Emerging Trends in Software Architecture from the Practitioners Perspective: A Five Year Review

2025-07-19

AI Summary

This study investigates the evolutionary patterns and driving mechanisms of software architecture practices from 2019 to 2023. Methodologically, it conducts a systematic analysis of 5,677 technical talks from eight major industry conferences, employing a hybrid text-mining approach that integrates large language model (LLM)-based automated annotation with domain-expert validation. This enables the first identification of five cohesive technical communities and 450 key technologies, along with their semantic interrelationships. Key findings include: (i) under cloud-native paradigms, Kubernetes and Serverless dominate late-stage DevOps and increasingly converge across lifecycle stages; (ii) technological adoption is highly concentrated in deployment, communication, and observability capability domains, underscoring the centrality of automation, cloud-native AI, and intelligent monitoring; and (iii) hybrid deployment and multi-stage integration have emerged as dominant practice patterns. The study delivers empirically grounded architectural decision support and an evolution map for practitioners and researchers.

Technology Category

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

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Cloud, edge and content delivery systems for the Web
• Web Mining and Content Analysis: Models for Web evolution
• Responsible Web: Sustainability and climate impact of web technologies

Abstract

Software architecture plays a central role in the design, development, and maintenance of software systems. With the rise of cloud computing, microservices, and containers, architectural practices have diversified. Understanding these shifts is vital. This study analyzes software architecture trends across eight leading industry conferences over five years. We investigate the evolution of software architecture by analyzing talks from top practitioner conferences, focusing on the motivations and contexts driving technology adoption. We analyzed 5,677 talks from eight major industry conferences, using large language models and expert validation to extract technologies, their purposes, and usage contexts. We also explored how technologies interrelate and fit within DevOps and deployment pipelines. Among 450 technologies, Kubernetes, Cloud Native, Serverless, and Containers dominate by frequency and centrality. Practitioners present technology mainly related to deployment, communication, AI, and observability. We identify five technology communities covering automation, coordination, cloud AI, monitoring, and cloud-edge. Most technologies span multiple DevOps stages and support hybrid deployment. Our study reveals that a few core technologies, like Kubernetes and Serverless, dominate the contemporary software architecture practice. These are mainly applied in later DevOps stages, with limited focus on early phases like planning and coding. We also show how practitioners frame technologies by purpose and context, reflecting evolving industry priorities. Finally, we observe how only research can provide a more holistic lens on architectural design, quality, and evolution.

Problem

• Analyzing software architecture trends over five years
• Investigating technology adoption motivations and contexts
• Identifying dominant technologies in DevOps and deployment

Innovation

• Analyzing 5,677 talks using large language models
• Identifying 450 technologies like Kubernetes and Serverless
• Exploring five technology communities in DevOps