用AI统一信息系统架构，实现代码与文档的自动双向转换。
Unified Architecture Metamodel of Information Systems Developed by Generative AI
- 构建分层架构元模型，支持代码-文档-代码的闭环生成
- 实验验证在结构化上下文中生成质量稳定，准确率提升明显
- 适合想自动化开发流程的团队和研究AI驱动设计的学者
AI与大语言模型的快速发展推动了软件开发生命周期（SDLC）的新方法，大量代码、技术文档和业务文档可自动生成。然而，由于缺乏统一的架构框架来保证不同表示层间的一致性与可重复转换，系统表示仍呈碎片化。本研究探索中小企业基于选定架构框架构建面向大语言模型应用的统一架构问题。提出一个涵盖关键架构图的框架结构，支持如“代码→文档→代码”的闭环转换。核心架构图均衡分布于三层：高层（业务与领域理解）、中层（系统架构）、低层（开发者层架构）。每层仍含抽象层级，增强灵活性并更好契合设计原则与架构模式。实验表明，在以架构图为结构化上下文时，生成文档与代码的质量保持稳定。结果证实，所提统一架构元模型可作为人与模型间的有效接口，显著提升大语言模型生成的准确性、稳定性与可重复性。但需优化部分图示以减少冗余，并更新部分图示以表达额外的上下文编排。该工作为新一代智能工具实现全链路自动化开发提供了可衡量的改进，支持与AI驱动开发兼容的全面架构。
原文摘要 · Abstract (English)
The rapid development of AI and LLMs has driven new methods of SDLC, in which a large portion of code, technical, and business documentation is generated automatically. However, since there is no single architectural framework that can provide consistent, repeatable transformations across different representation layers of information systems, such systems remain fragmented in their system representation. This study explores the problem of creating a unified architecture for LLM-oriented applications based on selected architectural frameworks by SMEs. A framework structure is proposed that covers some key types of architectural diagrams and supports a closed cycle of transformations, such as: "Code to Documentation to Code". The key architectural diagrams are split equally between main architectural layers: high-layer (business and domain understanding), middle-layer (system architecture), and low-layer (developer-layer architecture). Each architectural layer still contains some abstraction layers, which make it more flexible and better fit the requirements of design principles and architectural patterns. The conducted experiments demonstrated the stable quality of generated documentation and code when using a structured architectural context in the form of architectural diagrams. The results confirm that the proposed unified architecture metamodel can serve as an effective interface between humans and models, improving the accuracy, stability, and repeatability of LLM generation. However, the selected set of architectural diagrams should be optimised to avoid redundancy between some diagrams, and some diagrams should be updated to represent extra contextual orchestration. This work demonstrates measurable improvements for a new generation of intelligent tools that automate the SDLC and enable a comprehensive architecture compatible with AI-driven development.
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