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

2026-07-01 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Expert, extended

Summary

The paper "Artificial Intelligence for Software Architecture: Literature Review and the Road Ahead" presents a forward-looking vision for AI-driven software architecture (SA) to address longstanding challenges in design and evolution. It highlights that while AI has achieved notable success in software engineering, its explicit application to SA remains underexplored. The authors conducted a systematic review of AI applications in SA, integrating insights from 32 industry practitioners. This research identifies 14 current AI contributions to SA, such as using LLMs for generating SA candidates and NLP for microservice naming. It also outlines six AI-specific challenges in supporting architectural tasks, including continuous evolution and context-aware reasoning. The resulting roadmap proposes six distinct directions for future improvement, aiming to automate design, support quantitative trade-off analyses, and continuously update architectural documentation.

Key takeaway

For AI Architects evaluating future tooling, recognize that current AI for software architecture (AI4SA) is reactive and lacks deep contextual understanding. Prioritize solutions that offer real-time monitoring, automated documentation with traceability, and context-aware, explainable AI. Focus on tools supporting multi-objective optimization and integrated multi-level diagnostics to address complex trade-offs and technical debt effectively. Your adoption strategy should emphasize continuous evolution and human-AI collaboration, not just pattern matching.

Key insights

AI can automate architectural design, support trade-off analyses, and update documentation, but requires context-aware, explainable, and adaptive solutions.

Principles

• AI for SA must support continuous architectural evolution.
• Context-aware reasoning is essential for complex decomposition.
• Multi-objective optimization balances competing architectural trade-offs.

Method

A two-part methodology combined a systematic review of AI in SA (identifying 14 practices) with open SA challenges from 32 practitioner interviews. This analysis identified six AI-specific challenges and informed a six-direction roadmap.

In practice

• Use LLMs for generating SA or pattern candidates.
• Employ knowledge graphs for Architecture Decision Records analysis.
• Integrate AI plug-ins into architects' daily workflows.

Topics

• Software Architecture
• Artificial Intelligence
• AI4SA Roadmap
• Large Language Models
• Architectural Design Automation
• Systematic Literature Review

Best for: AI Architect, AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.