Agile framework for Digital Twin verification and validation: Addressing complexity challenges in cross-disciplinary hybrid systems

The deployment of Digital Twins (DTs) for decision-making support is rapidly expanding in different industries. DTs are cross-disciplinary hybrid systems that combine elements from various engineering domains. Their diverse services require hybrid modeling and simulation techniques to replicate complex system behaviors. Therefore, their inherent complexity requires comprehensive verification and validation frameworks. In the state-of-the-art, verification and validation of DTs are mostly narrowed down to simulation models. However, it needs to extend beyond that and encompass a broader scope. Thus, it is crucial to accurately identify the scope of verification and validation that encompasses data integration, model fidelity, and service functionality. The main contribution of this paper is to propose a verification and validation framework that synergistically integrates white-box and black-box testing techniques to systematically verify and validate data, models, and services of DTs throughout the development life cycle. It leverages an agile methodology to enable iterative and continuous development, verification, and validation. It employs white-box testing using formal methods and black-box testing using software testing techniques, respectively, for verification and validation. A case study is presented that utilizes the framework to develop, verify, and validate a DT. Ultimately, a set of defined evaluation metrics is tracked throughout the development life cycle in the case study to demonstrate the effectiveness of the agile V&V framework proposed.