用大模型自动识别企业架构文档中的设计缺陷，提升治理效率。
Large Language Models for Analyzing Enterprise Architecture Debt in Unstructured Documentation
- 基于微调的大模型分析非结构化文档，自动识别架构异味
- 基准模型精度更高、处理更快，本地部署模型更安全
- 适合企业架构师与IT治理团队用于自动化风险筛查
企业架构债务（EA Debt）源于次优设计决策和组件错配，会随时间恶化组织的IT环境。早期预警信号——企业架构异味（EA Smells）——目前主要依赖人工发现或仅从结构化文档中提取，大量非结构化文档未被充分分析。本研究提出一种基于大语言模型（LLM）的方法，用于在非结构化架构文档中识别并量化EA Debt。采用设计科学研究方法，构建并评估了一个基于LLM的原型系统：该系统接收非结构化文本（如流程描述、战略文件），应用微调检测模型，输出识别出的异味。通过使用合成但真实的企业文档进行案例研究，对比自定义GPT模型。结果表明，LLM可有效检测多种预定义的EA Smells；基准模型在精度和处理速度上表现更优，而微调的本地部署模型在数据保护方面更具优势。研究结果凸显了将基于LLM的异味检测融入企业架构治理实践的潜力。
原文摘要 · Abstract (English)
Enterprise Architecture Debt (EA Debt) arises from suboptimal design decisions and misaligned components that can degrade an organization's IT landscape over time. Early indicators, Enterprise Architecture Smells (EA Smells), are currently mainly detected manually or only from structured artifacts, leaving much unstructured documentation under-analyzed. This study proposes an approach using a large language model (LLM) to identify and quantify EA Debt in unstructured architectural documentation. Following a design science research approach, we design and evaluate an LLM-based prototype for automated EA Smell detection. The artifact ingests unstructured documents (e.g., process descriptions, strategy papers), applies fine-tuned detection models, and outputs identified smells. We evaluate the prototype through a case study using synthetic yet realistic business documents, benchmarking against a custom GPT-based model. Results show that LLMs can detect multiple predefined EA Smells in unstructured text, with the benchmark model achieving higher precision and processing speed, and the fine-tuned on-premise model offering data protection advantages. The findings highlight opportunities for integrating LLM-based smell detection into EA governance practice.
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