Digital twins (DT) are often defined as a pairing of a physical entity and a corresponding virtual entity (VE), mimicking certain aspects of the former depending on the use-case. In recent years, this concept has facilitated numerous use-cases ranging from design to validation and predictive maintenance of large and small high-tech systems. Various heterogeneous cross-domain models are essential for such systems and model-driven engineering plays a pivotal role in the design, development, and maintenance of these models. We believe models and model-driven engineering play a similarly crucial role in the context of a VE of a DT. Due to the rapidly growing popularity of DTs and their use in diverse domains and use-cases, the methodologies, tools, and practices for designing, developing, and maintaining the corresponding VEs differ vastly. To better understand these differences and similarities, we performed a semi-structured interview research with 19 professionals from industry and academia who are closely associated with different lifecycle stages of digital twins. In this paper, we present our analysis and findings from this study, which is based on seven research questions. In general, we identified an overall lack of uniformity in terms of the understanding of digital twins and used tools, techniques, and methodologies for the development and maintenance of the corresponding VEs. Furthermore, considering that digital twins are software intensive systems, we recognize a significant growth potential for adopting more software engineering practices, processes, and expertise in various stages of a digital twin's lifecycle.

翻译：数字孪生（DT）通常被定义为物理实体与对应虚拟实体（VE）的配对，根据具体用例模拟前者的某些方面。近年来，这一概念已催生从设计验证到大型和中小型高技术系统预测维护的众多应用案例。此类系统需要多种异构跨领域模型，而模型驱动工程在模型设计、开发和维护中发挥着关键作用。我们认为，在数字孪生的虚拟实体语境下，模型与模型驱动工程同样具有至关重要的地位。由于数字孪生技术快速普及且广泛应用于不同领域和用例，设计、开发与维护对应虚拟实体的方法论、工具和实践存在显著差异。为更深入理解这些异同点，我们对来自工业界和学术界的19位与数字孪生不同生命周期阶段密切相关的专业人士进行了半结构化访谈研究。本文基于七个研究问题呈现了该项研究的分析与发现。总体而言，我们发现各方对数字孪生的理解存在普遍不统一性，开发与维护对应虚拟实体所使用的工具、技术和方法论也缺乏一致性。此外，考虑数字孪生是软件密集型系统，我们认识到在数字孪生生命周期的各个阶段，采用更多软件工程实践、流程和专业知识的潜力巨大。