Exploring Sustainability in Scientific Software through Code Quality & Test Coverage Metrics

2026-05-04

AI Summary

This study addresses the long-standing lack of effective evaluation of sustainability in scientific open-source software. It proposes a data-driven approach to systematically uncover, for the first time, the relationships among code quality, test coverage, and software sustainability. By analyzing code structure, measuring test coverage, and modeling code–test correlations, the authors classify and compare projects within the CASS software portfolio. The findings reveal that sustainable projects consistently exhibit higher and more stable test coverage, along with clearer mappings between code and tests. In contrast, scientific software as a whole demonstrates generally low test coverage, and high code complexity coupled with strong coupling significantly impairs its testability.

Abstract

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.

Problem

• scientific software
• sustainability
• code quality
• test coverage
• open-source

Innovation

• scientific software sustainability
• code quality metrics
• test coverage
• data-driven assessment
• software health