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Bibliographic Details
Main Author: Waters, Gabriella
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2507.04555
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author Waters, Gabriella
author_facet Waters, Gabriella
contents Digital twins have emerged as a powerful technology for modeling and simulating complex systems across various domains (Fuller et al., 2020; Tao et al., 2019). As virtual representations of physical assets, processes, or systems, digital twins enable real-time monitoring, predictive analysis, and optimization. However, as digital twins become more sophisticated and integral to decision-making processes, ensuring their accuracy, reliability, and ethical implementation is essential. This paper presents a comprehensive framework for the Testing, Evaluation, Verification and Validation (TEVV) of digital twins to address the unique challenges posed by these dynamic and complex virtual models.
format Preprint
id arxiv_https___arxiv_org_abs_2507_04555
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Testing, Evaluation, Verification and Validation (TEVV) of Digital Twins: A Comprehensive Framework
Waters, Gabriella
Software Engineering
Digital twins have emerged as a powerful technology for modeling and simulating complex systems across various domains (Fuller et al., 2020; Tao et al., 2019). As virtual representations of physical assets, processes, or systems, digital twins enable real-time monitoring, predictive analysis, and optimization. However, as digital twins become more sophisticated and integral to decision-making processes, ensuring their accuracy, reliability, and ethical implementation is essential. This paper presents a comprehensive framework for the Testing, Evaluation, Verification and Validation (TEVV) of digital twins to address the unique challenges posed by these dynamic and complex virtual models.
title Testing, Evaluation, Verification and Validation (TEVV) of Digital Twins: A Comprehensive Framework
topic Software Engineering
url https://arxiv.org/abs/2507.04555