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Performance and reliability prediction for evolving service-oriented software systems

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Abstract

During software system evolution, software architects intuitively trade off the different architecture alternatives for their extra-functional properties, such as performance, maintainability, reliability, security, and usability. Researchers have proposed numerous model-driven prediction methods based on queuing networks or Petri nets, which claim to be more cost-effective and less error-prone than current practice. Practitioners are reluctant to apply these methods because of the unknown prediction accuracy and work effort. We have applied a novel model-driven prediction method called Q-ImPrESS on a large-scale process control system from ABB consisting of several million lines of code. This paper reports on the achieved performance prediction accuracy and reliability prediction sensitivity analyses as well as the effort in person hours for achieving these results.

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Notes

  1. http://www.q-impress.eu/wordpress/demonstrators/abb-demonstrator/

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Acknowledgements

This work was funded within the Q-ImPrESS research project (FP7-215013) by the European Union under the Information and Communication Technologies priority of FP7.

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Authors and Affiliations

  1. Industrial Software Systems, ABB Corporate Research, Wallstadter Str. 59, 68526, Ladenburg, Germany

    Heiko Koziolek & Bastian Schlich

  2. University of Paderborn, Paderborn, Germany

    Steffen Becker

  3. Forschungszentrum Informatik (FZI), Karlsruhe, Germany

    Michael Hauck

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  1. Heiko Koziolek
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  2. Bastian Schlich
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  3. Steffen Becker
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  4. Michael Hauck
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Correspondence to Heiko Koziolek.

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Koziolek, H., Schlich, B., Becker, S. et al. Performance and reliability prediction for evolving service-oriented software systems. Empir Software Eng 18, 746–790 (2013). https://doi.org/10.1007/s10664-012-9213-0

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