Modeling Dependency-Propagated Ecosystem Impact of Changes in Maintenance Activities: Evaluating Support Strategies in the PyPI Network
Alexandros Tsakpinis, Emil Schwenger, Alexander Pretschner

TL;DR
This paper introduces a dependency-aware model to evaluate how maintenance changes propagate through the PyPI ecosystem, aiding in prioritizing packages for support based on their systemic impact.
Contribution
It presents a novel impact-driven support prioritization framework that considers dependency propagation, improving support strategies in large open source ecosystems.
Findings
Approximately 80% of ecosystem impact is due to 0.1% of packages when prioritized by impact.
Support sets vary significantly in their alignment with modeled ecosystem impact.
Ecosystem impact, social footprint, and operational feasibility are distinct but complementary support dimensions.
Abstract
Background: Open source software ecosystems exhibit dense dependency networks in which maintenance degradation of structurally central packages can propagate widely. Despite increasing attention to open source sustainability, existing support mechanisms lack an explicit, dependencyaware notion of ecosystem-level impact to guide support decisions. Aims: In this paper, we introduce a dependency-aware model of ecosystem impact that captures how changes in maintenance activities propagate through the Python Package Index (PyPI) ecosystem and affect its overall state. Based on this model, we prioritize packages for ecosystem support using our dependency-propagated notion of ecosystem impact. Method: Applying this framework to a snapshot of 718,750 PyPI packages and over 2 million dependencies, we compare our impact-driven support strategy with existing support mechanisms (Tidelift,…
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