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Software Architecture Meets LLMs: A Systematic Literature Review
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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.
Forward citations
Cited by 4 Pith papers
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Verified LLM-Driven Synthesis for Concept Design
A formal reaction semantics plus an LLM+Alloy CEGIS loop synthesizes and bounded-verifies coordination rules for Concept Design, with scenarios beating natural-language prompts for intent recovery.
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Large Language Models for Software Engineering Diagrams: A Systematic Review of UML and ER modelling
A systematic review of 64 papers finds LLM research on software diagrams is concentrated on UML class-diagram generation, dominated by GPT models, and held back by weak evaluation and scarce shared benchmarks.
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MAAD: Automate Software Architecture Design through Knowledge-Driven Multi-Agent Collaboration
A multi-agent LLM framework generates software architecture designs and evaluation reports from requirements, claimed to outperform MetaGPT on architectural completeness.
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Adoption of Generative Artificial Intelligence in the German Software Engineering Industry: An Empirical Study
In a survey of 109 German developers plus 18 interviews, GenAI productivity gains cluster among 'power users'; junior and senior developers perceive prompting differently, and limited codebase context is a major barrier.
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