A Generalized Feature Model for Digital Twins
Philipp Zech, Yanis Mair, Michael Vierhauser, Pablo Oliveira Antonino, Frank Schnicke, Tony Clark
TL;DR
A generalized feature model for Digital Models, Shadows, and Twins is developed based on a systematic mapping study of existing literature and conjecture that it advances the field around Digital Twins by facilitating informed decision-making during design and fostering verification of Digital Twins by delivering a model-based foundation for test case inference.
Abstract
The adoption of Digital Twin technologies is rapidly expanding in diverse industrial, economic, and societal domains. Over the past decade, a multitude of studies, surveys, and investigations have been conducted, examining the nature, applications, and advantages of Digital Twins. However, up until now, no proposal for a comprehensive feature model exists that effectively captures the mandatory and optional features of Digital Twins. To address this shortcoming, in this article, we present a general feature model for Digital Twins. Based on a systematic mapping study of existing literature, we developed a generalized feature model for Digital Models, Shadows, and Twins. To assess the validity of our proposed feature model, we have applied them to three use cases from the emergency, vehicular, and manufacturing domain. We conjecture that our proposed general feature model advances the field around Digital Twins by facilitating informed decision-making during design, enabling improved model-driven development of Digital Twins, and, eventually, fostering verification~\&~validation of Digital Twins by delivering a model-based foundation for test case inference.