Analysis and Modeling of the Open Source Software

Community

Yongqin Gao, Greg MadeyComputer Science & Engineering

University of Notre Dame

Vincent FreehComputer Science Dept.

NCSU

NAACSOS ConferencePittsburgh, PAJune 25, 2003

Supported in part by the National Science Foundation – Digital Science & Technology

Outline

Overview Data collection Network modeling Topological statistical analysis Conclusion

Overview

What is OSS Free to use, distribution Unlimited user and usage Source code available and modifiable

Potential advantages over commercial software Higher quality Faster development Lower cost

Our goal Understanding the OSS phenomenon

Approach SourceForge is the source of our empirical data Modeling as social network Analysis of topological statistics

Data Collection — Monthly Web crawler (scripts)

Python Perl AWK Sed

Monthly Since Jan 2001 ProjectID DeveloperID Almost 2 million records Relational database

PROJ|DEVELOPER8001|dev3488001|dev89728001|dev99228002|dev276508005|dev313518006|dev124098007|dev199358007|dev42628007|dev367118008|dev8972

Modeling as collaboration network

What is collaboration network A social network representing the collaborating

relationships. Movie actor network and scientist collaboration network

Difference of SourceForge collaboration network Detachment Virtual collaboration Voluntary Global

Bipartite property of collaboration network

Collaboration network - bipartite

SourceForge developer network

15850 dev[46]dev[83] 15850 dev[46]

dev[48]

15850 dev[46]dev[56]

15850 dev[46]dev[58]

6882 dev[58]dev[47]

6882 dev[47]dev[79]

6882 dev[47]dev[52]

6882 dev[47]dev[55]

7028 dev[46]dev[99]

7028 dev[46]dev[51]

7028 dev[46]dev[57]

7597 dev[46]dev[45]

7597 dev[46]dev[72]

7597 dev[46]dev[55]

7597 dev[46]dev[58]

7597 dev[46]dev[61]

7597 dev[46]dev[64]7597 dev[46]

dev[67]

7597 dev[46]dev[70]

9859 dev[46]dev[49]9859 dev[46]

dev[53]

9859 dev[46]dev[54]

9859 dev[46]dev[59]

dev[46]

dev[83] dev[56]

dev[48]

dev[52]

dev[79]

dev[72]

dev[51]

dev[57]

dev[55]

dev[99]

dev[47]

dev[58]

dev[53]

dev[58]

dev[65]

dev[45]

dev[70]

dev[67]

dev[59]

dev[54]

dev[49]

dev[64]

dev[61]

Project 6882

Project 9859

Project 7597

Project 7028

Project 15850

OSS Developer Network (Part)Developers are nodes / Projects are links

24 Developers5 Projects

2 hub Developers1 Cluster

Topological analysis

Statistics inspected Diameter Average degree Clustering coefficient Degree distribution Cluster size distribution Relative size of major cluster Fitness and lift cycle

Evolution of these statistics

Diameter of developer network vs. time

The average of shortest paths between any pairs of vertices

The values for developer network (30,000 – 70,000) are between 6 and 8

Diameter of project network vs. time

The values for project network (20,000 – 50,000) are between 6 and 7

Diameter decreasing with time both for developer network and project network

Average degree vs. time

The values for developer network are between 7 and 8

The values for project network are just between 3 and 4

Clustering coefficient of developer network vs. time

Clustering coefficient of project network vs. time

Degree distribution (developers)

Power law in developer distribution.

R2 = 0.9714

Degree distribution (projects)

Power law in project distribution

R2 = 0.9838

Cluster size distribution

Cluster distribution of developer network

R2 with major cluster is 0.7426

R2 without major cluster is 0.9799

Relative size of major cluster vs. time

Stable increase of the relative size of the major cluster

Going to slowly converge to some fixed percentage at around 35%

May be an indication of the network evolution

Existence of fitness

Investigation of development of single project can verify the existence of “young upcomer” phenomenon

We tracked the development of every new project in July 2001 until now (total 1660 projects)

Maximal monthly growth per project is 13 while average monthly growth per project is just 0.3639

Life cycle of project

Summary of results

Power law rules Degree distributions, cluster distribution

Average degree increasing with time Diameter decreasing with time Clustering coefficient decreasing with time Fitness existed in SourceForge Projects have life cycle behaviors

Conclusion

Study of SourceForge collaboration network can help us understanding the OSS community

We investigate not only the topological statistics but also the evolution of these statistics.

Simulation is needed to further investigation of SourceForge collaboration network.

Thank you

Terminology Degree

The count of edges connected to given vertex Degree distribution

The distribution of degrees throughout a network Cluster

The connected components of the network Diameter

Average length of shortest paths between all pairs of vertices Clustering coefficient (CC)

CCi: Fraction representing the number of links actually present relative to the total possible number of links among the vertices in its neighborhood.

CC: average of all CCi in a network