Software Engineering for Computational Science: Past, Present, Future. Johanson, A. & Hasselbring, W. Computing in Science & Engineering, 2018. doi abstract bibtex While the importance of in silico experiments for the scientific discovery process increases, 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 for improving the current situation, we conduct a literature survey on software engineering practices in computational science. As a result of our survey, we identified 13 recurring key characteristics of scientific software development that can be divided into three groups: characteristics that results (1) from the nature of scientific challenges, (2) from limitations of computers, and (3) from 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.
@article{johansonSoftwareEngineeringComputational2018a,
title = {Software Engineering for Computational Science: Past, Present, Future},
author = {Johanson, Arne and Hasselbring, Wilhelm},
year = {2018},
pages = {1},
issn = {1521-9615},
doi = {10.1109/mcse.2018.108162940},
abstract = {While the importance of in silico experiments for the scientific discovery process increases, 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 for improving the current situation, we conduct a literature survey on software engineering practices in computational science. As a result of our survey, we identified 13 recurring key characteristics of scientific software development that can be divided into three groups: characteristics that results (1) from the nature of scientific challenges, (2) from limitations of computers, and (3) from 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.},
journal = {Computing in Science \& Engineering},
keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-14530926,~to-add-doi-URL,computational-science,disciplinary-barrier,knowledge-integration,reproducibility,reproducible-research,scientific-communication,software-engineering,software-uncertainty,uncertainty},
lccn = {INRMM-MiD:c-14530926}
}
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