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Clustering Variation Points in MATLAB/Simulink Models Using Reverse Signal Propagation Analysis

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Mastering Scale and Complexity in Software Reuse (ICSR 2017)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 10221))

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Abstract

Model-based languages such as MATLAB/Simulink play an essential role in the model-driven development of software systems. During their development, these systems can be subject to modification numerous times. For large-scale systems, to manually identify performed modifications is infeasible. However, their precise identification and subsequent validation is essential for the evolution of model-based systems. If not fully identified, modifications may cause unaccountable behavior as the system evolves and their redress can significantly delay the entire development process. In this paper, we propose a fully automated technique called Reverse Signal Propagation Analysis, which identifies and clusters variations within evolving MATLAB/Simulink models. With each cluster representing a clearly delimitable variation point between models, we allow model engineers not only to specifically focus on single variations, but by using their domain knowledge, to also relate and verify them. By identifying variation points, we assist model engineers in validating the respective parts and reduce the risk of improper system behavior as the system evolves. To assess the applicability of our technique, we present a feasibility study with real-world models from the automotive domain and show our technique to be very fast and highly precise.

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Notes

  1. 1.

    Mathworks®- https://mathworks.com/products/simulink/ - Nov. 2016.

  2. 2.

    Mathworks®- https://www.mathworks.com/products/stateflow/ - Nov. 2016.

  3. 3.

    We provide further details and a screencast on our website:

    https://www.isf.cs.tu-bs.de/cms/team/schlie/material/icsr17rspa/.

  4. 4.

    Oracle Systems®- https://www.java.com/en/ - Nov. 2016.

  5. 5.

    Eclipse Foundation®- https://eclipse.org/ - Nov. 2016.

  6. 6.

    Eclipse Foundation®- https://www.eclipse.org/modeling/emf/ - Nov. 2016.

  7. 7.

    http://spes2020.informatik.tu-muenchen.de/spes_xt-home.html - Nov. 2016.

  8. 8.

    \(153=18*(17/2)\), since the input order does not matter.

  9. 9.

    https://www.conqat.org.

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Acknowledgments

We would like to thank Remo Lachmann and Christoph Seidl for their strong support and guidance on this paper.

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Authors and Affiliations

  1. TU Braunschweig, Braunschweig, Germany

    Alexander Schlie, David Wille & Ina Schaefer

  2. Stellenbosch University, Stellenbosch, South Africa

    Loek Cleophas

  3. TU Eindhoven, Eindhoven, The Netherlands

    Loek Cleophas

Authors
  1. Alexander Schlie
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  2. David Wille
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  3. Loek Cleophas
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  4. Ina Schaefer
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Corresponding author

Correspondence to Alexander Schlie .

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Editors and Affiliations

  1. Lero - University of Limerick, Limerick, Ireland

    Goetz Botterweck

  2. Federal University of Rio de Janeiro, Rio de Janeiro, Brazil

    Claudia Werner

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Schlie, A., Wille, D., Cleophas, L., Schaefer, I. (2017). Clustering Variation Points in MATLAB/Simulink Models Using Reverse Signal Propagation Analysis. In: Botterweck, G., Werner, C. (eds) Mastering Scale and Complexity in Software Reuse. ICSR 2017. Lecture Notes in Computer Science(), vol 10221. Springer, Cham. https://doi.org/10.1007/978-3-319-56856-0_6

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  • DOI: https://doi.org/10.1007/978-3-319-56856-0_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-56855-3

  • Online ISBN: 978-3-319-56856-0

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