Uncovering Scientific Software Sustainability through Community Engagement and Software Quality Metrics

TL;DR

This paper investigates the sustainability of scientific open-source software on GitHub by analyzing community engagement and software quality metrics, introducing a novel visualization technique and revealing domain-specific sustainability patterns.

Contribution

It introduces a new visualization method for software metrics and provides insights into factors influencing long-term sustainability of Sci-OSS projects.

Findings

Community engagement correlates with software sustainability.

Domain-specific differences affect project longevity.

Natural language feedback impacts software quality.

Abstract

Scientific open-source software (Sci-OSS) projects are critical for advancing research, yet sustaining these projects long-term remains a major challenge. This paper explores the sustainability of Sci-OSS hosted on GitHub, focusing on two factors drawn from stewardship organizations: community engagement and software quality. We map sustainability to repository metrics from the literature and mined data from ten prominent Sci-OSS projects. A multimodal analysis of these projects led us to a novel visualization technique, providing a robust way to display both current and evolving software metrics over time, replacing multiple traditional visualizations with one. Additionally, our statistical analysis shows that even similar-domain projects sustain themselves differently. Natural language analysis supports claims from the literature, highlighting that project-specific feedback plays a key role in maintaining software quality. Our visualization and analysis methods offer researchers, funders, and developers key insights into long-term software sustainability.