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Measuring What Matters: Software Engineering and its Role in Scientific Software Success
Mid Sweden University, Faculty of Science, Technology and Media, Department of Communication, Quality Management, and Information Systems (2023-).
Mid Sweden University, Faculty of Science, Technology and Media, Department of Communication, Quality Management, and Information Systems (2023-).
2024 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

Scientific software is vital for research across domains and can be reused when it is open-source. To promote this reuse, it is generally beneficial to adopt software engineering best practices to improve accessibility and popularity. However, given the distinct properties of scientific software, there is no consensus on how these practices are being or should be used in scientific software. Since previous evidence on this topic is primarily anecdotal or qualitative, this study used repository mining to quantitatively examine best practices and their relationship with popularity in 90 software engineering artifacts, which are examples of scientific software. The data varied significantly but showed that the studied artifacts generally did not prioritize software engineering best practices, and no significant relationships were found between these aspects and popularity. The results may suggest that scientific software developers prioritize scientific quality over software quality and that traditional software quality measures may not be suitable quality benchmarks in scientific software. However, accessibility issues were identified, highlighting potential societal concerns. Based on these findings, we offer practical advice for quality improvements from a software engineering perspective. Further research is needed to obtain more conclusive and general results.

Place, publisher, year, edition, pages
2024. , p. 23
Keywords [en]
Software Engineering, Scientific Software, Repository Mining, Software Quality, Python
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:miun:diva-51839OAI: oai:DiVA.org:miun-51839DiVA, id: diva2:1880816
Subject / course
Computer Engineering DT1
Educational program
Software Engineering TPVAG 120/180 higher education credits
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Available from: 2024-07-02 Created: 2024-07-02 Last updated: 2024-07-02Bibliographically approved

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fulltext(3493 kB)228 downloads
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Hansson, TobiasThand, Samuel
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf