29/06/2020

Cheating death: A statistical survival analysis of publicly available python projects

Rao Hamza Ali, Chelsea Parlett-Pelleriti, Erik Linstead

Keywords: open source software projects, survival analysis, software repository health, hazard ratios

Abstract: We apply survival analysis methods to a dataset of publicly-available software projects in order to examine the attributes that might lead to their inactivity over time. We ran a Kaplan-Meier analysis and fit a Cox Proportional-Hazards model to a subset of Software Heritage Graph Dataset, consisting of 3052 popular Python projects hosted on GitLab/GitHub, Debian, and PyPI, over a period of 165 months. We show that projects with repositories on multiple hosting services, a timeline of publishing major releases, and a good network of developers, remain healthy over time and should be worthy of the effort put in by developers and contributors.

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