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.

翻译：人工智能与大语言模型的快速发展推动了软件开发生命周期新范式的出现，其中大量代码、技术文档和业务文档实现了自动生成。然而，由于缺乏能够跨信息系统不同表示层提供一致且可重复转换的统一架构框架，此类系统在系统表示层面仍存在碎片化问题。本研究探讨了基于中小企业选定的架构框架，为大语言模型导向型应用创建统一架构的问题。本文提出了一种涵盖若干关键架构图类型的框架结构，并支持"代码→文档→代码"的闭环转换。关键架构图在三个主要架构层间均衡分布：高层（业务与领域理解）、中层（系统架构）和低层（开发者层架构）。各架构层仍包含若干抽象层次，赋予其更高的灵活性，使其更贴合设计原则与架构模式的要求。实验表明，在架构图形式的结构化架构上下文支持下，生成文档与代码的质量保持稳定。结果证实，所提出的统一架构元模型可作为人机之间的有效接口，提升大语言模型生成的准确性、稳定性与可重复性。然而，需优化所选架构图集合以避免部分图之间的冗余，同时更新某些图表以表达额外的上下文编排。本研究为新型智能工具带来了可测量的改进——这些工具将自动化软件开发生命周期，并实现与人工智能驱动开发相兼容的全面架构。