Year: 2025
Type: article
Abstract: ABSTRACT Moving systems engineering's center of gravity from text documents to digital models, model‐based system engineering (MBSE) facilitates collaboration, reduces errors, and improves system understanding, but systems engineers often face challenges when they start working with MBSE, as they need to consider and model textual requirements as a basis for the system architecture. Synchronizing requirements with the system model can address critical questions, such as which requirements are assigned and covered in the system model, what effort is required to meet these requirements, how to manage changes to requirements, and how to effectively trace them in the system. Object Process Methodology (OPM, ISO 19450) is an MBSE language and methodology for modeling systems, which encapsulates function, structure, and behavior within a single model. The research goal is to expand the MBSE methodology by modeling requirements that are linked to system model artifacts. The objective is minimizing misunderstandings between stakeholders’ text‐based requirements and the engineered model while improving the ability to track changes in requirements and artifacts. An OPM‐based model, OPM‐R, which unifies system requirements and architecture, was developed and evaluated through (1) interviews with seven experts who compared the current text‐based approach and the proposed one based on the requirements of three test system models and (2) a researcher diary. Findings indicate that OPM‐R is preferred over the text‐based approach, as it reduces misunderstandings and gaps in requirements, enhancing the ability to trace coverage of system artifacts and track changes in requirements.
Source: Systems Engineering
DOI: 10.1002/sys.70035
Authors: Liat Katzir, Shahaf Rocker Yoel, Dov Dori
Institution: Technion – Israel Institute of Technology
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,140
OpenAlex ID: W7117305803