Artificial Intelligence Support for Software Architecture Practice: A Systematic Review and Future Directions
Alessio Bucaioni, Martin Weyssow, Junda He, Yunbo Lyu, David Lo · ACM Transactions on Software Engineering and Methodology · 2026
AI is increasingly applied across software engineering, yet its explicit role in software architecture remains insufficiently understood. Architectural practices rely on complex tradeoffs, documentation, and long-term evolution, all of which are traditionally manual, error-prone, and difficult to sustain. To clarify how AI can address these challenges, we conducted a systematic literature review of 51 peer-reviewed primary studies and systematically mapped their contributions onto 17 practitioner-reported software architecture challenges derived from empirical interviews. This analysis identifies 14 topical areas where AI has been applied to architectural tasks and identifies six AI-specific challenges that expose fundamental gaps between current capabilities and practitioner needs. Building on these findings, we chart a research agenda for AI-driven software architecture organized around five strategic pillars. By grounding the roadmap in both systematic evidence and practitioner insights, this work provides the first peer-reviewed comprehensive synthesis of AI contributions to software architecture, establishes a foundation for future research, and outlines the conditions under which AI can become a trustworthy partner in architectural design, evaluation, and evolution.