Integration of MBSE Elements and Automation with System Development Processes for Advanced Performance & Efficiency

Abstract

This research presents the culmination of a progressive study, detailing the results of integrating advanced Model Based Systems Engineering (MBSE) elements with system automation to enhance stakeholder selection processes. The importance of precise system selection remains paramount for optimal user safety and comprehension. By exploring the robust capabilities of Artificial Intelligence (AI), Machine Learning (ML), and automation tools, this study demonstrates significant improvements in developmental outputs over time. MBSE serves as a revolutionary methodology possessing complex capabilities that fundamentally elevate system performance and development. This methodology is successfully implemented through rigorous requirements writing, the formulation of architectural patterns, the establishment of comprehensive pattern libraries, and stringent verification processes. The strategic combination of these MBSE components, systems thinking, and automated intelligence functions in parallel to systematically improve selection processes for diverse users and stakeholders. Consequently, this final paper focuses on how these compounded systems engineering elements operate cohesively to guarantee that users select the most vital, beneficial systems tailored strictly to their preferences and operational needs. Furthermore, this study illustrates how the deployment of architectural patterns and pattern libraries seamlessly verifies requirements to output exceptionally performant architectures. Because modern architectures typically function as systems of systems requiring both high-level and low-level decomposition, this methodology efficiently promotes enhanced operational efficiency. While applicable across any diverse domain, this research specifically applies the refined framework to home security systems, definitively demonstrating the enhanced deliverables produced by merging MBSE, artificial intelligence, and advanced automation elements.

Keywords: Model-based Systems Engineering, Architecture Patterns, Requirements Pattern Library, Artificial Intelligence, Automation

DOI: 10.54941/ahfe1007690