Abstract
Context:
Generative Artificial Intelligence (GenAI) is transforming much of software development, yet its application in software architecture is still in its infancy.
Aim:
Systematically synthesize the use, rationale, contexts, usability, and challenges of GenAI in software architecture.
Method:
Multivocal literature review (MLR), analyzing peer-reviewed and gray literature, identifying current practices, models, adoption contexts, reported challenges, and extracting themes via open coding.
Results:
This review identifies a 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 retrieval-augmented generation (RAG). GenAI has been applied mostly to the initial stages of the Software Architecture Life Cycle (SALC), such as Requirements-to-Architecture and Architecture-to-Code. Monolithic and microservice architectures were the main 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 there are several challenges on its way towards 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 overcome the gap between theoretical possibility and practical use.