Software engineering for computational science
Software engineering for computational science
Despite 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.
Computational Science, Model-driven software engineering, Software architecture
43-44
Gesellschaft für Informatik
Johanson, Arne
06f6d12f-d816-4361-ab22-24a32021c595
Hasselbring, Wilhelm
ee89c5c9-a900-40b1-82c1-552268cd01bd
2019
Johanson, Arne
06f6d12f-d816-4361-ab22-24a32021c595
Hasselbring, Wilhelm
ee89c5c9-a900-40b1-82c1-552268cd01bd
Johanson, Arne and Hasselbring, Wilhelm
(2019)
Software engineering for computational science.
Becker, Steffen, Bogicevic, Ivan, Herzwurm, Georg and Wagner, Stefan
(eds.)
In Software Engineering and Software Management 2019.
vol. P-292,
Gesellschaft für Informatik.
.
(doi:10.18420/se2019-08).
Record type:
Conference or Workshop Item
(Paper)
Abstract
Despite 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.
Text
08
- Version of Record
More information
Published date: 2019
Venue - Dates:
2019 Software Engineering and Software Management, SE/SWM 2019, , Stuttgart, Germany, 2019-02-18 - 2019-02-22
Keywords:
Computational Science, Model-driven software engineering, Software architecture
Identifiers
Local EPrints ID: 488742
URI: http://eprints.soton.ac.uk/id/eprint/488742
ISSN: 1617-5468
PURE UUID: e3f7a117-41ef-47b8-be6b-a55e2d31b5ab
Catalogue record
Date deposited: 05 Apr 2024 16:35
Last modified: 10 Apr 2024 02:15
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Contributors
Author:
Arne Johanson
Author:
Wilhelm Hasselbring
Editor:
Steffen Becker
Editor:
Ivan Bogicevic
Editor:
Georg Herzwurm
Editor:
Stefan Wagner
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