Artificial Intelligence for Software Architecture: Literature Review and the Road Ahead
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917978689241088 |
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| author | Bucaioni, Alessio Weyssow, Martin He, Junda Lyu, Yunbo Lo, David |
| author_facet | Bucaioni, Alessio Weyssow, Martin He, Junda Lyu, Yunbo Lo, David |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_04334 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Artificial Intelligence for Software Architecture: Literature Review and the Road Ahead Bucaioni, Alessio Weyssow, Martin He, Junda Lyu, Yunbo Lo, David Software Engineering 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. |
| title | Artificial Intelligence for Software Architecture: Literature Review and the Road Ahead |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2504.04334 |