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dc.contributor.authorJohanson, Arne
dc.contributor.authorHasselbring, Wilhelm
dc.contributor.editorBecker, Steffen
dc.contributor.editorBogicevic, Ivan
dc.contributor.editorHerzwurm, Georg
dc.contributor.editorWagner, Stefan
dc.date.accessioned2019-03-14T11:49:24Z
dc.date.available2019-03-14T11:49:24Z
dc.date.issued2019
dc.identifier.isbn978-3-88579-686-2
dc.identifier.issn1617-5468
dc.identifier.urihttp://dl.gi.de/handle/20.500.12116/20924
dc.description.abstractDespite the increasing importance of in silico experiments to the scientific discovery process, state-of-the-art software engineering practices are rarely adopted in computational science. To understand the underlying causes for this situation and to identify ways to improve it, we conducted a literature survey on software engineering practices in computational science. We identified recurring key characteristics of scientific software development that are the result of the nature of scientific challenges, the limitations of computers, and the cultural environment of scientific software development. Our findings allow us to point out shortcomings of existing approaches for bridging the gap between software engineering and computational science and to provide an outlook on promising research directions that could contribute to improving the current situation.en
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofSoftware Engineering and Software Management 2019
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-292
dc.subjectComputational Science
dc.subjectModel-driven software engineering
dc.subjectSoftware architecture
dc.titleSoftware Engineering for Computational Scienceen
dc.typeText/Conference Paper
dc.pubPlaceBonn
mci.reference.pages43-44
mci.conference.sessiontitleSession 1: Computational Science
mci.conference.locationStuttgart, Germany
mci.conference.date18.-22. Februar 2019
dc.identifier.doi10.18420/se2019-08


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