Software engineering for computational science: past, present, future
Software engineering for computational science: past, present, future
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 13 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.
90-109
Johanson, Arne N.
06f6d12f-d816-4361-ab22-24a32021c595
Hasselbring, Wilhelm
ee89c5c9-a900-40b1-82c1-552268cd01bd
15 March 2018
Johanson, Arne N.
06f6d12f-d816-4361-ab22-24a32021c595
Hasselbring, Wilhelm
ee89c5c9-a900-40b1-82c1-552268cd01bd
Johanson, Arne N. and Hasselbring, Wilhelm
(2018)
Software engineering for computational science: past, present, future.
Computing in Science and Engineering, 20 (2), .
(doi:10.1109/MCSE.2018.021651343).
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 13 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.
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Published date: 15 March 2018
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© 2018 IEEE.
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Local EPrints ID: 488709
URI: http://eprints.soton.ac.uk/id/eprint/488709
ISSN: 1521-9615
PURE UUID: a34ad90e-5ef2-4861-93e7-801b716af7d7
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Date deposited: 04 Apr 2024 16:47
Last modified: 13 Jul 2024 02:12
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Author:
Arne N. Johanson
Author:
Wilhelm Hasselbring
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