An Analysis of Open Source Software Development Using Social Network Theory and Agent-Based Modeling
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1 An Analysis of Open Source Software Development Using Social Network Theory and Agent-Based Modeling Greg Madey Vincent Freeh Renee Tynan Chris Hoffman University of Notre Dame March 2003 The Second Lake Arrowhead Conference on HUMAN COMPLEX SYSTEMS Hosted by the UCLA Center for Computational Social Sciences
2 Collaborators Greg Madey* Yongqin Gao Nadir Kiyanclar Carlos Siu Computer Science University of Notre Dame Vincent Freeh* Computer Science North Carolina State University Renee Tynan* Chris Hoffman Department of Management University of Notre Dame *co-pis Operation Research, Computer Science, Social Psychology
3 Goals and Objectives Understanding Open Source Software Development NSF Grant, CISE/IIS/Digital Society Technology Conceptual Explanatory Model of OSS Phenomenon Agent-Based Simulation of OSS Large Scale Data Mining: SourceForge and other Archives Social Network Analysis with Dynamic Attachment Understanding Social & Task Dynamics Understand Role of Linchpin Developers Frameworks, Data, Models to Support Future Studies
4 Research Model Cross Validation Conceptual Explanatory Model of OSS: Agent-Based Modeling and Simulation Combined Data Mining Parameter Values Parameter Values Structural Features Understanding the Social and Task Dynamics that Predict Developer Behaviors Social Network Analysis: Longitudinal Study of Preferential Attachment and Dynamic Attachment Structural Features Parameter Values
5 Overview Characteristics of Open Source Software (OSS) Development phenomenon Self-organized Decentralized Emergent properties Complex Adaptive Process Research Data collection Social Network Models Agent-Based Models Agent-Based Simulation
6 Open Source Software (OSS) GNU Savannah Free to view source to modify to share of cost Examples Apache Perl GNU Linux Sendmail Python KDE GNOME Mozilla Thousands more Linux
7 Leaders Larry Wall Perl Linus Tolvalds Linux Eric Raymond Cathedral and Bazaar Richard Stallman GNU Manifesto
8 Open Source Software (OSS) Development Mostly volunteer Global teams Virtual teams Self-organized - often peer-based meritocracy Self-managed - but often a charasmatic leader Often large numbers of developers, testers, support help, end user participation Rapid, frequent releases Mostly unpaid
9 Open Source Software (OSS): Significance Contradicts traditional wisdom: Software engineering Coordination, large numbers Motivation of developers Quality Security Business strategy Significant component of e- Commerce infrastructure Almost everything is done electronically and available in digital form Opportunity for Social Science Research -- large amounts of data available Research issues: Understanding motives Understanding processes Intellectual property Digital divide Self-organization Government policy Impact on innovation Ethics Economic models Cultural issues International factors
10 Open Source Software (OSS) Major Component of e-technology Infrastructure with major presence in e-commerce e-science e-government e-learning Apache has over 62% market share of Internet Web servers Linux on over 7 million computers Most Internet runs on Sendmail Tens of thousands of quality products Part of product offerings of companies like IBM, Apple Apache in WebSphere, Linux on mainframe, FreeBSD in OSX Corporate employees participating on OSS projects
11 Open Source Software (OSS) Seems to challenge traditional economic assumptions Model for software engineering New business strategies Cooperation with competitors Beyond trade associations, shared industry research, and standards processes shared product development! Virtual, self-organizing and self-managing teams Social issues, e.g., digital divide, international participation Government policy issues, e.g., US software industry, impact on innovation, security, intellectual property
12 Related Research Feller and Fitzgerald (ICIS, 2000) Research framework and analysis of the OSS phenomenon Hars and Ou (HICSS 2001) Survey of OSS developers Reported on motivations of developers Scacchi (IEE Proceedings - Software, 2002) Study of socio-technical processes associated with OSS development practices Wolf, Lakhani, and Bates (BCG/MIT Sloan, 2002) Survey of Source Forge Developers Hann, Roberts, Slaughter, and Fielding (ICSE, 2002) Survey of Apache developers - economic incentives Madey, Freeh, and Tynan (ICSE 2002, AMCIS 2002)
13 Collaborative Social Networks Small World Phenomenon Research papers on joint authorship Newman, 2001 Barabasi et al, 2001 Erdos number Kevin Bacon Game Open Source Software development Link detachment
14 Related Research: Methodology Watts and Strogatz, (Nature, 1998) Collective Dynamics of Small-World Networks Watts (Small Worlds, 1999) Pumain and Moriconi-Ebrard, (GeoJournal, 1997) Zipf distribution of city sizes Huberman and Adamic, (Nature, 1999) Growth Dynamics of the World Wide Web, Power Laws Axtell, (Science, 2001) Zipf Distribution of U.S. Firm Sizes, Barabasi, et al, (xxx.lanl.gov, 2001) Evolution of the Social Network of Scientific Collaborations, Power Law distribution
15 OSS as a Social Network Social Network Theory: Wasserman & Faust (1994), Watts (1999), and Barabasi (2002) Agents are nodes on a graph (developers) Edges are relationships (joint project participation) Growth of network: random or types of preferential attachment, formation of clusters Network attributes: diameter, average degree, power law, clusters developers node or vertex OSS joint project membership project circle of friends or clique edge or link hub project Social Network linchpin weak tie
16 SourceForge VA Software Part of OSDN Started 12/1999 Collaboration tools 58,685 Projects 80,000 Developers 590,005 Registered Users
17 Savannah Uses SourceForge Software Free Software Foundation 1,508 Projects 15,265 Registered
18 Data Collection Monthly Web crawler (scripts) Python Perl AWK Sed Monthly Since Jan 2001 ProjectID DeveloperID Almost 2 million records Relational database PROJ DEVELOPER 8001 dev dev dev dev dev dev dev dev dev dev7698
19 Project 6882 OSS Developer - Social Network Developers are nodes / Projects are links 24 Developers 5 Projects 2 Linchpin Developers 1 Cluster dev[72] Project 7597 dev[64] dev[67] dev[47] 6882 dev[47] dev[52] 6882 dev[47] dev[55] 6882 dev[47] 6882 dev[58] dev[79] dev[47] dev[79] dev[52] dev[55] dev[58] dev[83] Project Project 7028 dev[99] dev[51] dev[46] dev[58] dev[57] 7028 dev[46] 7028 dev[46] dev[57] dev[99] 7028 dev[46] dev[51] dev[46] dev[46] dev[56] dev[83] dev[46] dev[48] dev[48] dev[70] 7597 dev[46] dev[72] 7597 dev[46] dev[70] dev[46] dev[56] 7597 dev[46] dev[64] 7597 dev[46] dev[67] 7597 dev[46] dev[55] 7597 dev[46] dev[45] 7597 dev[46] dev[61] 7597 dev[46] dev[58] 9859 dev[46] dev[54] 9859 dev[46] 9859 dev[46] dev[49] dev[53] 9859 dev[46] dev[59] dev[53] dev[54] dev[58] dev[59] dev[49] Project 9859 dev[65] dev[45] dev[61]
20 Regression: Number of projects that developers are on # projects # of developers on that many projects Log(# of Developers) Log( # of Projects) y = x R 2 = Scale Free Power Law (developers)
21 Empirical Data Scale Free Power Law (projects) Growth Rates
22 Agent Based Model Grow an Artificial SourceForge Model as a Collaborative Social Network Parameterize model with empirical data Guess hidden processes, mechanisms and developer behaviors Agent Based Simulation Verify, validate, and iterate Discover how OSS works => Understanding
23 Agent Based Simulation Java Swarm / JDBC / Oracle Relational DB Same database table design, same analysis tools Developer class Each simulated developer is an instance of Developer with random attributes and behaviors Local decision logic Simulates self-organization Create new projects Join existing projects Abandon a project
24 Results: New Understanding? Random Attachment does not generate Power Law Preferential Attachment does, but Bipartite nature of the social network Preferential and random attachment can be implemented independently on developers and projects Both display power laws
25 Results: New Understanding? Young upstart phenomenon not captured with preferential attachment Added fitness Random fitness parameter for projects and developers Power law still observed in both projects and developers and young upstart feature modeled Still missing a real feature observed in the real SourceForge
26 Results: New Understanding Problem Static fitness insufficient Dynamic fitness needed Discovered a life cycle property of the projects Reflects both dynamic attachment and detachment Thus, dynamic fitness, with a life cycle Power law preserved!
27 Results/Limitations/Future Work Using social network theory, and agent-based modeling and simulation to gain understanding Role of social network theory and agent based modeling: A framework An investigation tool What does it mean if the simulated SourceForge fits the real SourceForge real well? Survey instruments to collect additional data on individual project and developer behavior More types of data More data sources Savannah Large OSS projects
28 Thank You Questions?
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