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Incorporating measurement uncertainty into OCL/UML primitive datatypes

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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.

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Notes

  1. This is inspired by how various simple robots operate, in particular, Ozobot robots (https://ozobot.com).

  2. Operations on basic datatypes normally use infix notation (e.g., \(x+y\), \(a<b\), \(P \ {\texttt {and}}\ Q\)). This is the notation that we already support in our USE implementation for the newly defined types (UReal, UBoolean, etc.). However, other languages that we have used to implement these new types (e.g., Java) do not support infix notation. Therefore, in the following we will use either an infix or prefix notation (x.add(y), a.lt(b), P.and(Q)) for the operations of these types, depending on the context and the language used.

  3. Although a confidence value of 0.9 is rather low, it may occur due to restricted visibility on foggy days, for instance.

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Acknowledgements

This work was partially funded by the Spanish Research Projects TIN2014-52034-R, TIN2016-75944-R and PGC2018-094905-B-I00. We are really thankful to the reviewers of this paper, for their insightful comments and suggestions.

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Authors and Affiliations

  1. Universidad de Málaga, Málaga, Spain

    Manuel F. Bertoa, Nathalie Moreno & Antonio Vallecillo

  2. IN3, Open University of Catalonia, Barcelona, Spain

    Loli Burgueño

  3. Institut LIST, CEA, Université Paris-Saclay, Paris, France

    Loli Burgueño

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  1. Manuel F. Bertoa
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  2. Loli Burgueño
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  3. Nathalie Moreno
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  4. Antonio Vallecillo
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Correspondence to Loli Burgueño.

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Communicated by A. Pierantonio, A. Anjorin, S. Trujillo, and H. Espinoza.

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Bertoa, M.F., Burgueño, L., Moreno, N. et al. Incorporating measurement uncertainty into OCL/UML primitive datatypes. Softw Syst Model 19, 1163–1189 (2020). https://doi.org/10.1007/s10270-019-00741-0

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