SE in ES: opportunities for software engineering and cloud computing in environmental science

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SIMM, W.A., SAMREEN, Faiza, BASSETT, R., FERRARIO, M.A., BLAIR, G., WHITTLE, J. and YOUNG, P.J. (2018). SE in ES: opportunities for software engineering and cloud computing in environmental science. In: ICSE-SEIS '18: Proceedings of the 40th International Conference on Software Engineering: Software Engineering in Society. ACM Press, 61-70.

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Official URL: https://dl.acm.org/doi/10.1145/3183428.3183430
Open Access URL: https://dl.acm.org/doi/pdf/10.1145/3183428.3183430 (Published version)
Link to published version:: https://doi.org/10.1145/3183428.3183430

Abstract

New and emergent computing architectures and software engineering practices provide an opportunity for environmental models to be deployed more efficiently and democratically. In this paper we aim to capture the software engineering practices of environmental scientists, highlight opportunities for software engineering and work towards developing a domain specific language for the configuration and deployment of environmental models. We hold a series of interviews with environmental scientists involved in developing and deploying computer based environmental models about the approach taken in engineering models, and describe a case study in deploying an environmental model (WRF: Weather Research Forecasting) on a cloud architecture. From these studies we find a number of opportunities for A) software engineering methods and tools such as Domain Specific Languages to play a role in abstracting from underlying computing complexity, and for B) new architectures to increase efficiency and availability of deployment. Together, we propose they will allow scientists to concentrate on fundamental science rather than specifics of the underlying computing.

Item Type: Book Section
Identification Number: https://doi.org/10.1145/3183428.3183430
Page Range: 61-70
SWORD Depositor: Symplectic Elements
Depositing User: Symplectic Elements
Date Deposited: 27 May 2022 14:02
Last Modified: 27 May 2022 14:02
URI: https://shura.shu.ac.uk/id/eprint/26222

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