# Generative AI for Software Architecture. Applications, Challenges, and Future Directions

arXiv: https://arxiv.org/abs/2503.13310 · HTML: https://mlsanity.com/paper/2503.13310/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 65 references that are in the map.
- First posted on arXiv 17 Mar 2025.

## Builds on (most cited first, 14 of 14)

- [A Survey on Large Language Models for Code Generation](https://mlsanity.com/paper/2406.00515/) (2024)
- [Large Language Models for Software Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2308.10620/) (2023)
- [Large Language Models for Software Engineering: Survey and Open Problems](https://mlsanity.com/paper/2310.03533/) (2023)
- [Software Testing with Large Language Models: Survey, Landscape, and Vision](https://mlsanity.com/paper/2307.07221/) (2023)
- [Guidelines for including grey literature and conducting multivocal literature reviews in software engineering](https://mlsanity.com/paper/1707.02553/) (2017)
- [Towards Human-Bot Collaborative Software Architecting with ChatGPT](https://mlsanity.com/paper/2302.14600/) (2023)
- [Requirements are All You Need: From Requirements to Code with LLMs](https://mlsanity.com/paper/2406.10101/) (2024)
- [Can LLMs Generate Architectural Design Decisions? -An Exploratory Empirical study](https://mlsanity.com/paper/2403.01709/) (2024)
- [Beyond Words: On Large Language Models Actionability in Mission-Critical Risk Analysis](https://mlsanity.com/paper/2406.10273/) (2024)
- [On Large Language Models in Mission-Critical IT Governance: Are We Ready Yet?](https://mlsanity.com/paper/2412.11698/) (2024)
- [From Requirements to Architecture: An AI-Based Journey to Semi-Automatically Generate Software Architectures](https://mlsanity.com/paper/2401.14079/) (2024)
- [LLMs for Generation of Architectural Components: An Exploratory Empirical Study in the Serverless World](https://mlsanity.com/paper/2502.02539/) (2025)
- [Leveraging LLMs for Dynamic IoT Systems Generation through Mixed-Initiative Interaction](https://mlsanity.com/paper/2502.00689/) (2025)
- [Automated Microservice Pattern Instance Detection Using Infrastructure-as-Code Artifacts and Large Language Models](https://mlsanity.com/paper/2502.04188/) (2025)

## Built on by (most cited first, 15 of 15)

- [AI-Assisted Engineering Should Track the Epistemic Status and Temporal Validity of Architectural Decisions](https://mlsanity.com/paper/2601.21116/) (2026)
- [Vibe Coding in Practice: Flow, Technical Debt, and Guidelines for Sustainable Use](https://mlsanity.com/paper/2512.11922/) (2025)
- [ArchAgent: Scalable Legacy Software Architecture Recovery with LLMs](https://mlsanity.com/paper/2601.13007/) (2026)
- [MAAD: Automate Software Architecture Design through Knowledge-Driven Multi-Agent Collaboration](https://mlsanity.com/paper/2507.21382/) (2025)
- [Can AI Agents Generate Microservices? How Far are We?](https://mlsanity.com/paper/2603.09004/) (2026)
- [Descriptor: C++ Self-Admitted Technical Debt Dataset (CppSATD)](https://mlsanity.com/paper/2505.01136/) (2025)
- [Autonomic Microservice Management via Agentic AI and MAPE-K Integration](https://mlsanity.com/paper/2506.22185/) (2025)
- [The Invisible Hand of AI Libraries Shaping Open Source Projects and Communities](https://mlsanity.com/paper/2601.01944/) (2026)
- [Evaluating Large Language Models for Detecting Architectural Decision Violations](https://mlsanity.com/paper/2602.07609/) (2026)
- [LLM-based Automated Architecture View Generation: Where Are We Now?](https://mlsanity.com/paper/2603.21178/) (2026)
- [Automating Domain-Driven Design: Experience with a Prompting Framework](https://mlsanity.com/paper/2603.26244/) (2026)
- [SAGAI-MID: A Generative AI-Driven Middleware for Dynamic Runtime Interoperability](https://mlsanity.com/paper/2603.28731/) (2026)
- [RAD-AI: Rethinking Architecture Documentation for AI-Augmented Ecosystems](https://mlsanity.com/paper/2603.28735/) (2026)
- [Toward a Sustainable Software Architecture Community: Evaluating ICSA's Environmental Impact](https://mlsanity.com/paper/2604.04096/) (2026)
- [CAKE: Cloud Architecture Knowledge Evaluation of Large Language Models](https://mlsanity.com/paper/2604.05755/) (2026)

## Abstract (arXiv)

Context: Generative Artificial Intelligence (GenAI) is transforming much of software development, yet its application in software architecture is still in its infancy, and no prior study has systematically addressed the topic. Aim: We aim to systematically synthesize the use, rationale, contexts, usability, and future challenges of GenAI in software architecture. Method: We performed a multivocal literature review (MLR), analyzing peer-reviewed and gray literature, identifying current practices, models, adoption contexts, and reported challenges, extracting themes via open coding. Results: Our review identified significant adoption of GenAI for architectural decision support and architectural reconstruction. OpenAI GPT models are predominantly applied, and there is consistent use of techniques such as few-shot prompting and retrieved-augmented generation (RAG). GenAI has been applied mostly to initial stages of the Software Development Life Cycle (SDLC), such as Requirements-to-Architecture and Architecture-to-Code. Monolithic and microservice architectures were the dominant targets. However, rigorous testing of GenAI outputs was typically missing from the studies. Among the most frequent challenges are model precision, hallucinations, ethical aspects, privacy issues, lack of architecture-specific datasets, and the absence of sound evaluation frameworks. Conclusions: GenAI shows significant potential in software design, but several challenges remain on its path to greater adoption. Research efforts should target designing general evaluation methodologies, handling ethics and precision, increasing transparency and explainability, and promoting architecture-specific datasets and benchmarks to bridge the gap between theoretical possibilities and practical use.