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

2025-04-06

AI Summary

This paper addresses longstanding challenges in software architecture design and evolution—namely, heavy reliance on expert knowledge, insufficient quantification of architectural trade-off analysis, and documentation that is chronically outdated and error-prone. To tackle these issues, we propose the first AI-empowered, end-to-end technical roadmap for software architecture. Grounded in a dual-validated approach combining systematic literature review and industrial practice, our methodology integrates qualitative analysis with domain mapping to synthesize 14 representative works, identify six AI-specific challenges (e.g., architectural semantic modeling, cross-level reasoning interpretability), and articulate six key future research directions. The resulting roadmap is structured, actionable, and empirically grounded. It provides researchers with a rigorous theoretical framework and practitioners with a data-driven, adaptive paradigm for architecture engineering—thereby significantly enhancing system quality, evolvability, and maintainability.

Technology Category

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

Application Category

• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

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.

Problem

• AI-driven automation of software architectural design
• Supporting quantitative trade-off analyses in architecture
• Continuous updating of architectural documentation using AI

Innovation

• AI automates architectural design processes
• AI supports quantitative trade-off analyses
• AI continuously updates architectural documentation