 2024.

## Built on by (most cited first, 8 of 8)

- [Generative AI for Software Architecture. Applications, Challenges, and Future Directions](https://mlsanity.com/paper/2503.13310/) (2025)
- [DRAFT-ing Architectural Design Decisions using LLMs](https://mlsanity.com/paper/2504.08207/) (2025)
- [Leveraging Large Language Models for Use Case Model Generation from Software Requirements](https://mlsanity.com/paper/2511.09231/) (2025)
- [ARLO: A Tailorable Approach for Transforming Natural Language Software Requirements into Architecture using LLMs](https://mlsanity.com/paper/2504.06143/) (2025)
- [From Requirements to Architecture: Semi-Automatically Generating Software Architectures](https://mlsanity.com/paper/2504.12192/) (2025)
- [MAAD: Automate Software Architecture Design through Knowledge-Driven Multi-Agent Collaboration](https://mlsanity.com/paper/2507.21382/) (2025)
- [Towards LLM-Assisted Architecture Recovery for Real-World ROS~2 Systems: An Agent-Based Multi-Level Approach to Hierarchical Structural Architecture Reconstruction](https://mlsanity.com/paper/2605.20055/) (2026)
- [Bridging Requirements and Architecture: Multi-Agent Orchestration with External Knowledge and Hierarchical Memory](https://mlsanity.com/paper/2606.01385/) (2026)

## Abstract (arXiv)

Designing domain models and software architectures represents a significant challenge in software development, as the resulting architectures play a vital role in fulfilling the system's quality of service. Due to time pressure, architects often model only one architecture based on their known limited domain understanding, patterns, and experience instead of thoroughly analyzing the domain and evaluating multiple candidates, selecting the best fitting. Existing approaches try to generate domain models based on requirements, but still require time-consuming manual effort to achieve good results. Therefore, in this vision paper, we propose a method to generate software architecture candidates semi-automatically based on requirements using artificial intelligence techniques. We further envision an automatic evaluation and trade-off analysis of the generated architecture candidates using, e.g., the architecture trade-off analysis method combined with large language models and quantitative analyses. To evaluate this approach, we aim to analyze the quality of the generated architecture models and the efficiency and effectiveness of our proposed process by conducting qualitative studies.