跨组织SysML模型集成：挑战与AI支持任务综述
Cross-Organizational SysML Model Integration: A Survey of Challenges and AI-Supported Tasks
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中文总结 AI 辅助
本文通过29名MBSE利益相关者的问卷调查，揭示跨组织SysML模型集成面临多维对齐挑战，并指出AI在分析任务中受青睐但需人机协同，旨在增强而非取代工程责任。
中文摘要 AI 辅助
跨组织协作被广泛视为基于SysML的基于模型的系统工程（MBSE）的一个关键优势，然而从业者在交换和集成系统模型时仍面临持续挑战。与此同时，大型语言模型（LLMs）提高了对AI辅助模型理解和集成的期望，而可靠性和所需的人工监督仍然构成挑战。本文报告了一项针对参与跨组织协作的29名MBSE利益相关者的在线问卷调查结果。受访者在五点李克特量表上对八个预定义的集成挑战类别和六种AI支持任务类型进行了评分。结果表明，利益相关者认为模型集成是一个跨语义、行为、可追溯性和交换互操作性的多维对齐问题。这些看法因组织角色和集成参与频率而异。AI在语义结构分析和不一致检测等分析任务中被评为高度有用，受访者主要倾向于采用带强制验证的人机协同使用方式。这些发现激励AI支持增强而非取代SysML集成中的工程责任。
英文摘要
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.
