Exploring Sustainability in Scientific Software through Code Quality & Test Coverage Metrics
For developers and maintainers of scientific software, this provides a data-driven approach to assess sustainability, though the findings are incremental and domain-specific.
This study investigates the long-term sustainability of scientific open-source software by analyzing code quality and test coverage metrics. Results show that sustainable projects have higher and more consistent test coverage, while unsustainable projects exhibit weaker patterns, with overall low test coverage in scientific software.
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.