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“Never doubt that a small group of thoughtful,
committed citizens can change the world. Indeed, it is the only thing that ever
has.”--Margaret Mead
Thank You R Hackers of NYC
Harvesting & Analyzing Interaction Data in R:
The Case of MyLyn
Sean P. Goggins, PhDDrexel University
MyLyn Research Collaborators:
Peppo Valetto, PhD (PI) & Kelly Blincoe
I Study Small GroupsI use electronic trace data, interviews, field notes, electronic content & surveys for raw data
Coolest Open* Data to Me Group’s Emerging & Evolving
Group Formation & Development
The long tail of social computing, which I describe as everything *except* Wikipedia & Facebook
Groups constructing knowledge, creating information and forming identity.
*Available, but not always easy to get in an analyzable form
Points Harvesting Small, Open Data [MyLyn]
Analyzing Temporal Changes in the MyLyn Network
Work
Talk
Libraries Used & Source Code StatNet
iGraph
TNET
R Sourcecode and Data will be available for download at http://www.groupinformatics.org . If you use this data or scripts please cite:
Goggins, S. P., Laffey, J., Amelung, C., and Gallagher, M. 2010. Social Intelligence In Completely Online Groups. IEEE International Conference on Social Computing. 500-507. DOI=10.1109/SocialCom.2010.79.
Blincoe, K., Valetto, G., and Goggins, S. 2011. Leveraging Task Contexts for Managing Developers’ Coordination. Under Review.
Data for RAn Example From the MyLyn Project
More About MyLyn: http://tasktop.com/blog/http://www.eclipse.org/mylyn/
Bug Database
HTML Parser
MySQL Database
MyLyn ContextUploads
Work
Talk
.zip file
Talk
Talk
Talk Cues
TalkWork
Coordination Requirements & Dependencies
MyLyn Data Has 2 Advantages for Analysis compared to source Control systems analysis:1. You see files *viewed*
together2. Discourse on a Bug is
directly connected to the files read and edited1. Closer connection
between analysis of work & talk.
Talk
Work
Harvesting Data for RAn Example From the MyLyn Project
MyLyn Interaction Datamart
Interaction Warehouse
MyLyn
CANS
ETC
Talk
Work
TalkWork
Analyzing Open Data with R
An Example From the MyLyn Project
Analysis Tools Eight Mylyn Releases (Temporal Analysis)
R Packages Used TNET
iGraph
Statnet
Weighted Network: TNET
The Dense Graph (Work) Developers create a dense graph. Not a
complete graph, but dense.
Work
A Sparser Graph (Talk) Commenter's create a sparse graph
Talk
Release One (2.0) Analysis
CodeDiscussion
WorkTalk
iGraph
Release 1
STATNET for Discussion StatNet
Red = Bug CommenterBlue = Bug Opener
StatNET
Talk
Release 1
Release OneWork & Talk
Release 1 (2.0) iGraph & Statnet
Talk
Clusters
In Degree & Out Degree
Red = Bug CommenterBlue = Bug Opener
iGraph
StatNET
Release 1
Google Summer Coder
Release One (2.0): Filtered
Code Discussion
304, 373, 399 & 143 formThe Strongest ConnectionsIn both networks
Red = Bug CommenterBlue = Bug Opener
Talk
WorkRelease 1
Release One (2.0): Filtered
Code Discussion
304, 373, 399 & 143 formThe Strongest ConnectionsIn both networks
Red = Bug CommenterBlue = Bug Opener
Google Summer Coder
TalkWork
457, 391 & 159 – Comment & Open
Release 1
Compare Over TimeFirst & Last Release
Release 1 (2.0) Compared to Release 8 (3.3)
Talk
304, 399, 143, 159, 173, 373
399, 118, 304, 159, 391, 416StatNET & ordinary plotting
Release 1
Release 8
Release 1 (2.0) Compared to Release 8 (3.3)
Work
Two disconnectedGraphs in release 8
304, 373, 399 & 143
Release 1
Release 8
143 & 304 disengagedOr missing entirely
iGraph
Release EightWork & Talk
Release 8 (3.3): Filtered
Code
Discussion
Red = Bug CommenterBlue = Bug Opener
Talk
Work
Nobody is“Just Blue”
Release 8
Release 8 (3.3): Filtered
Code
Discussion
Red = Bug CommenterBlue = Bug Opener
Talk
Work
Notice 416 in Talk & Second Coder Graph
Release 8
Talk
Clusters
In Degree & Out Degree
Red = Bug CommenterBlue = Bug Opener
iGraph
StatNET
Release 8 (3.3) iGraph & Statnet
399, 118 & 159 are significant, But play with different clusters of Other people.
BlueCluste
r
Release 8
Releases One EightHigh Level Views Over Time
Discussion, Releases 1 – 8
Where there is no color,There are multiple, incompleteGraphs.
Code, Releases 1 – 8
One Possible explanation: A few centralPeople who slowly butObservably begin to engageOther contributors inAn open source softwareDevelopment project.
Structure evolvesKey Groups Evolve
iGraph
Next Step: The StoryBut that’s the research part, not the cool “R Stuff” Part
The People
399304
159
143
373
Our next step is piecing together a narrative about the groups that emerged on this project, and describing each of the individuals. This is all open data. When we finish this part, we will publish one or more papers. For now, Let’s look at the cool “R Stuff”
Interaction Traces from Small Groups: The Case
of MyLyn
Sean P. Goggins, PhDDrexel University
Collaborators:
Peppo Valetto, PhD & Kelly Blincoe
Questions? In the after session.