This document discusses visualization for software analytics and identifies three key trends: 1) developers moving from solo coders to social coders, 2) software development shifting from code-centric to data-centric, and 3) visualization becoming ubiquitous rather than standalone. It provides examples of visualizations for software design, code, dynamic behavior, architecture, and human activities. It discusses how visualization can provide insights, support tasks, and communicate knowledge. It also outlines opportunities and challenges for visual analytics and ubiquitous visualization in software engineering.

1. Visualization for Software Analytics
Margaret-Anne (Peggy) Storey
@margaretstorey
#vissoft14 #icsme14
@margaretstorey
#vis4se

2. Why visualization?
Provide insights
Answer questions
Support wayfinding
Tell stories
Communicate knowledge, awareness
!
!
!
!

3. For software…
Visualize what?

4. Design
UML

5. Algorithms
bost.ocks.org/mike/algorithms/

6. Code,
dependencies http://thechiselgroup.org/2005/07/06/zest/
http://swerl.tudelft.nl/bin/view/Main/ExTraVis

7. Dynamic behaviour
Cleary, B., Storey, M., Chan, L., Salois, M., Painchaud, F.,
"ATLANTIS - Assembly Trace Analysis Environment,"
Working Conference on Reverse Engineering (WCRE), 2012.
http://hapao.dcc.uchile.cl

8. Architecture
Wettel, R. & Lanza, M. "CodeCity: 3D
visualization of large-scale software,” (ICSE
Companion '08), 2008
Creole: http://thechiselgroup.org/
2003/07/06/creole/

9. Gourse: visualizing commits
Human activities

10. Software ecosystems

11. ADOPTION
Lessons learned?
THEORIES
METHODS
FLOW
USERS
TASKS

12. Diver:
Myers, D. & Storey, M. "Focusing on Execution Traces Using Diver," 18th Working Conference on
Reverse Engineering (WCRE), 2011, pp.439-440

13. A theory of cognitive support for Diver…

14. Framework…
Dimensions Characteristics Elements
Intent Role Team, Developer, Manager, Researcher, Maintainer, Reengineer
Time Present, Recent Past, Historical
Authorship Authorship, Rationale, Time, Artifacts
Information Change management Local History, Releases, Releases, Branching, Revisions
Defect tracking Defects, Changes
Program code Modules/components, Syntactic units (e.g. files), Semantic analysis
Documentation Requirements, Design, Test cases, Architecture
Informal communication Email, Instant messages
Derived/Aggregated Single source, multiple source
Presentation Form Text, Hypertext, Graphical
Kinds of views Annotated views, Statistical views, Graph views, Special views
Techniques Visual variables (colour, position etc), Animation, 2D/3D
Interaction Batch/Live Offline, Online, Customizable
Customization Level of customization, sharing and saving customizations
Queries Query language, Visual queries, Filter widgets
View navigation Multiple views, Overview+detail, Zoomable views, Coupled
Effectiveness System Implemented, Availability, Scalability, Interoperability
Cost Economic cost, Installation, Learning, Usage
Evaluation Adopted, Case study, User study
Storey, M.-A. & Cubranic, D. & German, D. M. "On the use of visualization to support awareness of
human activities in software development: a survey and a framework," ACM symposium on
Software Visualization (SoftVis), 2005.

15. What’s next?

16. Three trends to consider…
Developers:
solo coder -> social coder
Software development:
code centric -> data centric
Visualization:
standalone -> ubiquitous

17. Three trends to consider…
Developers:
solo coder -> social coder
Software development:
code centric -> data centric
Visualization:
standalone -> ubiquitous

18. “I know how this was done because I did it”
“I need complete understanding”
Peter Norvig, Coders at Work

19. “How is this likely done?”
“Can I quickly get an understanding of what I need?”
Peter Norvig, Coders at Work
“Google
team?”

20. Space Place
P. Dourish and V. Bellotti. Awareness and Coordination in Shared Workspaces. Proceedings of
the ACM Conference on Computer-Supported Cooperative Work (CSCW'92).

21. Developer tools…
1968 1970 1980 1990 2000 2010

22. Nondigital Digital Digital & Socially Enabled
Societies LinkedIn
Documents
Project
Workbook
Podcasts
Coderwall
Masterbranch
Yammer
Punchcards TFS
Email
Face2Face
Telephone
Email Lists
VisualAge
SourceForge
Wikis
Visual Studio
NetBeans Eclipse
IRC
Meetups
Basecamp
Jazz
ICQ Skype
1968 1970 1980 1990 2000 2010
Trello
Campfire
Google
Hangouts
Books Usenet
Stack
Overflow
Twitter
Google
Groups
Blogs
GitHub
Conferences
Facebook
Slashdot HackerNews
Storey, M.-A., L. Singer, F. Figueira Filho, B. Cleary and A. Zagalsky,The (R)evolutionary Role of Social Media in Software Engineering,
ICSE 2014 Future of Software Engineering Track, 36th International Conference on Software Engineering (ICSE 2014) Hyderabad.

23. Social Media and
Participatory Cultures [Jenkins]
Low barriers to artistic expression and engagement
Strong support for sharing one’s creations
Informal mentorship for novices
Members believe their contributions matter
Members care about social connections and what
others think about their creations
3

24. The Participatory Culture in
Software Engineering is not new
Internet and free/open source projects
Linux and the bazaar model of programming
Global software development (GSD)
Historical importance of tools and the
social shaping of communities
4

25. Three trends to consider…
Developers:
solo coder -> social coder
Software development:
code centric -> data centric
Visualization:
standalone -> ubiquitous

26. Era of software analytics
!
Code centric -> (Big) Data centric
User feedback -> usage logs, social media
In lab testing -> large scale testing in the wild
Centralized -> distributed development
Long product cycle -> continuous releases
!
!

27. Quiz!!! Which code should I test!
1. Which day of the week is likely to produce the
buggiest code? Mon-Sun?
!
2. Who produces more buggy code?
Junior or Senior Developers?
!
3. Which metrics are most useful in predicting defects?
a. Lines of code,
b. complexity of the code,
c. number of developers that worked on the code,
d. previous bugs in the code, or
e. code churn

28. Software Analytics: A definition
Software Analytics is to enable software
practitioners to perform data exploration and
analysis to obtain insightful and actionable
information for data-driven tasks around
software and services.
Dongmei Zhang & Tao Xie, http://research.microsoft.com/en-us/groups/sa/
softwareanalyticsinpractice_minitutorial_icse2012.pdf

29. Goals of software analytics?
To improve:
- quality of the software
- experience of the users
- productivity of the developers
!
Dongmei Zhang & Tao Xie, http://research.microsoft.com/en-us/groups/sa/
softwareanalyticsinpractice_minitutorial_icse2012.pdf

30. Prolific data sources and
analysis techniques
Program data: runtime traces, program logs,
system events, failure logs, performance…
!
User data: usage logs, user surveys, user
forums, twitter and blogs, …
!
Development data: versions, bug data,
commits, testing, communication

31. Need for actionable insights
To support decision making
“use data rather than fortune tellers”
[A. Hassan]
!
!
!
But need more than data!
!
http://www.slideshare.net/taoxiease/software-analytics-towards-software-mining-that-matters

32. The need for visual analytics!
Focus has been on:
- acquiring/cleaning/managing the data
- analytics
- understanding which questions to ask…
One of the key pillars to support software
analytics is visualization [Zhang & Xie]
Dongmei Zhang & Tao Xie, http://research.microsoft.com/en-us/groups/sa/
softwareanalyticsinpractice_minitutorial_icse2012.pdf

33. Three trends to consider…
Developers:
solo coder -> social coder
Software development:
code centric -> data centric
Visualization:
standalone -> ubiquitous

34. Recap: Why software visualization?
Provide insights
Answer questions
Support wayfinding
Tell stories
Communicate knowledge, awareness
!
!
!
!

35. Visualization ubiquity
Visual analytics (gain insights)
Deep integration (cognitive support in context)
Infographics (tell a story)
Dashboards (awareness)
!

36. Visualization ubiquity
Visual analytics (gain insights)
Deep integration (cognitive support in context)
Infographics (tell a story)
Dashboards (awareness)
!

37. Visual analytics
Information visualization process:
overview, filter and zoom, details on demand
!
!
vs
!
Visual analytics process:
analyze first, show the important, zoom, filter
and analyze further, details on demand

38. Visualization ubiquity
Visual analytics (gain insights)
Deep integration (cognitive support in context)
Infographics (tell a story)
Dashboards (awareness)
!

39. Visual debugging: Debugger Canvas
http://www.youtube.com/watch?v=3p9XUwIlhJg

41. Visualization ubiquity
Visual analytics (gain insights)
Deep integration (cognitive support in context)
Infographics (tell a story)
Dashboards (awareness)
!

42. Infographics
Tells a story, quickly
Shared socially
Interactive
!
Popular, accessible: visual.ly, Tableau Public
!
Examples: New York Times, Tagging,
Stackoverflow, Twitter…
!

43. http://www.nytimes.com/newsgraphics/2013/07/21/silk-road/

44. Tagging work items in
C. Treude and M.-A. Storey. Work Item Tagging: Communicating Concerns in Collaborative Software Development. In IEEE
Transactions on Software Engineering 38, 1 (January/February 2012). pp. 19-34.

45. ConcernLines

46. http://githut.info

47. Coverage of API documentation: 77% of the
Java API classes & 87% of Android API classes
Speed of coverage:
C. Parnin, C. Treude, L. Grammel and M.-A. Storey. Crowd Documentation: Exploring the Coverage and the Dynamics of API
Discussions on Stack Overflow”. at http://blog.ninlabs.com/2012/05/crowd-documentation/ May 2012.

48. Crowd authored API documentation!
http://latest-print.crowd-documentation.appspot.com/?api=android

49. http://graphoverflow.com

50. How developers use Twitter
!
Awareness
Learning
Relationships
Why non-adoption
Strategies
“It was evolving way faster than I was
able to keep up with it. And the only
way to keep up was to follow some
Node.js people on Twitter.”
Leif Singer, Fernando Figueira Filho, Margaret-Anne Storey. Software Engineering at the Speed of Light:
How Developers Stay Current Using Twitter ICSE 2014.

51. Sentiments on Twitter for: shellshock
http://www.csc.ncsu.edu/faculty/healey/tweet_viz/tweet_app/

52. Visualization ubiquity
Visual analytics (gain insights)
Deep integration (cognitive support in context)
Infographics (tell a story)
Dashboards (awareness)
!

53. Dashboards
Awareness
Making informed decisions
Live data
Business intelligence

54. Dashboards for developer awareness
Treude, C., and M.-A. Storey, “Awareness 2.0: staying aware of projects, developers and tasks using dashboards and
feeds,” in ICSE’10: Proc. of the 32nd ACM/IEEE Int. Conference on Software Engineering, ACM.

55. Assessing and watching developers
!
L. Singer, F. F. Filho, B. Cleary, C. Treude, M.-A. Storey, K. Schneider. Mutual Assessment in the Social Programmer Ecosystem: An
Empirical Investigation of Developer Profile Aggregators Blog: http://to.leif.me/devprofiles

56. Recap…
Developers:
solo coder -> social coder
Visualization for software analytics
Software development:
code centric -> data centric
Visualization:
standalone -> ubiquitous

57. Opportunities and challenges
TL;DR
!
Mobile
!
Scale
!
Visualizations as social media
!
Visual software analytics should be
actionable!

58. Visualize and share your research results!
http://think.withgoogle.com/databoard/

59. Takeaways
Software developers are the prototype
knowledge workers of tomorrow
!
Software visualization has come of age:
social coder
software analytics
ubiquitous visualization

60. Acknowledgements
CHISEL group, UVic, Canada:
– Christoph Treude
– Brendan Cleary
– Alexey Zagalsky
– Peter Rigby
– Lars Grammel
– ……
Chris Parnin, NCSU
Leif Singer, I Done This
Daniel German, UVic
Arie van Deursen, TU Delft
Fernando Figueira Filho, Brazil

61. Selected additional References
Software visualization:
Stasko, J. T., Brown, M. H. & Price, B. A. (Eds.) Software Visualization MIT Press, 1997
Petre, M. "UML in practice," Proceedings of the 2013 International Conference on
Software Engineering (ICSE), 2013, pp.722-731
Blackwell, A., Britton, C., Cox, A., Green, T., Gurr, C., Kadoda, G., Kutar, M., Loomes,
M., Nehaniv, C., Petre, M., Roast, C., Roe, C., Wong, A. & Young, "Cognitive
Dimensions of Notations: Design Tools for Cognitive Technology Cognitive
Technology: Instruments of Mind," Springer Berlin Heidelberg, 2001, vol.2117, pp.
325-341
DeLine, R., Bragdon, A., Rowan, K., Jacobsen, J., & Reiss, S. "Debugger canvas:
industrial experience with the code bubbles paradigm," Proceedings of the 34th
International Conference on Software Engineering (ICSE), 2012, pp.1064-1073.
Bull, R. I. & Storey, M.-A. "Towards visualization support for the eclipse modeling
framework," A Research-Industry Technology Exchange, 2005
Cleary, B., Gorman, P., Verbeek, E., Storey, M.-A, Salois, M., Painchaud, F.,
"Reconstructing program memory state from multi-gigabyte instruction traces to
support interactive analysis," 20th Working Conference on Reverse Engineering
(WCRE), Oct. 2013, pp.42-51
!
!

62. Social coding:
Communities of practice: http://www.ewenger.com/theory/
!
C. Treude and M.-A. Storey. Effective Communication of Software Development
Knowledge Through Community Portals. ESEC/FSE ’11.
M.-A. Storey, C. Treude, A. van Deursen and L.-T. Cheng. The Impact of Social
Media on Software Engineering Practices and Tools. In FoSER
’10: Proceedings of the FSE/SDP workshop on Future of software engineering
research.
!
Storey, M.-A., L. Singer, F. Figueira Filho, B. Cleary and A. Zagalsky,The
(R)evolutionary Role of Social Media in Software Engineering, ICSE 2014
Future of Software Engineering Track, 36th International Conference on Software
Engineering (ICSE 2014) Hyderabad, 2014
!
Begel, A., J. Bosch, and M.-A. Storey., Social Networking Meets Software
Development: Perspectives from GitHub, MSDN, Stack Exchange, and
TopCoder. Software, IEEE 30.1 (2013): 52-66.
!!!!!

63. Software analytics:
IEEE Software — two special issues on Software Analytics, July/August 2013
Tao Xie’s tutorial on software analytics: http://www.slideshare.net/taoxiease/software-analytics-
towards-software-mining-that-matters
!
Research methods:
Easterbrook, S., Singer, J., Storey, M.-A. & Damian, D. "Selecting Empirical Methods for
Software Engineering Research," Guide to Advanced Empirical Software Engineering,
Springer London, 2008, pp.285-311
Walenstein, A., "Observing and measuring cognitive support: steps toward systematic
tool evaluation and engineering," 11th IEEE International Workshop on Program
Comprehension (IWPC), 2003, pp.185-194
Begel, A. & Zimmermann, T. "Analyze this! 145 questions for data scientists in software
engineering," Proceedings of the 36th International Conference on Software
Engineering (ICSE), 2014, pp.12-23.
!
Visual analytics:
Illuminating the path: http://vis.pnnl.gov/pdf/RD_Agenda_VisualAnalytics.pdf
Mark Smiciklas (2012). The Power of Infographics: Using Pictures to Communicate and
Connect with Your Audience.
!

64. Takeaways
Software developers are the prototype
knowledge workers of tomorrow
!
Software visualization has come of age:
social coder
software analytics
ubiquitous visualization