Cheating Death: A Statistical Survival Analysis of Publicly Available Python']Python Projects

被引：2
|
作者：
Ali, Rao Hamza [1 ]
Parlett-Pelleriti, Chelsea [1 ]
Linstead, Erik [1 ]
机构：
[1] Chapman Univ, Machine Learning & Assist Technol Lab, Orange, CA 92866 USA
来源：
2020 IEEE/ACM 17TH INTERNATIONAL CONFERENCE ON MINING SOFTWARE REPOSITORIES, MSR | 2020年
关键词：
open source software projects; survival analysis; software repository health; hazard ratios;
D O I：
10.1145/3379597.3387511
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
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.
引用
收藏
页码：6 / 10
页数：5
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