Expressing Measurement Uncertainty in OCL/UML Datatypes

Uncertainty is an inherent property of any measure or estimation performed in any physical setting, and therefore it needs to be considered when modeling systems that manage real data. Although several modeling languages permit the representation of measurement uncertainty for describing certain system attributes, these aspects are not normally incorporated into their type systems. Thus, operating with uncertain values and propagating uncertainty are normally cumbersome processes, difficult to achieve at the model level. This paper proposes an extension of OCL and UML datatypes to incorporate data uncertainty coming from physical measurements or user estimations into the models, along with the set of operations defined for the values of these types.

[1]  Bran Selic.  Beyond Mere Logic - A Vision of Modeling Languages for the 21st Century , 2015, MODELSWARD.

[2]  Manfred Broy,et al.  Challenges in modeling Cyber-Physical Systems , 2013, International Symposium on Information Processing in Sensor Networks.

[3]  Antonio Vallecillo,et al.  Using Physical Quantities in Robot Software Models , 2018, 2018 IEEE/ACM 1st International Workshop on Robotics Software Engineering (RoSE).

[4]  Marco Wolf,et al.  A modeling language for measurement uncertainty evaluation , 2009 .

[5]  Man Zhang,et al.  Uncertainty-Wise Cyber-Physical System test modeling , 2019, Software & Systems Modeling.

[6]  Antonio Vallecillo,et al.  Expressing Measurement Uncertainty in Software Models , 2016, 2016 10th International Conference on the Quality of Information and Communications Technology (QUATIC).

[7]  Antonio Vallecillo,et al.  Specifying quantities in software models , 2019, Inf. Softw. Technol..

[8]  Rick Salay,et al.  Managing requirements uncertainty with partial models , 2012, RE.

[9]  David Garlan,et al.  Software engineering in an uncertain world , 2010, FoSER '10.

[10]  B. Kosko.  Fuzziness vs. probability , 1990 .

[11]  Martin Gogolla,et al.  On OCL-based imperative languages , 2014, Sci. Comput. Program..

[12]  Edward A. Lee.  Cyber Physical Systems: Design Challenges , 2008, 2008 11th IEEE International Symposium on Object and Component-Oriented Real-Time Distributed Computing (ISORC).

[13]  Pierre America,et al.  Inheritance and Subtyping in a Parallel Object-Oriented Language , 1987, ECOOP.

[14]  Irene Barba,et al.  Generating optimized configurable business process models in scenarios subject to uncertainty , 2015, Inf. Softw. Technol..

[15]  B. D. Hall,et al.  Component interfaces that support measurement uncertainty , 2006, Comput. Stand. Interfaces.

[16]  Bev Littlewood,et al.  The role of models in managing the uncertainty of software-intensive systems , 1995 .

[17]  Sam Malek,et al.  Uncertainty in Self-Adaptive Software Systems , 2010, Software Engineering for Self-Adaptive Systems.

[18]  Martin Gogolla,et al.  Model Validation and Verification Options in a Contemporary UML and OCL Analysis Tool , 2016, Modellierung.

[19]  Jeannette M. Wing,et al.  A behavioral notion of subtyping , 1994, TOPL.

[20]  Alfonso Pierantonio,et al.  Managing uncertainty in bidirectional model transformations , 2015, SLE.

[21]  Martin Gogolla,et al.  USE: A UML-based specification environment for validating UML and OCL , 2007, Sci. Comput. Program..

[22]  Bran Selic,et al.  Understanding Uncertainty in Cyber-Physical Systems: A Conceptual Model , 2016, ECMFA.

[23]  Rick Salay,et al.  Partial models: Towards modeling and reasoning with uncertainty , 2012, 2012 34th International Conference on Software Engineering (ICSE).

[24]  Juan Manuel Fernández Peña,et al.  Unified Modeling Language Unified Modeling Language , 2006 .