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Discerning Quantities from Units of Measurement
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Informatics and Media.ORCID iD: 0000-0002-1970-2884
2022 (English)In: Proceedings of the 10th International Conference on Model-Driven Engineering and Software Development, Portugal: INSTICC Press, 2022, p. 105-115Conference paper, Published paper (Refereed)
Abstract [en]

In scientific and engineering applications, physical quantities embodied as units of measurement (UoM) are frequently used. While managing units of measurement is a fairly mature topic in software engineering, more subtle metrological concepts such as named quantities have had little traction within the scientific programming community. The loss of the Mars climate orbiter, attributed to a confusion between the metric and imperial unit systems, popularised the disastrous consequences of incorrectly handling measurement values. This has led to the development of a large number of libraries, languages and tools to ensure developers can specify and validate UoM information in their designs and codes. However these systems do not differentiate between quantities and dimensions. For instance torque and work, which share the same UoM, can not be interchanged because they do not represent the same entity. We present a named quantity layer that sits on top of a dimension checker and unit con verter ensuring values of different quantities are correctly managed without undue restrictions. Our quantity algebra works alongside the unit dimensions to ensure we maintain named quantities when we perform arithmetic and function calls.

Place, publisher, year, edition, pages
Portugal: INSTICC Press, 2022. p. 105-115
Series
Proceedings : International Conference on Model-Driven Engineering and Software Development (MODELSWARD), ISSN 2184-4348
Keywords [en]
Quantities, Units of Measurement, Quantity Checking, Dimensional Analysis
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:uu:diva-492458DOI: 10.5220/0010971300003119OAI: oai:DiVA.org:uu-492458DiVA, id: diva2:1724053
Conference
MODELSWARD 2022, 6-8 February, online
Available from: 2023-01-04 Created: 2023-01-04 Last updated: 2023-08-25Bibliographically approved

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McKeever, Steve

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CiteExportLink to record
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  • apa
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