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Scaling size and parameter spaces in variability-aware software performance models

dc.contributor.authorKowal, Matthias
dc.contributor.authorTschaikowski, Max
dc.contributor.authorTribastone, Mirco
dc.contributor.authorSchaefer, Ina
dc.contributor.editorKnoop, Jens
dc.contributor.editorZdun, Uwe
dc.date.accessioned2017-06-21T07:37:31Z
dc.date.available2017-06-21T07:37:31Z
dc.date.issued2016
dc.description.abstractModel-based software performance engineering often requires the analysis of many instances of a model to find optimizations or to do capacity planning. These performance predictions get increasingly more difficult with larger models due to state space explosion as well as large parameter spaces since each configuration has its own performance model and must be analyzed in isolation (product-based (PB) analysis). We propose an efficient family-based (FB) analysis using UML activity diagrams with performance annotations. The FB analysis enables us to analyze all configurations at once using symbolic computation. Previous work has already shown that a FB analysis is significant faster than its PB counterpart. This work is an extension of our previous research lifting several limitations.en
dc.identifier.isbn978-3-88579-646-6
dc.identifier.pissn1617-5468
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofSoftware Engineering 2016
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-252
dc.titleScaling size and parameter spaces in variability-aware software performance modelsen
dc.typeText/Conference Paper
gi.citation.endPage34
gi.citation.publisherPlaceBonn
gi.citation.startPage33
gi.conference.date23.-26. Februar 2016
gi.conference.locationWien

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