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Issue No. 06 - November/December (2010 vol. 36)
ISSN: 0098-5589
pp: 865-877
Klaus Krogmann , Karlsruhe Institute of Technology (KIT), Karlsruhe
Michael Kuperberg , Karlsruhe Institute of Technology (KIT), Karlsruhe
Ralf Reussner , Karlsruhe Institute of Technology (KIT), Karlsruhe
In component-based software engineering, existing components are often reused in new applications. Correspondingly, the response time of an entire component-based application can be predicted from the execution durations of individual component services. These execution durations depend on the runtime behavior of a component which itself is influenced by three factors: the execution platform, the usage profile, and the component wiring. To cover all relevant combinations of these influencing factors, conventional prediction of response times requires repeated deployment and measurements of component services for all such combinations, incurring a substantial effort. This paper presents a novel comprehensive approach for reverse engineering and performance prediction of components. In it, genetic programming is utilized for reconstructing a behavior model from monitoring data, runtime bytecode counts, and static bytecode analysis. The resulting behavior model is parameterized over all three performance-influencing factors, which are specified separately. This results in significantly fewer measurements: The behavior model is reconstructed only once per component service, and one application-independent bytecode benchmark run is sufficient to characterize an execution platform. To predict the execution durations for a concrete platform, our approach combines the behavior model with platform-specific benchmarking results. We validate our approach by predicting the performance of a file sharing application.
Genetic search, genetic programming, reverse engineering, performance prediction, bytecode benchmarking.

K. Krogmann, M. Kuperberg and R. Reussner, "Using Genetic Search for Reverse Engineering of Parametric Behavior Models for Performance Prediction," in IEEE Transactions on Software Engineering, vol. 36, no. , pp. 865-877, 2010.
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