  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.
