Incorporating measurement uncertainty into OCL/UML primitive datatypes

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.

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

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

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

[4]  B. D. Finetti,et al.  Theory of Probability: A Critical Introductory Treatment , 2017 .

[5]  FEDERICO CICCOZZI,et al.  Adopting MDE for Specifying and Executing Civilian Missions of Mobile Multi-Robot Systems , 2016, IEEE Access.

[6]  Earl T. Barr,et al.  Uncertainty, risk, and information value in software requirements and architecture , 2014, ICSE.

[7]  Martin Gogolla,et al.  Adding Random Operations to OCL , 2017, MODELS.

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

[9]  Vladimir I. Levenshtein,et al.  Binary codes capable of correcting deletions, insertions, and reversals , 1965 .

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

[11]  Samuel Greengard,et al.  The Internet of Things , 2015 .

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

[13]  Jordi Cabot,et al.  Model-Driven Software Engineering in Practice , 2017, Synthesis Lectures on Software Engineering.

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

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

[16]  Raymond T. Boute.  A heretical view on type embedding , 1990, SIGP.

[17]  Lionel C. Briand,et al.  OCLR: A More Expressive, Pattern-Based Temporal Extension of OCL , 2014, ECMFA.

[18]  Muhammad Zohaib Z. Iqbal,et al.  AspectOCL: Extending OCL for Crosscutting Constraints , 2015, ECMFA.

[19]  Pieter J. Mosterman,et al.  Industry 4.0 as a Cyber-Physical System study , 2016, Software & Systems Modeling.

[20]  Olivier Casse,et al.  SysML: Object Management Group (OMG) Systems Modeling Language , 2018 .

[21]  Antonio Vallecillo,et al.  Expressing Measurement Uncertainty in OCL/UML Datatypes , 2018, ECMFA.

[22]  Erik Ernst,et al.  Family Polymorphism , 2001, ECOOP.

[23]  Selma Kchir,et al.  RobotML for industrial robots: Design and simulation of manipulation scenarios , 2016, 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA).

[24]  Marcel Kyas,et al.  An Extended Type System for OCL Supporting Templates and Transformations , 2005, FMOODS.

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

[26]  Antonio Vallecillo,et al.  Expressing Confidence in Models and in Model Transformation Elements , 2018, MoDELS.

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

[28]  Zoran Budimac,et al.  Redefining Software Quality Metrics to XML Schema Needs , 2013, SQAMIA.

[29]  Kim B. Bruce,et al.  Semantics-Driven Language Design: Statically Type-safe Virtual Types in Object-oriented Languages , 1999, MFPS.

[30]  Hans-Jürgen Zimmermann,et al.  Fuzzy Set Theory - and Its Applications , 1985 .

[31]  Rick Salay,et al.  Managing requirements uncertainty with partial models , 2012, 2012 20th IEEE International Requirements Engineering Conference (RE).

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

[33]  Jose-Norberto Mazón,et al.  Extending OCL for OLAP querying on conceptual multidimensional models of data warehouses , 2010, Inf. Sci..

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

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

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

[37]  Bran Selic,et al.  The Pragmatics of Model-Driven Development , 2003, IEEE Softw..

[38]  P. America,et al.  A behavioural approach to subtyping in object-oriented programming languages , 1991 .

[39]  Fernando Orejas,et al.  GSBL: An Algebraic Specification Language Based on Inheritance , 1988, ECOOP.

[40]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[41]  Jim Steel,et al.  On Model Subtyping , 2012, ECMFA.

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

[43]  Henri Prade,et al.  Fuzzy sets and probability: misunderstandings, bridges and gaps , 1993, [Proceedings 1993] Second IEEE International Conference on Fuzzy Systems.

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

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

[46]  Kathleen V. Diegert,et al.  Error and uncertainty in modeling and simulation , 2002, Reliab. Eng. Syst. Saf..

[47]  Rick Salay,et al.  Software Product Lines with Design Choices: Reasoning about Variability and Design Uncertainty , 2017, 2017 ACM/IEEE 20th International Conference on Model Driven Engineering Languages and Systems (MODELS).

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

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

[50]  Jordi Cabot,et al.  Specifying Aggregation Functions in Multidimensional Models with OCL , 2010, ER.

[51]  Federico Ciccozzi,et al.  UML-Based Development of Embedded Real-Time Software on Multi-Core in Practice: Lessons Learned and Future Perspectives , 2016, IEEE Access.

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

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