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Empirical Software Engineering
Erschienen in: Empirical Software Engineering 4/2013

01.08.2013 | Experience Report

Performance and reliability prediction for evolving service-oriented software systems

Industrial experience report

verfasst von: Heiko Koziolek, Bastian Schlich, Steffen Becker, Michael Hauck

Erschienen in: Empirical Software Engineering | Ausgabe 4/2013

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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.
Vorheriger Artikel Do background colors improve program comprehension in the #ifdef hell?
Nächster Artikel STAMINA: a competition to encourage the development and assessment of software model inference techniques

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Fußnoten
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Metadaten
Titel
Performance and reliability prediction for evolving service-oriented software systems
Industrial experience report
verfasst von
Heiko Koziolek
Bastian Schlich
Steffen Becker
Michael Hauck
Publikationsdatum
01.08.2013
Verlag
Springer US
Erschienen in
Empirical Software Engineering / Ausgabe 4/2013
Print ISSN: 1382-3256
Elektronische ISSN: 1573-7616
DOI
https://doi.org/10.1007/s10664-012-9213-0

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