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This literature review synthesizes recent studies on software architecture advancements, focusing on the implications of cloud computing, microservices, and AI in contemporary design practices. By employing a thematic synthesis approach, the review identifies key trends such as the importance of continuous architectural governance and AI-assisted decision-making, while also highlighting persistent research gaps in empirical validation and security integration. The findings underscore a shift towards multi-faceted architectural views and intelligent design techniques that enhance scalability and sustainability in software systems.
Modern software architecture is evolving to prioritize continuous governance and AI-assisted decision-making, yet critical research gaps in security and empirical validation remain unaddressed.
Software architecture has evolved considerably in response to the increasing complexity of modern software systems, particularly those based on cloud computing, microservices, artificial intelligence (AI), and distributed computing environments. This literature review synthesizes recent studies published between 2024 and 2025 to examine emerging trends, challenges, and future directions in software design and architecture. The review adopts a thematic synthesis approach to analyse contemporary research across five major areas: architectural modelling and representation, software quality attributes and self-adaptive architectures, architectural evolution and complexity management, artificial intelligence-assisted architectural decision-making, and existing research gaps. The findings indicate that modern software architecture extends beyond traditional structural design to support continuous architectural governance, stakeholder communication, runtime observability, resilience, and intelligent decision support throughout the software lifecycle. Furthermore, the reviewed studies demonstrate that multiple architectural views, continuous monitoring, domain-driven decomposition, and AI-assisted design techniques contribute significantly to improving scalability, maintainability, adaptability, and long-term software sustainability. Despite these advances, several research gaps remain, including limited empirical validation of proposed approaches, insufficient integration of security and privacy into architectural decision-making, inadequate exploration of emerging paradigms such as edge and serverless computing, and the absence of standardized frameworks for trustworthy AI-assisted architecture.
