Assessing Open Source Software Survivability using Kaplan-Meier Survival Function and Polynomial Regression

This study evaluates OSS project survivability using the Kaplan-Meier Survival Function and polynomial regression models. The key factors identified include the number of contributors and project popularity, which significantly influence survivability. Traditional indicators like project age do not directly correlate with OSS survivability. Instead, community engagement and recognition are crucial, offering valuable guidelines for managing and selecting Survivable OSS projects.

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Publication Details

Published
2024-10-18
DOI
https://doi.org/10.1145/3691620.3695333
Primary Topic
Software Reliability and Analysis Research
Type
article
Field-Weighted Citation Impact
0.00

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article

Assessing Open Source Software Survivability using Kaplan-Meier Survival Function and Polynomial Regression

Ryeonggu Kwon, Gihwon Kwon, Sohee Park
Software Reliability and Analysis Research
article

Assessing Open Source Software Survivability using Kaplan-Meier Survival Function and Polynomial Regression

Ryeonggu Kwon, Gihwon Kwon, Sohee Park
article en

Abstract

This study evaluates OSS project survivability using the Kaplan-Meier Survival Function and polynomial regression models. The key factors identified include the number of contributors and project popularity, which significantly influence survivability. Traditional indicators like project age do not directly correlate with OSS survivability. Instead, community engagement and recognition are crucial, offering valuable guidelines for managing and selecting Survivable OSS projects.

Kyonggi University (KR)
Ministry of Science and ICT, South Korea, Institute for Information and Communications Technology Promotion
Zero hunger
Openalex Percentile: Top 81%
Software Reliability and Analysis Research
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