Unified Architecture Metamodel of Information Systems Developed by Generative AI

TL;DR

This paper proposes a unified architectural framework for LLM-oriented applications, enabling consistent transformations across information system representations and improving AI-driven SDLC processes.

Contribution

It introduces a structured architectural metamodel that supports multi-layer diagrams and transformations, enhancing the stability and accuracy of generated documentation and code.

Findings

Stable quality of generated documentation and code demonstrated.

The unified architecture improves accuracy, stability, and repeatability of LLM outputs.

Framework supports a closed cycle of transformations like 'Code to Documentation to Code'.

Abstract

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.