Artificial Intelligence for Software Architecture: Literature Review and the Road Ahead
Alessio Bucaioni, Martin Weyssow, Junda He, Yunbo Lyu, David Lo
TL;DR
The paper tackles how artificial intelligence can be applied to software architecture, addressing enduring design and evolution challenges. It employs a systematic literature review complemented by practitioner insights to map the current state and gaps, resulting in 14 AI contributions and six AI-specific challenges. A six-direction roadmap (AI4SA) is proposed, encompassing real-time monitoring, automated documentation, context-aware explainable AI, multi-objective optimization, integrated diagnostics, and benchmarks. The work lays a foundation for practical AI-enabled SA, emphasizing benchmarks and industrial studies to validate approaches in real-world workflows.
Abstract
This paper presents a forward-looking vision for artificial intelligence-driven software architecture that addresses longstanding challenges in design and evolution. Although artificial intelligence has achieved notable success in software engineering, its explicit application to software architecture remains under-explored. Traditional practices, heavily reliant on expert knowledge and complex trade-off reasoning, tend to be manual and error-prone, thereby compromising system quality and maintainability. Building on recent advances, we examine how artificial intelligence can automate architectural design, support quantitative trade-off analyses, and continuously update architectural documentation. Our approach combines a systematic review of state-of-the-art applications with insights from industry practitioners. The resulting roadmap outlines 14 current artificial intelligence contributions to software architecture, identifies six artificial intelligence-specific challenges in supporting architectural tasks, and reveals six avenues for future improvement, charting a course for future research and practical implementations.