Cross-Organizational SysML Model Integration: A Survey of Challenges and AI-Supported Tasks

2026-09-29

AI Summary

This study addresses the semantic, behavioral, and interoperability challenges inherent in cross-organizational SysML model integration. By surveying Model-Based Systems Engineering (MBSE) stakeholders through online questionnaires employing Likert scales, we evaluate the supportive utility of large language models (LLMs). Our findings reveal multidimensional alignment difficulties and identify high-value scenarios where AI excels in semantic analysis and inconsistency detection. Consequently, this work proposes an AI-assisted paradigm that augments rather than replaces engineering accountability. The core contribution lies in establishing a human-in-the-loop collaborative verification mechanism, providing both empirical evidence and a practical framework for AI-empowered complex systems engineering.

Abstract

Cross-organizational collaboration is widely regarded as a key promise of SysML-based Model-Based Systems Engineering (MBSE), yet practitioners still face persistent challenges when exchanging and integrating system models. In parallel, Large Language Models (LLMs) raise expectations for AI-assisted model understanding and integration, while reliability and required human oversight continue to pose challenges. This paper reports the results of an online questionnaire survey with 29 MBSE stakeholders involved in cross-organizational collaboration. Respondents rated eight predefined integration challenge categories and six AI-supported task types on five-point Likert scales. The results indicate that stakeholders perceive model integration as a multi-dimensional alignment problem across semantics, behavior, traceability, and exchange interoperability. These perceptions vary by organizational role and frequency of integration involvement. AI is rated highly useful for analysis tasks such as semantic structure analysis and inconsistency detection, and respondents predominantly prefer human-in-the-loop use with mandatory verification. These findings motivate AI support that enhances, rather than replaces, engineering responsibility in SysML-based integration.

Problem

• Cross-organizational collaboration
• SysML model integration
• Model-Based Systems Engineering
• Large Language Models
• Human-in-the-loop

Innovation

• SysML Model Integration
• Large Language Models
• Cross-Organizational Collaboration
• Human-in-the-loop
• Model-Based Systems Engineering