1. “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

2. Thank You R Hackers of NYC

3. Harvesting & Analyzing Interaction Data in R: The Case of MyLyn Sean P. Goggins, PhD Drexel University outdoors@acm.org MyLyn Research Collaborators: Peppo Valetto, PhD (PI) & Kelly Blincoe

4. I Study Small Groups MyLyn – Software Engineering CANS/Sakai – Online Learning Virtual Math Teams http://www.mathforum.org Health Care Communities (Under NDA) Small Group Interactions I use electronic trace data, interviews, field notes, electronic content & surveys for raw data

5. 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

6. 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: http://www.groupinformatics.org  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 Harvest Analyze

7. Data for R An Example From the MyLyn Project Data Harvest Analyze

8. More About MyLyn: http://tasktop.com/blog/ http://www.eclipse.org/mylyn/ Bug Database HTML Parser MySQL Database MyLyn Context Uploads Work Talk.zip file Talk

9. Talk Cues TalkWork

10. Coordination Requirements & Dependencies MyLyn Data Has 2 Advantages for Analysis compared to source Control systems analysis: 1.You see files *viewed* together 2.Discourse on a Bug is directly connected to the files read and edited 1.Closer connection between analysis of work & talk. Talk Work

11. Harvesting Data for R An Example From the MyLyn Project Data Harvest Analyze

12. MyLyn Interaction Datamart Files Accessed & Edited Bugs/Tasks Worked on Developer Context Initial Bug Description -> Task Discussion Related to Bugs Bug/Task Context Work – Bug/Task Action Talk – Bug/Task Discussion Integrated Repository Interaction Warehouse MyLyn CANS ETC Data Harvest Analyze Talk Work TalkWork

13. Analyzing Open Data with R An Example From the MyLyn Project Data Harvest Analyze

14. Analysis Tools  Eight Mylyn Releases (Temporal Analysis)  R Packages Used  TNET  iGraph  Statnet

15. Weighted Network: TNET

16. The Dense Graph (Work)  Developers create a dense graph. Not a complete graph, but dense. Work

17. A Sparser Graph (Talk)  Commenter's create a sparse graph Talk

18. Release One (2.0) Analysis Code Discussion Work Talk iGraph

19. STATNET for Discussion  StatNet Red = Bug Commenter Blue = Bug Opener StatNET Talk

20. Release One Work & Talk

21. Release 1 (2.0) iGraph & Statnet Talk Clusters In Degree & Out Degree Red = Bug Commenter Blue = Bug Opener iGraph StatNET

22. Google Summer Coder Release One (2.0): Filtered CodeDiscussion 304, 373, 399 & 143 form The Strongest Connections In both networks Red = Bug Commenter Blue = Bug Opener Talk Work

23. Release One (2.0): Filtered CodeDiscussion 304, 373, 399 & 143 form The Strongest Connections In both networks Red = Bug Commenter Blue = Bug Opener Google Summer Coder TalkWork 457, 391 & 159 – Comment & Open

24. Compare Over Time First & Last Release

25. Release 1 (2.0) Compared to Release 8 (3.3) Talk 304, 399, 143, 159, 173, 373 399, 118, 304, 159, 391, 416 StatNET & ordinary plotting

26. Release 1 (2.0) Compared to Release 8 (3.3) Work Two disconnected Graphs in release 8 304, 373, 399 & 143 143 & 304 disengaged Or missing entirely iGraph

27. Release Eight Work & Talk

28. Release 8 (3.3): Filtered Code Discussion Red = Bug Commenter Blue = Bug Opener Talk Work Nobody is “Just Blue”

29. Release 8 (3.3): Filtered Code Discussion Red = Bug Commenter Blue = Bug Opener Talk Work Notice 416 in Talk & Second Coder Graph

30. Talk Clusters In Degree & Out Degree Red = Bug Commenter Blue = Bug Opener iGraph StatNET Release 8 (3.3) iGraph & Statnet 399, 118 & 159 are significant, But play with different clusters of Other people. Blue Cluster

31. Releases One  Eight High Level Views Over Time

32. Discussion, Releases 1 – 8 Where there is no color, There are multiple, incomplete Graphs.

33. Code, Releases 1 – 8 One Possible explanation: A few central People who slowly but Observably begin to engage Other contributors in An open source software Development project. Structure evolves Key Groups Evolve iGraph

34. Next Step: The Story But that’s the research part, not the cool “R Stuff” Part

35. The People 399 304 159 143 373 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”

36. Interaction Traces from Small Groups: The Case of MyLyn Sean P. Goggins, PhD Drexel University outdoors@acm.org Collaborators: Peppo Valetto, PhD & Kelly Blincoe Questions? In the after session.