Collaborative LLM Agents for C4 Software Architecture Design Automation
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866914116182999040 |
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| author | Szczepanik, Kamil Chudziak, Jarosław A. |
| author_facet | Szczepanik, Kamil Chudziak, Jarosław A. |
| contents | Software architecture design is a fundamental part of creating every software system. Despite its importance, producing a C4 software architecture model, the preferred notation for such architecture, remains manual and time-consuming. We introduce an LLM-based multi-agent system that automates this task by simulating a dialogue between role-specific experts who analyze requirements and generate the Context, Container, and Component views of the C4 model. Quality is assessed with a hybrid evaluation framework: deterministic checks for structural and syntactic integrity and C4 rule consistency, plus semantic and qualitative scoring via an LLM-as-a-Judge approach. Tested on five canonical system briefs, the workflow demonstrates fast C4 model creation, sustains high compilation success, and delivers semantic fidelity. A comparison of four state-of-the-art LLMs shows different strengths relevant to architectural design. This study contributes to automated software architecture design and its evaluation methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_22787 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Collaborative LLM Agents for C4 Software Architecture Design Automation Szczepanik, Kamil Chudziak, Jarosław A. Software Engineering Artificial Intelligence 68T07 I.2.11; I.2.7; I.2.8 Software architecture design is a fundamental part of creating every software system. Despite its importance, producing a C4 software architecture model, the preferred notation for such architecture, remains manual and time-consuming. We introduce an LLM-based multi-agent system that automates this task by simulating a dialogue between role-specific experts who analyze requirements and generate the Context, Container, and Component views of the C4 model. Quality is assessed with a hybrid evaluation framework: deterministic checks for structural and syntactic integrity and C4 rule consistency, plus semantic and qualitative scoring via an LLM-as-a-Judge approach. Tested on five canonical system briefs, the workflow demonstrates fast C4 model creation, sustains high compilation success, and delivers semantic fidelity. A comparison of four state-of-the-art LLMs shows different strengths relevant to architectural design. This study contributes to automated software architecture design and its evaluation methods. |
| title | Collaborative LLM Agents for C4 Software Architecture Design Automation |
| topic | Software Engineering Artificial Intelligence 68T07 I.2.11; I.2.7; I.2.8 |
| url | https://arxiv.org/abs/2510.22787 |