Research Papers

Analysis of the Structure and Evolution of an Open-Source Community

[+] Author and Article Information
Hao-Yun Huang, Qize Le

School of Mechanical and Materials Engineering,  Washington State University, Pullman, WA 99164panchal@wsu.edu

Jitesh H. Panchal1

School of Mechanical and Materials Engineering,  Washington State University, Pullman, WA 99164panchal@wsu.edu


Corresponding author.

J. Comput. Inf. Sci. Eng 11(3), 031008 (Sep 02, 2011) (14 pages) doi:10.1115/1.3615677 History: Received June 01, 2010; Revised March 29, 2011; Published September 02, 2011; Online September 02, 2011

Open-source processes are based on the paradigm of self-organized communities as opposed to the traditional hierarchical teams. These processes have not only been successful in the software development domain but are also increasingly being used in the development of physical products. In order to successfully adapt open-source processes to product realization, there is a need to understand how open-source communities self-organize and how this impacts the development of products. Toward the direction of fulfilling this need, we present an analysis of an existing open-source community involved in developing a web-based content-management platform, Drupal. The approach is based on the analysis of networks using techniques such as social network analysis, degree distribution, and hierarchical clustering. Openly available information on the Drupal website is utilized to perform the analysis of the community. The data are transformed into two weighted undirected networks: networks of people and networks of Drupal modules. Both the structures of these networks and their evolution during the past 6 years are studied. Based on the analysis, it is observed that the structure of the Drupal community has the characteristics of a scale-free network, which is similar to many other complex networks in diverse domains. Key trends in the evolution of the networks are identified. Finally, a predictive model is presented to provide potential explanations for the observed structures and evolutionary trends.

Copyright © 2011 by American Society of Mechanical Engineers
Topics: Networks
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Figure 1

Illustration of the data-gathering step

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Figure 2

Example of a bipartite network and the two derived networks

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Figure 3

Degree distribution of two types of nodes in the bipartite network

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Figure 4

Degree distribution of projects and people networks

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Figure 5

Adjacency matrix plots of (a) people network and (b) projects network before and after clustering

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Figure 6

Number of people and modules at different times

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Figure 7

Average degree, degree centrality, average density, clustering coefficients, and connectedness of project and people networks with respect to time

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Figure 8

Evolution of the power-law coefficients for degrees of people in the bipartite network

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Figure 9

Evolution of people network

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Figure 10

Evolution of project network

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Figure 11

Degree distribution of the nodes obtained from the model, compared with the degree distribution from Drupal

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Figure 12

Joint degree distributions of Drupal and simulated networks

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Figure 13

Degree distribution of people in Drupal and simulated networks



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