A Study of FOSS'2013 Survey Data Using Clustering Techniques
Mani A, Rebeka Mukherjee
TL;DR
This paper analyzes the FOSS 2013 survey data using clustering and statistical methods to uncover hidden trends, especially among women contributors, providing insights into open source software development practices.
Contribution
It introduces a novel analysis of FOSS survey data focusing on women contributors using clustering and correspondence analysis techniques.
Findings
Identifies gender-based participation patterns
Reveals hidden trends in development practices
Provides insights into open source community dynamics
Abstract
FOSS is an acronym for Free and Open Source Software. The FOSS 2013 survey primarily targets FOSS contributors and relevant anonymized dataset is publicly available under CC by SA license. In this study, the dataset is analyzed from a critical perspective using statistical and clustering techniques (especially multiple correspondence analysis) with a strong focus on women contributors towards discovering hidden trends and facts. Important inferences are drawn about development practices and other facets of the free software and OSS worlds.
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Taxonomy
TopicsSensory Analysis and Statistical Methods · Food Chemistry and Fat Analysis