SysML model integration faces varied challenges with AI support valued
Cross-Organizational SysML Model Integration: A Survey of Challenges and AI-Supported Tasks
Software EngineeringArtificial Intelligence
Summary
Combining system models from different organizations is hard because they have to match in meaning, behavior, and way of sharing data. The authors surveyed people who work on this and found that the challenges depend on their roles and how often they do integration. People think AI can help spot problems in models and understand their structure, but they want to keep humans checking the AI’s work. This suggests AI tools should assist engineers without taking over their responsibilities.
What this means in practice
- •For systems engineering teams: Improve model integration by using AI tools that analyze semantic structures and detect inconsistencies while keeping engineers in control.
- •For software integrators: Design workflows that combine AI-supported model analysis with human verification to reduce errors in cross-company system integration.
A survey. It maps existing work.
Authors
Zirui Li, Torsten Brix, Stephan Husung
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.