Architecting complex, long-lived scientific software

dc.contributor.authorErnst, Neil A.
dc.contributor.authorKlein, John
dc.contributor.authorBartolini, Marco
dc.contributor.authorColes, Jeremy
dc.contributor.authorRees, Nick
dc.date.accessioned2023-11-30T17:39:26Z
dc.date.available2023-11-30T17:39:26Z
dc.date.copyright2023en_US
dc.date.issued2023
dc.descriptionThanks also to all the participants in the workshops for their time and energy, and to Mary Popeck for her help in the second workshop. NR would also like to thank Peter Braam of Braam Research LLC for suggesting the SEI approach to him in the first place. References herein to any specific commercial product, process, or service by trade name, trade mark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by Carnegie Mellon University or its Software Engineering Institute. NO WARRANTY. This Carnegie Mellon University and Software Engineering Institute material is furnished on an "as-is" basis. Carnegie Mellon University makes no warranties of any kind, either expressed or implied, as to any matter including, but not limited to, warranty of fitness for purpose or merchantability, exclusivity, or results obtained from use of the material. Carnegie Mellon University does not make any warranty of any kind with respect to freedom from from patent, trademark, or copyright infringement. Architecture Tradeoff Analysis Method® is registered in the U.S. Patent and Trademark Office by Carnegie Mellon University DM22-0316.en_US
dc.description.abstractSoftware is a critical aspect of large-scale science, providing essential capabilities for making scientific discoveries. Large-scale scientific projects are vast in scope, with lifespans measured in decades and costs exceeding hundreds of millions of dollars. Successfully designing software that can exist for that span of time, at that scale, is challenging for even the most capable software companies. Yet scientific endeavors face challenges with funding, staffing, and operate in complex, poorly understood software settings. In this paper we discuss the practice of early-phase software architecture in the Square Kilometre Array Observatory’s Science Data Processor. The Science Data Processor is a critical software component in this next-generation radio astronomy instrument. We customized an existing set of processes for software architecture analysis and design to this project’s unique circumstances. We report on the series of comprehensive software architecture plans that were the result. The plans were used to obtain construction approval in a critical design review with outside stakeholders. We conclude with implications for other long-lived software architectures in the scientific domain, including potential risks and mitigations.en_US
dc.description.reviewstatusRevieweden_US
dc.description.scholarlevelFacultyen_US
dc.description.sponsorshipThe authors thank the University of Cambridge and STFC for funding the SEI involvement in the SDP and the principals of the SDP design consortia—Paul Alexander in particular—for their continued support.en_US
dc.identifier.citationErnst, N. A., Klein, J., Bartolini, M., Coles, J., & Rees, N. (2023). Architecting complex, long-lived scientific software. Journal of Systems and Software, 204, 111732. https://doi.org/10.1016/j.jss.2023.111732en_US
dc.identifier.urihttps://doi.org/10.1016/j.jss.2023.111732
dc.identifier.urihttp://hdl.handle.net/1828/15661
dc.language.isoenen_US
dc.publisherJournal of Systems and Softwareen_US
dc.subjectSoftware architectureen_US
dc.subjectSoftware processen_US
dc.subjectSoftware engineering in practiceen_US
dc.subjectRadio astronomyen_US
dc.subjectScientific softwareen_US
dc.titleArchitecting complex, long-lived scientific softwareen_US
dc.typeArticleen_US

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