By Chris Zachor

 Introduction  Background  Changes  Methodology  Data Collection  Network Topologies  Measures  Tools  Conclusion  Questions

 Use network analysis to better understand the SourceForge and Github community developers  Identify key differences (if any) within the two communities  Examine the diversity of collaborations within these two communities

 The addition of Github to the study  Contains some of the same attributes to allow for a comparison  Other communities were looked at, but they either were not large enough or did not provide enough public data.

 Crawling the websites using a simple Perl script and regular expressions  Collect a project list from Sourceforge   No specified request limit  Check for duplicates

 Using the Github API provides our data  Limited to 60 API calls per minute  Use multiple computers to collect all 1.5 million projects

 Degree  Clustering Coeficient  Modularity  Power Law  Small World Phenomenon

 Average number of projects worked on by a developer  Average number of collaborations  Average number of developers on a project

 Examine how likely developers are to stick together in groups  Examine both average clustering coefficient for the entire network and the local clustering coefficient for nodes of interest

 Provide us with a measure of how diverse developer collaborations are.  Range -1 < Q < 1  Ranges closer to one show less diversity in collaboration choices  Ranges closer to negative one show more diversity in collaboration choices

 Previous studies have found that the Sourceforge community does follow the power law  No such study has been done on the Github community  Fewer developers should be apart of many project while many developers should be involved with only one project

 Previous studies have shown the Sourceforge community does exhibit small world properties  Once again, no study has been done on the Github community  Using Pajek, I will create a random network of the same nodes and edges  Then, compare the clustering coefficient and the average shortest path

 Perl  Pajek  cURL  wget  GUESS

 Through the use of network analysis, we hope to gain a better understanding of the developers of Sourceforge and Github communities.

Suggestions? Comments?