Incorporating measurement uncertainty into OCL/UML primitive datatypes
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SPECIAL SECTION PAPER
Incorporating measurement uncertainty into OCL/UML primitive datatypes Manuel F. Bertoa1
· Loli Burgueño2,3
· Nathalie Moreno1
· Antonio Vallecillo1
Received: 5 December 2018 / Revised: 31 May 2019 / Accepted: 20 June 2019 © Springer-Verlag GmbH Germany, part of Springer Nature 2019
Abstract The correct representation of the relevant properties of a system is an essential requirement for the effective use and wide adoption of model-based practices in industry. Uncertainty is one of the inherent properties of any measurement or estimation that is obtained in any physical setting; as such, it must be considered when modeling software systems deal with real data. Although a few modeling languages enable the representation of measurement uncertainty, these aspects are not normally incorporated into their type systems. Therefore, operating with uncertain values and propagating their uncertainty become cumbersome processes, which hinder their realization in real environments. This paper proposes an extension of OCL/UML primitive datatypes that enables the representation of the uncertainty that comes from physical measurements or user estimates into the models, together with an algebra of operations that are defined for the values of these types. Keywords Measurement uncertainty · OCL · UML · Primitive datatypes
1 Introduction The emergence of cyber-physical systems (CPSs) [9] and the internet of things (IoT) [29], which are examples of systems that must interact with the physical world, and the current industrial practices, such as the Industry 4.0 [50], have made evident the need to faithfully represent extra-functional properties in models of systems and their elements. This is an essential requirement for leveraging some of the potential benefits of model-based software engineering (MBSE) [8,
Communicated by A. Pierantonio, A. Anjorin, S. Trujillo, and H. Espinoza.
B
Loli Burgueño [email protected] Manuel F. Bertoa [email protected] Nathalie Moreno [email protected] Antonio Vallecillo [email protected]
1
Universidad de Málaga, Málaga, Spain
2
IN3, Open University of Catalonia, Barcelona, Spain
3
Institut LIST, CEA, Université Paris-Saclay, Paris, France
16,59] in industrial settings—particularly if MBSE is indeed going to become widely adopted in practice. It has been claimed that the expressiveness of a model is as important as the formality of its expression [47]. This expressiveness is determined by the suitability of the language for describing the concepts of the problem domain or for implementing the design. Although in software engineering a variety of modeling languages are tailored to various problems, they may not be well suited for capturing key aspects of the real world [9,43,60] and, in particular, for managing data uncertainty in a natural manner. One relevant issue is related to the uncertainty of the attribute values of the modeled elements, especially when dealing with physical quantities such as lengths, times, weights, or other measurable elements. Data uncertaint
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