topology and evolution of the open source software community advisors: dr. vincent w. freeh dr....
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![Page 1: Topology and Evolution of the Open Source Software Community Advisors: Dr. Vincent W. Freeh Dr. Kevin Bowyer Supported in part by the National Science](https://reader035.vdocument.in/reader035/viewer/2022062517/56649ef45503460f94c07830/html5/thumbnails/1.jpg)
Topology and Evolution of the Open Source Software Community
Advisors:
Dr. Vincent W. FreehDr. Kevin Bowyer
Supported in part by the National Science Foundation – Digital Science & Technology
Yongqin Gao
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Outline
Overview• Data collection
• Network modeling
• Topological statistical analysis (real data)
• Simulations
• Publications
• Conclusions
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Overview (about OSS)
• What is OSS– Free to use, free to distribute – Unlimited user and usage – Source code available and modifiable
• Potential advantages over commercial software– Higher quality– Faster development– Lower cost– Transparent
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Overview (about our research)
• Our goal– Understanding the OSS phenomenon
• Approach– SourceForge is the source of our empirical data– Modeling as a social network– Analysis of topological statistics– Use simulation to verify and validate the model
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Outline
• OverviewData collection
• Network modeling
• Topological statistical analysis
• Simulations
• Publications
• Conclusions
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Data Collection — Monthly
• Web crawler (scripts)– Python– Shell– 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
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Outline
• Overview
• Data collectionNetwork modeling
• Topological statistical analysis (real data)
• Simulations
• Publications
• Conclusions
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Modeling as Collaboration Network
• What is a collaboration network?– A social network representing the collaborating
relationships.– Movie actor network and scientist collaboration
network
• Difference of SourceForge collaboration network– Link detachment– Virtual collaboration– Voluntary– Global
• Bipartite property of collaboration networks
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Collaboration network - bipartite
Adapted from Newman, Strogatz and Watts, 2001
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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[80]
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
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Outline
• Overview
• Data collection
• Network modelingTopological statistical analysis (real data)
• Simulations
• Publications
• Conclusion
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Topological Analysis
• Statistics inspected– Diameter– Average degree– Clustering coefficient– Degree distribution– Cluster size distribution– Relative size of major cluster– Fitness and life cycle
• Evolution of these statistics• Dual networks
– developer network and project network
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Terminology
• Diameter– Average length of shortest paths between all pairs of vertices
• Degree– The count of edges connected to given vertex
• Average degree– Average of the degrees of all vertices in the network
• Cluster– The connected components of the network
• Clustering coefficient (CC)– CCi: Fraction representing the number of links actually present relative t
o the total possible number of links among the vertices in its neighborhood.
– CC: average of all CCi in a network• Degree distribution
– The distribution of degrees throughout a network• Major cluster
– The largest cluster in the network
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Diameter of Developer Network vs. Time
• Network size increased from 30,000 to 70,000
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Diameter of Project Network vs. Time
• Network size increased from 20,000 to 50,000.
• Diameter decreasing with time both for developer network and project network
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Clustering Coefficient of Developer Network vs. Time
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Clustering Coefficient of Project Network vs. Time
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Degree Distribution (developers)
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Degree Distribution (projects)
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Cluster Size Distribution
• R2 with major cluster is 0.7426
• R2 without major cluster is 0.9799
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Relative Size of Major Cluster vs. Time
• Increase of the relative size of the major cluster
• Increasing rate is decreasing
• May be an indication of the network evolution
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Existence of Fitness
• Investigation of development of single project can verify the existence of “newcomer” 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
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Life Cycle of Project
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Summary
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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
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Outline
• Overview
• Data collection
• Network modeling
• Topological statistical analysis (real data)Simulations
• Publications
• Conclusion
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Conceptual Framework
Empirical data
Adjustment
Generation
Verification
Validation
Cha
ract
eriz
atio
nD
escr
iptio
n
Model
Simulation
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Agent-based Modeling
• EBM vs. ABM– Heterogeneous individuals– Complex network
• Experience environment– Hardware: computer cluster– Software:
• Simulation toolkits: Swarm• Database: Oracle• Language: Java, PL/SQL
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Model for SourceForge
• ABM based on bipartite graph• Model description
– Agent: developer– Behaviors: Create, join, abandon and idle– Preference: developer’s and project’s– Fitness
• Four models in iterations– ER, BA, BA with constant fitness and BA with dynamic
fitness
• Comparison of empirical and simulated data
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ER Model - Diameter
• Average degree is decreasing while it is increasing in empirical data
• Diameter is increasing while it is decreasing in empirical data
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ER Model – Clustering Coefficient
• Clustering coefficient is relatively low under 0.3 while it is around 0.7 in empirical data.
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ER Model – Degree Distribution
• Degree distribution is normal distribution while it is power law in empirical data
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ER Model – Cluster Size Distribution
• power law distribution with R2 as 0.6667 (0.9653 without the major cluster) while R2 in empirical data is 0.7426 (0.9799 without the major cluster)
• The actual distribution is different from empirical data
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BA Model – Diameter and Clustering Coefficient
• Small diameter and high clustering coefficient like empirical data
• Diameter and clustering coefficient are both decreasing like empirical data
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BA Model – Degree Distribution
• Power laws in degree distributions, similar to empirical data (o for simulated data and x for empirical data).
• For developer distribution: simulated data has R2 as 0.9798 and empirical data has R2 as 0.9714.
• For project distribution: simulated data has R2 as 0.6650 and empirical data has R2 as 0.9838.
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BA Model with Constant Fitness
• Power laws in degree distributions, similar to empirical data (o for simulated data and x for empirical data).
• For developer distribution: simulated data has R2 as 0.9742 and empirical data has R2 as 0.9714.
• For project distribution: simulated data has R2 as 0.7253 and empirical data has R2 as 0.9838.
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BA Model with Dynamic Fitness
• Power laws in degree distribution, similar to empirical data (o for simulated data and x for empirical data).
• For developer distribution: simulated data has R2 as 0.9695 and empirical data has R2 as 0.9714.
• For project distribution: simulated data has R2 as 0.8051 and empirical data has R2 as 0.9838.
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Advantage of Dynamic Fitness
• Intuition: Fitness should decreasing with time.
• Statistics: project has life cycle behavior which can not be replicated by BA model with constant fitness but can be replicated by BA model with dynamic fitness
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Summary
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Summary of Results
• We use ABM to model and simulate the SourceForge collaboration network.
• Conceptual framework is proposed for agent-based modeling and simulation.
• Case study of this framework: SourceForge study through ER, BA, BA with constant fitness and BA with dynamic fitness.
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Outline
• Overview
• Data collection
• Network modeling
• Topological statistical analysis (real data)
• SimulationsPublications
• Conclusion
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Publications To-date
• Yongqin Gao, "Modeling and Simulation of the OSS Community", Seventh Annual Swarm Researchers Meeting (Swarm2003), Notre Dame, IN, 2003.
• Yongqin Gao, Vince Freeh, and Greg Madey, "Analysis and Modeling of the Open Source Software Community", NAACSOS Conference 2003, Pittsburgh.
• Yongqin Gao, Vince Freeh, and Greg Madey, "Conceptual Framework for Agent-based Modeling and Simulation", NAACSOS Conference 2003, Pittsburgh.
• Greg Madey, Vincent Freeh, Renee Tynan, Yongqin Gao, Chris Hoffman, "Agent-based Modeling and Simulation of Collaborative Social Networks", AMCIS 2003, Tampa, FL.
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Possible Journals
• Chapter 3– Physica A: statistical mechanics and its applicatio
ns– Journal of Social Structure (JSS)
• Chapter 4– Journal of Artificial Societies and Social Simulatio
n (JASSS)– Journal of Statistical Computation and Simulation
(JSCS)
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Outline
• Overview
• Data collection
• Network modeling
• Topological statistical analysis (real data)
• Simulations
• PublicationsConclusion
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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 used to investigate of SourceForge collaboration network.
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Contribution
• Statistical study of the SourceForge community (snapshot and evolution)
• Verification of the approximate method to calculate the diameter and CC
• Proposal of a model for the SourceForge community
• Improvement of dynamic fitness to BA model
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Future Work
• Data collection– Database dump from SourceForge (PostgreSQL 8GB)– All the possible attributes– Database schema in UML
• More topology analysis (with more attributes)– Discussion forum– Task assignment– Project management– Active testing
• Behavior-based analysis– Interaction between agents– H. Beyton Young’s model
• Information entropy analysis
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Acknowledgements
• Committee
• Advisors
• Colleagues
• SourceForge
• NSF
• Others
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Thank you