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

2025-03-17

AI Summary

This study addresses the research gap concerning generative AI (GenAI) in software architecture. Through the first multi-source literature review (MLR) in this domain—integrating open coding and multilingual retrieval—the authors systematically synthesize GenAI applications across early SDLC phases, including requirements-to-architecture and architecture-to-code translation. Key findings reveal that retrieval-augmented generation (RAG) and few-shot prompting demonstrate broad applicability in architectural decision-making and refactoring; microservices and monolithic architectures are the predominant targets for GenAI adaptation; yet six critical challenges persist: hallucination, insufficient precision, ethical and privacy concerns, and the absence of architecture-specific datasets and evaluation frameworks. The primary contributions include establishing a coherent application taxonomy for GenAI in software architecture, identifying core technical bottlenecks, and providing a foundational theoretical and practical roadmap for future research.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Machine Learning: Deep Neural Architectures and Foundation Models
• Multiagent Systems: Agent/AI Theories and Architectures

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• Explores GenAI applications in software architecture.
• Identifies challenges like precision, ethics, and datasets.
• Proposes future directions for GenAI in SDLC.

Innovation

• Uses OpenAI GPT models for architectural decisions
• Applies few-shot prompting and RAG techniques
• Focuses on early SDLC stages like Requirements-to-Architecture