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Enabling Model Testing of Cyber-Physical Systems

Authors: Carlos A. González; Mojtaba Varmazyar; Shiva Nejati 0001; Lionel C. Briand; Yago Isasi;

Enabling Model Testing of Cyber-Physical Systems

Abstract

Applying traditional testing techniques to Cyber-Physical Systems (CPS) is challenging due to the deep intertwining of software and hardware, and the complex, continuous interactions between the system and its environment. To alleviate these challenges we propose to conduct testing at early stages and over executable models of the system and its environment. Model testing of CPSs is however not without difficulties. The complexity and heterogeneity of CPSs renders necessary the combination of different modeling formalisms to build faithful models of their different components. The execution of CPS models thus requires an execution framework supporting the cosimulation of different types of models, including models of the software (e.g., SysML), hardware (e.g., SysML or Simulink), and physical environment (e.g., Simulink). Furthermore, to enable testing in realistic conditions, the cosimulation process must be (1) fast, so that thousands of simulations can be conducted in practical time, (2) controllable, to precisely emulate the expected runtime behavior of the system and, (3) observable, by producing simulation data enabling the detection of failures. To tackle these challenges, we propose a SysML-based modeling methodology for model testing of CPSs, and an efficient SysML-Simulink cosimulation framework. Our approach was validated on a case study from the satellite domain.

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Luxembourg
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Keywords

Sciences informatiques, : Computer science [C05] [Engineering, computing & technology], Cyber-Physical Systems, Software Testing, : Sciences informatiques [C05] [Ingénierie, informatique & technologie], Computer science, Model-Based Systems Engineering, Engineering, computing & technology, Ingénierie, informatique & technologie

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    popularity
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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
28
Top 10%
Top 10%
Top 10%
Green