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This paper analyzes the CASS Software Portfolio to assess the sustainability of scientific open-source software (SciOSS) based on code quality and test coverage metrics. By classifying projects by sustainability, the study reveals that sustainable projects exhibit higher and more consistent test coverage alongside clearer code-test correlations. The analysis also highlights a general trend of low test coverage and reduced testability due to high complexity and coupling in SciOSS.
Sustainable scientific software isn't just about the code; it's about consistent testing and clear links between code quality and tests, a pattern often missing in unsustainable projects.
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.
