Digital twins (DT) are often defined as a pairing of a physical entity and a corresponding virtual entity 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. Although growing in popularity in both industry and academia, digital twins and the methodologies for developing and maintaining them differ vastly. To better understand these differences and similarities, we performed a semi-structured interview research study with 19 professionals from industry and academia who are closely associated with different lifecycle stages of the corresponding digital twins. In this paper, we present our analysis and findings from this study, which is based on eight research questions (RQ). We present our findings per research question. In general, we identified an overall lack of uniformity in terms of the understanding of digital twins and used tools, techniques, and methodologies for their development and maintenance. 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其对应虚拟实体的配对，后者根据具体用例模拟前者的某些特性。近年来，这一概念已支撑起从复杂系统的设计与验证到预测性维护等众多应用场景。尽管在工业界和学术界日益流行，但数字孪生及其开发维护方法仍存在显著差异。为深入理解这些异同，我们对来自工业界和学术界的19位专家进行了半结构化访谈研究，这些专家与相应数字孪生的全生命周期阶段紧密相关。本文基于八个研究问题（RQ）呈现了该研究的分析与发现。我们按研究问题逐一呈现结果。总体而言，我们发现学界与业界在数字孪生概念理解、开发维护工具技术及方法上缺乏统一性。此外，鉴于数字孪生属于软件密集型系统，我们认为在数字孪生生命周期的各个阶段，采用更多软件工程实践、流程与专业知识具有显著的增长潜力。