Artificial Intelligence for Software Architecture: Literature Review and the Road Ahead

Abstract

Artificial intelligence is increasingly applied across software engineering, yet its explicit role in software architecture remains insufficiently understood. Architectural practices rely on complex trade-offs, documentation, and long-term evolution, all of which are traditionally manual, error-prone, and difficult to sustain. To clarify how artificial intelligence 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 artificial intelligence has been applied to architectural tasks and identifies six artificial intelligence-specific challenges that expose fundamental gaps between current capabilities and practitioner needs. Building on these findings, we chart a research agenda for artificial intelligence-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 artificial intelligence contributions to software architecture, establishes a foundation for future research, and outlines the conditions under which artificial intelligence can become a trustworthy partner in architectural design, evaluation, and evolution.

37 pages, accepted for publication in TOSEM