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Testing Autogenerated OPC UA NodeSet Models for Product Variants in Industry

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Software Quality: Future Perspectives on Software Engineering Quality (SWQD 2021)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 404))

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Abstract

Product line management activities have to ensure that offered product options are valid and compatible. With the arise of the Internet of Things (IoT) movement not only the own product compatibility has to be managed by the vendors anymore, but also the compliance and openness to standardized interfaces has to be supported as well. The Machine to Machine (M2M) communication protocol standard Open Platform Communications Unified Architecture (OPC UA) has received great attention in the field of mechanical engineering recently. In this industrial experience report we describe our approach how to support the testing of automatically generated models for OPC UA, by applying test case generation at the integration level. We show the feasibility of our approach and report about found issues, discuss some general findings and provide an outlook for future work.

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Notes

  1. 1.

    https://reference.opcfoundation.org/.

  2. 2.

    https://velocity.apache.org/.

  3. 3.

    https://junit.org/junit5/.

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Acknowledgements

The research reported in this paper has been funded by the Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology (BMK), the Federal Ministry for Digital and Economic Affairs (BMDW), and the Province of Upper Austria in the frame of the COMET - Competence Centers for Excellent Technologies Programme managed by Austrian Research Promotion Agency FFG.

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Correspondence to Claus Klammer .

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Klammer, C., Wetzlmaier, T., Pfeiffer, M., Steiner, T., Konnerth, M. (2021). Testing Autogenerated OPC UA NodeSet Models for Product Variants in Industry. In: Winkler, D., Biffl, S., Mendez, D., Wimmer, M., Bergsmann, J. (eds) Software Quality: Future Perspectives on Software Engineering Quality. SWQD 2021. Lecture Notes in Business Information Processing, vol 404. Springer, Cham. https://doi.org/10.1007/978-3-030-65854-0_2

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  • DOI: https://doi.org/10.1007/978-3-030-65854-0_2

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  • Online ISBN: 978-3-030-65854-0

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