Dynamic Social Network Analysis (and more!) with eResearch Tools

Andrea Wiggins

iSchool @ Syracuse University

21 July, 2008

eResearch for FLOSS

• An approach to research using cyberinfrastructure

• Collaborative and transparent, like FLOSS

• Large-scale shared data sets– FLOSSmole– Notre Dame SourceForge dumps– CVSanalY– Etc…

• Uses tools and analyses that allow sharing among researchers to support open science ideals– Taverna Workbench– MyExperiment.org

Using Taverna

• Scientific analysis workflow tool– Open source development lead by myGrid team

– Target users are UK life sciences community

• Create analysis workflows by connecting modular components through input/output ports– Produces (rigorous) analyses that are replicable, self-

documenting, and easy to share

– Components include WSDL SOAP web services, Beanshell, RShell, and local Java shims

• Collaboratively developing our workflows

Replicating FLOSS Research as eResearch

• Replicating a selection of FLOSS papers and presentations, currently in progress

• Demonstrating utility and viability of eResearch approaches for FLOSS and social science

• Building reusable, customizable analysis components specific to FLOSS research, e.g. for data selection, sociomatrix generation for SNA, etc.

• Extending the original research analysis by parameterization (inputs, thresholds) and implementing “future work” suggestions of authors (plus our own ideas, of course)

Social dynamics of FLOSS communications

• Replication of Howison, Inoue & Crowston, 2006– Compute dynamic network centrality of projects from

trackers for 120 projects

• Extension– Added exponentially-decayed edge weighting function

(needs sensitivity testing)

– Made sliding window adjustable

– Can apply to any threaded communication venue for which data is available

– Completed: all venues for 2 projects; queued: 216 projects with 635 venues!

Workflow for Dynamic SNA

Dynamic SNA Across FLOSS Communication Channels

• Clearly a lot of variation across channels (user, developer & trackers), no easily observed patterns except overall trend toward decentralization

• Implications: carefully match theoretical constructs to data sampling, as different venues are very likely to yield different results, which significantly impacts interpretations

“Do the Rich Get Richer?”

• Replication of OSCon 2004 presentation by Conklin– Demonstrate scale-free distribution of developers

among projects

• Almost there– A little more analysis to replicate

• Hoping to extend to dynamic analysis of preferential attachment– Showing change to project sizes over time

– Comparing evolution and growth across repositories

Workflow for Rich Get Richer

• Using a single FLOSSmole summary statistic• Very simple workflow, can expand analysis considerably• Analysis of over 65K projects completes in under 3 minutes!

Scale-free Developer Distribution in FLOSS

“Identifying success and tragedy of FLOSS Commons”

• Replication of English & Schweik, 2007– Classification of project success by stage of growth for

110K projects as of August 2006

– Requires data from 2 repositories, FLOSSmole & ND

• Extension– Parameterized all thresholds, makes sensitivity

analysis possible

– Added 2 additional options for a criterion test, one suggested by authors in article

– Limitation: slightly less available data in FLOSSmole, 94K projects as of April 2005

Workflow for Success-Abandonment Classification

Classifying FLOSS Projects

• Very complex data requirements; meshing across repositories

• Difficult to scale and resource intensive

• Already using this workflow for project sampling

• For small (non-random) sample of 54 projects:– 64% growth, 17% initiation,

19% null (i.e. missing data)• Indeterminate Growth:

18.9%

• Success Growth: 39.6%

• Tragedy Growth: 7.5%

• Other: 34%

amsn,downloaded,growth,enough.releases,active,ok.release.rate,true,SGanjuta,downloaded,growth,enough.releases,active,ok.release.rate,false,SGanon,downloaded,growth,enough.releases,inactive,fast.release.rate,false,TGetc…

Future Directions

• Replication of “Evolution & Growth in Large Libre Software Projects” by Robles et al., 2005

• Prototyping OWL ontology of FLOSS communication data, already in use with RDF & SPARQL

• Cross-linking data, analyses, and papers• Increasing scale of analyses to thousands of projects• Extending analyses, sensitivity testing to strengthen

findings• Building reusable analysis components to share,

enabling cumulative research

Thanks!

• More at floss.syr.edu/publications/