Exploring Sustainability in Scientific Software through Code Quality & Test Coverage Metrics
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866913088991657984 |
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| author | Rahman, Sheikh Md. Mushfiqur Watson, Gregory R. Eisty, Nasir U. |
| author_facet | Rahman, Sheikh Md. Mushfiqur Watson, Gregory R. Eisty, Nasir U. |
| contents | Context: Scientific open-source software (SciOSS) plays a foundational role in research and engineering, yet its long-term sustainability has often been overlooked and remains a significant concern. Objective: This study investigates the long-term sustainability of SciOSS through code and test quality metrics. Method: We analyze CASS Software Portfolio projects, classifying them by sustainability and comparing their code structure, test coverage, and links between code quality and testing across the dataset. Results: Sustainable projects show higher, more consistent test coverage and clearer code-test correlations, while unsustainable ones show weaker patterns. Overall, test coverage is low in scientific software, and high complexity and coupling reduce testability. Conclusion: In this study, we present a practical, data-driven approach for assessing sustainability in scientific software, offering a foundation for evaluating long-term software health and supporting future efforts in quality assurance and sustainability monitoring. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_03243 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Exploring Sustainability in Scientific Software through Code Quality & Test Coverage Metrics Rahman, Sheikh Md. Mushfiqur Watson, Gregory R. Eisty, Nasir U. Software Engineering Context: Scientific open-source software (SciOSS) plays a foundational role in research and engineering, yet its long-term sustainability has often been overlooked and remains a significant concern. Objective: This study investigates the long-term sustainability of SciOSS through code and test quality metrics. Method: We analyze CASS Software Portfolio projects, classifying them by sustainability and comparing their code structure, test coverage, and links between code quality and testing across the dataset. Results: Sustainable projects show higher, more consistent test coverage and clearer code-test correlations, while unsustainable ones show weaker patterns. Overall, test coverage is low in scientific software, and high complexity and coupling reduce testability. Conclusion: In this study, we present a practical, data-driven approach for assessing sustainability in scientific software, offering a foundation for evaluating long-term software health and supporting future efforts in quality assurance and sustainability monitoring. |
| title | Exploring Sustainability in Scientific Software through Code Quality & Test Coverage Metrics |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2605.03243 |