Predicting Abandonment of Open Source Software Projects with An Integrated Feature Framework

TL;DR

This paper presents a scalable, interpretable framework for predicting OSS project abandonment using a large dataset, multi-perspective features, and survival analysis, improving prediction accuracy and practical applicability.

Contribution

It introduces an integrated feature framework and a precise labeling pipeline, enabling high-performance, interpretable abandonment prediction for large OSS datasets.

Findings

Survival analysis achieves a C-index of 0.846, outperforming surface feature models.

The framework effectively identifies at-risk OSS projects with high accuracy.

Practical deployment demonstrates utility in openEuler package risk assessment.

Abstract

Open Source Software (OSS) is a cornerstone of contemporary software development, yet the increasing prevalence of OSS project abandonment threatens global software supply chains. Although previous research has explored abandonment prediction methods, these methods often demonstrate unsatisfactory predictive performance, further plagued by imprecise abandonment discrimination, limited interpretability, and a lack of large, generalizable datasets. In this work, we address these challenges by reliably detecting OSS project abandonment through a dual approach: explicit archival status and rigorous semantic analysis of project documentation or description. Leveraging a precise and scalable labeling pipeline, we curate a comprehensive longitudinal dataset of 115,466 GitHub repositories, encompassing 57,733 confirmed abandonment repositories, enriched with detailed, timeline-based behavioral features. Building on this foundation, we introduce an integrated, multi-perspective feature framework for abandonment prediction, capturing user-centric, maintainer-centric, and project evolution features. Survival analysis using an AFT model yields a high C-index of 0.846, substantially outperforming models confined to surface features. Further, feature ablation and SHAP analyses confirm both the predictive power and interpretability of our approach. We further demonstrate practical deployment of a GBSA classifier for package risk in openEuler. By unifying precise labeling, multi-perspective features, and interpretable modeling, our work provides reproducible, scalable, and practitioner-oriented support for understanding and managing abandonment risk in large OSS ecosystems. Our tool not only predicts abandonment but also enhances program comprehension by providing actionable insights into the health and sustainability of OSS projects.