1. By Chris Zachor

2.  Introduction  Background  Open Source Software  The SourceForge community and network  Previous Work  What can be done different?  Related Work  Conclusion  Questions

3.  Goal: Use network analysis to better understand the SourceForge community developers  Identify key developers and groups of developers who create popular open source applications

4.  Open Source (OS) Software continues to be a popular alternative to standard commercial software  Many OS alternatives to traditional closed source projects exist  osalt.com provides a convenient database for this

5.  A website to help promote collaboration between developers of OS projects  A repository for OS projects  Developers: revision control, bug tracking, donation system, etc.  Users: bug reporting, recommendations, commenting, etc.

6.  Multiple networks can be formed from the SourceForge community Project-Developer network Developer network Project network Lots of interesting data to be collected from the website such as total downloads of a project, length of developer membership, recommendations, etc.

7.  A bipartite graph with two groups of vertices: projects and developers  An edge indicates the developer works on that project

8.  A collaboration network  Edges are formed where one developer 1 has worked with developer 2

9.  An edge can represent a related project  An edge can represent projects that share a developer  Or perhaps an edge can represent a related project

10.  The open source group at Notre Dame  Used network analysis as a tool to understand the Open Source Software phenomenon and predict growth over time  Monthly data dumps directly from SourceForge.net

11.  The latest paper produced concerning network analysis was in 2007  The project count has more than doubled in size to ~250,000 projects (from ~90,000 in 2007).

12.  Their main concern was with how the network was evolving  Focus was on the change in measures from month to month  No interpretation of data

13.  M. E. J. Newman  Scientific Collaboration Networks  4 Major Databases spanning 5 years  Collaboration network using authors who have worked together on a single paper  Explored what fields were producing more papers, what fields collaborated more, etc.

14.  Obermeier et al.  University College Dublin  Co-authorship between departments at UCD  They wanted to understand the interdisciplinary publication culture within the University  Looked at brokerage individuals and how they play a part in their own departments  Found these brokerage individuals to be most central within their own departments

15.  Gao and Madey  Network analysis of SourceForge  Used as a tool to understand the open source movement  Documented the growth of the SourceForge community  Structural analysis, centrality analysis, path analysis  They did not interpret the data

16.  Xu, Christley, and Mady  Network analysis of the SourceForge community  Attempt to explain the success and efficiency of OS development practices  Noted that the SourceForge Network is a scale free network  Also noted the presence of the small world phenomenon within the community

17.  Xu, Christy, and Madey continued  Observed that co-developers and active users were a major factor in large scale projects  Meanwhile, project leads and core developers were largely involved in small projects

18.  While previous studies were focused on growth and why the process is a success, this study will focus on how key developers and groups play a part in creating popular software  Many attributes not looked at in previous studies

19. Anyone?