# Software Architecture Meets LLMs: A Systematic Literature Review

arXiv: https://arxiv.org/abs/2505.16697 · HTML: https://mlsanity.com/paper/2505.16697/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2025.

## Built on by (most cited first, 11 of 11)

- [Unified Architecture Metamodel of Information Systems Developed by Generative AI](https://mlsanity.com/paper/2604.00171/) (2026)
- [Architecture Without Architects: How AI Coding Agents Shape Software Architecture](https://mlsanity.com/paper/2604.04990/) (2026)
- [MAAD: Automate Software Architecture Design through Knowledge-Driven Multi-Agent Collaboration](https://mlsanity.com/paper/2507.21382/) (2025)
- [(How) Do Large Language Models Understand High-Level Message Sequence Charts?](https://mlsanity.com/paper/2605.13773/) (2026)
- [Rule Taxonomy and Evolution in AI IDEs: A Mining and Survey Study](https://mlsanity.com/paper/2606.12231/) (2026)
- [Mining Architectural Quality Under Agentic AI Adoption: A Causal Study of Java Repositories](https://mlsanity.com/paper/2606.13298/) (2026)
- [Adoption of Generative Artificial Intelligence in the German Software Engineering Industry: An Empirical Study](https://mlsanity.com/paper/2601.16700/) (2026)
- [LLM-based Automated Architecture View Generation: Where Are We Now?](https://mlsanity.com/paper/2603.21178/) (2026)
- [CAKE: Cloud Architecture Knowledge Evaluation of Large Language Models](https://mlsanity.com/paper/2604.05755/) (2026)
- [Using LLMs in Software Design: An Empirical Study of GitHub and A Practitioner Survey](https://mlsanity.com/paper/2605.01392/) (2026)
- [SAKE: Software Architectural Knowledge Evaluation Benchmark for Large Language Models](https://mlsanity.com/paper/2606.29520/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) are used for many different software engineering tasks. In software architecture, they have been applied to tasks such as classification of design decisions, detection of design patterns, and generation of software architecture design from requirements. However, there is little overview on how well they work, what challenges exist, and what open problems remain. In this paper, we present a systematic literature review on the use of LLMs in software architecture. We analyze 18 research articles to answer five research questions, such as which software architecture tasks LLMs are used for, how much automation they provide, which models and techniques are used, and how these approaches are evaluated. Our findings show that while LLMs are increasingly applied to a variety of software architecture tasks and often outperform baselines, some areas, such as generating source code from architectural design, cloud-native computing and architecture, and checking conformance remain underexplored. Although current approaches mostly use simple prompting techniques, we identify a growing research interest in refining LLM-based approaches by integrating advanced techniques.