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

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.

6GSoft: Software for Edge-to-Cloud Continuum

In the era of 6G, developing and managing software requires cutting-edge software engineering (SE) theories and practices tailored for such complexity across a vast number of connected edge devices. Our project aims to lead the development of sustainable methods and energy-efficient orchestration models specifically for edge environments, enhancing architectural support driven by AI for contemporary edge-to-cloud continuum computing. This initiative seeks to position Finland at the forefront of the 6G landscape, focusing on sophisticated edge orchestration and robust software architectures to optimize the performance and scalability of edge networks. Collaborating with leading Finnish universities and companies, the project emphasizes deep industry-academia collaboration and international expertise to address critical challenges in edge orchestration and software architecture, aiming to drive significant advancements in software productivity and market impact.

AI Techniques in the Microservices Life-Cycle: A Survey

Microservices is a popular architectural style for the development of distributed software, with an emphasis on modularity, scalability, and flexibility. Indeed, in microservice systems, functionalities are provided by loosely coupled, small services, each focusing on a specific business capability. Building a system according to the microservices architectural style brings a number of challenges, mainly related to how the different microservices are deployed and coordinated and how they interact. In this paper, we provide a survey about how techniques in the area of Artificial Intelligence have been used to tackle these challenges.