Predicting Maintenance Cessation of Open Source Software Repositories with An Integrated Feature Framework

• URL: http://arxiv.org/abs/2507.21678v1
• Date: Tue, 29 Jul 2025 10:45:24 GMT
• Title: Predicting Maintenance Cessation of Open Source Software Repositories with An Integrated Feature Framework
• Authors: Yiming Xu, Runzhi He, Hengzhi Ye, Minghui Zhou, Huaimin Wang,
• Abstract summary: Maintenance risks of open source software (OSS) projects pose significant threats to the quality, security, and resilience of modern software supply chains.<n>We introduce maintenance cessation'', grounded in explicit archival status and rigorous semantic analysis of project documentation.<n>We propose an integrated, multi-perspective feature framework for predicting maintenance cessation, systematically combining user-centric features, maintainer-centric features and project evolution features.
• Score: 14.346295005927347
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The maintenance risks of open source software (OSS) projects pose significant threats to the quality, security, and resilience of modern software supply chains. While prior research has proposed diverse approaches for predicting OSS maintenance risk -- leveraging signals ranging from surface features (e.g., stars, commits) to social network analyses and behavioral patterns -- existing methods often suffer from ambiguous operational definitions, limited interpretability, and datasets of insufficient scale or generalizability. In this work, we introduce ``maintenance cessation'', grounded in both explicit archival status and rigorous semantic analysis of project documentation. Building on this foundation, we curate a large-scale, longitudinal dataset of 115,466 GitHub repositories -- encompassing 57,733 confirmed cessation events -- complemented by comprehensive, timeline-based behavioral features. We propose an integrated, multi-perspective feature framework for predicting maintenance cessation, systematically combining user-centric features, maintainer-centric features and project evolution features. AFT survival analysis demonstrates a high C-index (0.846), substantially outperforming models relying only on surface features. Feature ablation and SHAP analysis further confirm the effectiveness and interpretability of our approach. Finally, we demonstrate real-world applicability by deploying a GBSA classifier in the openEuler ecosystem for proactive package risk screening. Our work establishes a scalable, interpretable foundation for maintenance-risk prediction, enabling reproducible risk management across large-scale open source ecosystems.

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