Multi-Ecosystem Modeling of OSS Project Sustainability

2026-02-19

AI Summary

This study addresses the challenges open-source software (OSS) projects face in navigating policy differences across foundations and aligning sustainability strategies with diverse lifecycle needs. It presents the first quantitative sustainability model spanning three major foundations—Apache, Eclipse, and OSGeo—integrating socio-technical trajectory features through machine learning and a triage-based classification approach to accurately predict project sustainability. The proposed generalizable socio-technical framework supports cross-ecosystem applicability and extends to non-foundation OSS projects, while also incorporating actionable recovery strategies for failing projects. Empirical evaluation demonstrates the model’s effectiveness both within individual foundations and across foundation boundaries, with successful validation on GitHub-hosted projects and real-world recovery cases of previously failed initiatives.

Technology Category

• Machine Learning: Other Foundations of Machine Learning
• Humans and AI: Other Foundations of Human Computation & AI
• Constraint Satisfaction and Optimization: Other Foundations of Constraint Satisfaction

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT
• Responsible Web: Sustainability and climate impact of web technologies

Abstract

Many OSS projects join foundations such as Apache, Eclipse, and OSGeo, to aid their immediate plans and improve long-term prospects by getting governance advice, incubation support, and community-building mechanisms. But foundations differ in their policies, funding models, and support strategies. Moreover, since projects joining these foundations are diverse, coming at different lifecycle stages and having different needs, it can be challenging to decide on the appropriate project-foundation match and on the project-specific plan for sustainability. Here, we present an empirical study and quantitative analysis of the sustainability of incubator projects in the Apache, Eclipse, and OSGeo foundations, and, additionally, of OSS projects from GitHub outside of foundations. We develop foundation-specific sustainability models and a project triage, based on projects' sociotechnical trace profiles, and demonstrate their effectiveness across the foundations. Our results show that our models with triage can effectively forecast sustainability outcomes not only within but across foundations. In addition, the generalizability of the framework allows us to apply the approach to GitHub projects outside the foundations. We complement our findings with actionable recovery strategies from previous work and apply them to case studies of failed incubator projects. Our study highlights the value of sociotechnical frameworks in characterizing and addressing software project sustainability issues.

Problem

• OSS project sustainability
• foundation matching
• sociotechnical trace
• project triage
• open source software

Innovation

• sociotechnical modeling
• OSS sustainability
• foundation-specific models
• project triage
• cross-ecosystem generalization