Analyzing the social ties and structure of contributors in open source software community

Authors:
Mohammad Y. Allaho;Wang-Chien Lee
Affiliations:
-;The Pennsylvania State University, University Park, Pennsylvania
Venue:
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Year:
2013

Citing 6
Cited 0

Group formation in large social networks: membership, growth, and evolution

Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Systematic topology analysis and generation using degree correlations

Proceedings of the 2006 conference on Applications, technologies, architectures, and protocols for computer communications
Measurement and analysis of online social networks

Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Latent social structure in open source projects

Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of software engineering
Determinants of open source software project success: A longitudinal study

Decision Support Systems
Evaluating Longitudinal Success of Open Source Software Projects: A Social Network Perspective

HICSS '09 Proceedings of the 42nd Hawaii International Conference on System Sciences

Quantified Score

Hi-index	0.00

Visualization

Abstract

We conduct a statistical analysis on the social networks of contributors in Open Source Software (OSS) communities using datasets collected from two most fast-growing OSS social interaction sites, Github.com and Ohloh.net. Our goal is to analyze the connectivity structure of the social networks of contributors and to investigate the effect of the different social tie structures on developers' overall productivity to OSS projects. We first analyze the general structure of the social networks, e.g., graph distances and the degree distribution of the social networks. Our analysis confirms that the social networks of OSS communities follow power-law degree distributions and exhibit small-world characteristics. However, the degree mixing pattern shows that high degree nodes tend to connect more with low degree nodes, suggesting collaborations between experts and newbie developers. Second, we study the correlation between graph degrees and the productivity of the contributors in terms of the amount of contribution and commitment to OSS projects. The analysis demonstrates evident influence of the social ties on the developers' overall productivity.