NETWORK ANALYSIS OF THE SOURCEFORGE

COMMUNITYBy Chris Zachor

Overview

Introduction Background

Open Source Software The SourceForge community and network

Previous Work What can be done different? Related Work Conclusion Questions

Introduction

Goal: Use network analysis to better understand the SourceForge community developers

Identify key developers and groups of developers who create popular open source applications

Open Source Software

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

The SourceForge Community

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.

The SourceForge Network

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.

Project-Developer Network

A bipartite graph with two groups of vertices: projects and developers

An edge indicates the developer works on that project

Developer Network

A collaboration network Edges are formed where one developer 1

has worked with developer 2

Project Network

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

Previous Work on SourceForge

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

What Can Be Done Different?

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).

What Can Be Done Different?

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

Related Work

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.

Related Work

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

Related Work

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

Related Work

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

Related Work

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

Conclusion

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

Questions?

Anyone?