Software Engineering for Computational Science: Past, Present, Future. Johanson, A. & Hasselbring, W. 20(2):90–109.
Software Engineering for Computational Science: Past, Present, Future [link]Paper  doi  abstract   bibtex   
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, the authors conducted a literature survey on software engineering practices in computational science. They 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. Their findings allow them 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{johansonSoftwareEngineeringComputational2018,
  title = {Software Engineering for Computational Science: Past, Present, Future},
  shorttitle = {Software {{Engineering}} for {{Computational Science}}},
  author = {Johanson, Arne and Hasselbring, Wilhelm},
  date = {2018-03},
  journaltitle = {Computing in Science Engineering},
  volume = {20},
  pages = {90--109},
  issn = {1521-9615},
  doi = {10.1109/MCSE.2018.021651343},
  url = {https://doi.org/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, the authors conducted a literature survey on software engineering practices in computational science. They 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. Their findings allow them 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.},
  keywords = {~INRMM-MiD:z-TNU9KHS9,antipattern,bias-disembodied-science-vs-computational-scholarship,complexity-vs-uncertainty,computational-science,epistemology,historical-perspective,scientific-software,software-engineering,software-uncertainty,technical-vs-scientific,technology},
  number = {2}
}
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