Using Genetic Search for Reverse Engineering of Parametric Behaviour Models for Performance Prediction

Created by W.Langdon from gp-bibliography.bib Revision:1.8081

@Article{Krogmann:2010:ieeeTSE,

• author = "Klaus Krogmann and Michael Kuperberg and Ralf Reussner",
• title = "Using Genetic Search for Reverse Engineering of Parametric Behaviour Models for Performance Prediction",
• journal = "IEEE Transactions on Software Engineering",
• year = "2010",
• month = nov # "/" # dec,
• volume = "36",
• number = "6",
• pages = "865--877",
• abstract = "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 run time behaviour 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 used for reconstructing a behavior model from monitoring data, runtime bytecode counts, and static bytecode analysis. The resulting behavior model is parametrised over all three performance-influencing factors, which are specified separately. This results in significantly fewer measurements: The behaviour model is reconstructed only once per component service, and one application-independent bytecode benchmark run is sufficient to characterise an execution platform. To predict the execution durations for a concrete platform, our approach combines the behaviour model with platform-specific benchmarking results. We validate our approach by predicting the performance of a file sharing application.",
• keywords = "genetic algorithms, genetic programming, sbse, application independent bytecode benchmark, component based software engineering, genetic search, parametric behaviour model, reverse engineering, runtime bytecode count, static bytecode analysis, object-oriented programming, reverse engineering, search problems, software performance evaluation",
• DOI = "doi:10.1109/TSE.2010.69",
• ISSN = "0098-5589",
• notes = "p868 'overhead of BYCOUNTER ... at most 250 percent' p869 SVM 'do not lend themselves easily to human understanding'. JGAP p870 restricted ranges for ERC known as 'special constants' see \cite{daida:2001:GPEM}. p873 'Through static code analysis, a gene representing the constant XXX extract from bytecode was available to genetic programming'. MARS 1500 generations 'applies genetic programming for each byte code instruction' palladiofileshare p874 in 98percent GP approximations better than MARS approximations. Also known as \cite{5530323}",

}

Genetic Programming entries for Klaus Krogmann Michael Kuperberg Ralf H Reussner

Citations