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：数字孪生常被定义为物理实体与对应虚拟实体的配对，后者根据用例需求模拟前者的特定方面。近年来，这一概念已在从大型到小型的各类高科技系统中，催生了从设计、验证到预测性维护的诸多应用场景。尽管在工业界和学术界日益普及，但数字孪生及其开发与维护方法却存在巨大差异。为更好理解这些异同，我们对19位来自工业界和学术界的专业人士进行了半结构化访谈研究，这些受访者均深度参与了相关数字孪生不同生命周期阶段的工作。本文基于八项研究问题，呈现了本研究的分析结果与发现。我们按研究问题逐一阐述发现。总体而言，我们发现数字孪生概念的理解、以及其开发与维护所使用的工具、技术和方法存在显著的非统一性。此外，鉴于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阶段，采纳更多软件工程实践、流程和专业知识具有显著的增长潜力。