Incorporating measurement uncertainty into OCL/UML primitive datatypes

• Published in: Software and systems modeling Vol. 19; no. 5; pp. 1163 - 1189
• Main Authors: Bertoa, Manuel F., Burgueño, Loli, Moreno, Nathalie, Vallecillo, Antonio
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-09-2020; Springer Nature B.V
• Subjects: Compilers; Computer models; Computer Science; Information Systems Applications (incl.Internet); Interpreters; IT in Business; Modelling; Programming Languages; Programming Techniques; Representations; Software Engineering; Software Engineering/Programming and Operating Systems; Special Section Paper; Uncertainty; OCL; Primitive datatypes; Measurement uncertainty; UML
• ISSN: 1619-1366; 1619-1374
• DOI: 10.1007/s10270-019-00741-0

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.