Hossain Muhammad Muctadir, David A. Manrique Negrin, Raghavendran Gunasekaran, Loek Cleophas, Mark van den Brand, Boudewijn R. Haverkort
{"title":"Current trends in digital twin development, maintenance, and operation: an interview study","authors":"Hossain Muhammad Muctadir, David A. Manrique Negrin, Raghavendran Gunasekaran, Loek Cleophas, Mark van den Brand, Boudewijn R. Haverkort","doi":"10.1007/s10270-024-01167-z","DOIUrl":null,"url":null,"abstract":"<p>Digital twins (DTs) 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.\n</p>","PeriodicalId":49507,"journal":{"name":"Software and Systems Modeling","volume":"2023 1","pages":""},"PeriodicalIF":2.0000,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Software and Systems Modeling","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s10270-024-01167-z","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
引用次数: 0
Abstract
Digital twins (DTs) 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.
期刊介绍:
We invite authors to submit papers that discuss and analyze research challenges and experiences pertaining to software and system modeling languages, techniques, tools, practices and other facets. The following are some of the topic areas that are of special interest, but the journal publishes on a wide range of software and systems modeling concerns:
Domain-specific models and modeling standards;
Model-based testing techniques;
Model-based simulation techniques;
Formal syntax and semantics of modeling languages such as the UML;
Rigorous model-based analysis;
Model composition, refinement and transformation;
Software Language Engineering;
Modeling Languages in Science and Engineering;
Language Adaptation and Composition;
Metamodeling techniques;
Measuring quality of models and languages;
Ontological approaches to model engineering;
Generating test and code artifacts from models;
Model synthesis;
Methodology;
Model development tool environments;
Modeling Cyberphysical Systems;
Data intensive modeling;
Derivation of explicit models from data;
Case studies and experience reports with significant modeling lessons learned;
Comparative analyses of modeling languages and techniques;
Scientific assessment of modeling practices