The (C)omprehensive (A)rchitecture (P)attern (I)ntegration method: Navigating the sea of technology

2025-08-22

AI Summary

Technical selection and architectural design decisions for large-scale software systems are highly complex and traditionally rely on trial-and-error. Method: This paper proposes CAPI—a decision-support method that recommends architectural patterns (rather than concrete tools) as atomic units, organizing them into a diagnostic decision tree; pattern abstraction drastically reduces the solution space, and iterative development is validated through multiple rounds of academic and industrial user studies. Contribution/Results: CAPI innovatively treats architectural patterns as computable, recommendable decision objects and establishes a structured, requirement-to-pattern mapping. Evaluation demonstrates that CAPI significantly improves decision efficiency and is consistently regarded by users as a practical, reproducible aid for efficient architectural practice.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Multiagent Systems: Agent/AI Theories and Architectures

Application Category

• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

The technological landscape changes daily, making it nearly impossible for a single person to be aware of all trends or available tools that may or may not be suitable for their software project. This makes tool selection and architectural design decisions a complex problem, especially for large-scale software systems. To tackle this issue, we introduce CAPI, the Comprehensive Architecture Pattern Integration method that uses a diagnostic decision tree to suggest architectural patterns depending on user needs. By suggesting patterns instead of tools, the overall complexity for further decisions is lower as there are fewer architectural patterns than tools due to the abstract nature of patterns. Moreover, since tools implement patterns, each non-proposed pattern reduces the number of tools to choose from, reducing complexity. We iteratively developed CAPI, evaluating its understandability and usability in small studies with academic participants. When satisfied with the outcome, we performed a user-study with industry representatives to investigate the state-of-the-art in technology selection and the effectiveness of our proposed method. We find that technology selection is largely performed via trial and error, that CAPI is uniformly perceived as helpful, and that CAPI is able to reproduce the productive architectural environments of our participants.

Problem

• Simplifying architectural pattern selection for software projects
• Reducing complexity in tool selection via pattern-based recommendations
• Addressing technology trend awareness challenges in software design

Innovation

• Diagnostic decision tree for pattern suggestions
• Abstract patterns reduce tool selection complexity
• Iterative development with academic and industry evaluation