Visualization for Software Analytics

M
Margaret-Anne StoreyProfessor of Computer Science at University of Victoria
Visualization for Software Analytics 
Margaret-Anne (Peggy) Storey 
@margaretstorey 
#vissoft14 #icsme14 
@margaretstorey 
#vis4se
Why visualization? 
Provide insights 
Answer questions 
Support wayfinding 
Tell stories 
Communicate knowledge, awareness 
! 
! 
! 
!
For software… 
Visualize what?
Design 
UML
Algorithms 
bost.ocks.org/mike/algorithms/
Code, 
dependencies http://thechiselgroup.org/2005/07/06/zest/ 
http://swerl.tudelft.nl/bin/view/Main/ExTraVis
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
Architecture 
Wettel, R. & Lanza, M. "CodeCity: 3D 
visualization of large-scale software,” (ICSE 
Companion '08), 2008 
Creole: http://thechiselgroup.org/ 
2003/07/06/creole/
Gourse: visualizing commits 
Human activities
Software ecosystems
ADOPTION 
Lessons learned? 
THEORIES 
METHODS 
FLOW 
USERS 
TASKS
Diver: 
Myers, D. & Storey, M. "Focusing on Execution Traces Using Diver," 18th Working Conference on 
Reverse Engineering (WCRE), 2011, pp.439-440
A theory of cognitive support for Diver…
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.
What’s next?
Three trends to consider… 
Developers: 
solo coder -> social coder 
Software development: 
code centric -> data centric 
Visualization: 
standalone -> ubiquitous
Three trends to consider… 
Developers: 
solo coder -> social coder 
Software development: 
code centric -> data centric 
Visualization: 
standalone -> ubiquitous
“I know how this was done because I did it” 
“I need complete understanding” 
Peter Norvig, Coders at Work
“How is this likely done?” 
“Can I quickly get an understanding of what I need?” 
Peter Norvig, Coders at Work 
“Google 
team?”
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).
Developer tools… 
1968 1970 1980 1990 2000 2010
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.
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
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
Three trends to consider… 
Developers: 
solo coder -> social coder 
Software development: 
code centric -> data centric 
Visualization: 
standalone -> ubiquitous
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 
! 
!
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
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
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
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
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
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
Three trends to consider… 
Developers: 
solo coder -> social coder 
Software development: 
code centric -> data centric 
Visualization: 
standalone -> ubiquitous
Recap: Why software visualization? 
Provide insights 
Answer questions 
Support wayfinding 
Tell stories 
Communicate knowledge, awareness 
! 
! 
! 
!
Visualization ubiquity 
Visual analytics (gain insights) 
Deep integration (cognitive support in context) 
Infographics (tell a story) 
Dashboards (awareness) 
!
Visualization ubiquity 
Visual analytics (gain insights) 
Deep integration (cognitive support in context) 
Infographics (tell a story) 
Dashboards (awareness) 
!
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
Visualization ubiquity 
Visual analytics (gain insights) 
Deep integration (cognitive support in context) 
Infographics (tell a story) 
Dashboards (awareness) 
!
Visual debugging: Debugger Canvas 
http://www.youtube.com/watch?v=3p9XUwIlhJg
Visualization for Software Analytics
Visualization ubiquity 
Visual analytics (gain insights) 
Deep integration (cognitive support in context) 
Infographics (tell a story) 
Dashboards (awareness) 
!
Infographics 
Tells a story, quickly 
Shared socially 
Interactive 
! 
Popular, accessible: visual.ly, Tableau Public 
! 
Examples: New York Times, Tagging, 
Stackoverflow, Twitter… 
!
http://www.nytimes.com/newsgraphics/2013/07/21/silk-road/
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.
ConcernLines
http://githut.info
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.
Crowd authored API documentation! 
http://latest-print.crowd-documentation.appspot.com/?api=android
http://graphoverflow.com
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.
Sentiments on Twitter for: shellshock 
http://www.csc.ncsu.edu/faculty/healey/tweet_viz/tweet_app/
Visualization ubiquity 
Visual analytics (gain insights) 
Deep integration (cognitive support in context) 
Infographics (tell a story) 
Dashboards (awareness) 
!
Dashboards 
Awareness 
Making informed decisions 
Live data 
Business intelligence
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.
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
Recap… 
Developers: 
solo coder -> social coder 
Visualization for software analytics 
Software development: 
code centric -> data centric 
Visualization: 
standalone -> ubiquitous
Opportunities and challenges 
TL;DR 
! 
Mobile 
! 
Scale 
! 
Visualizations as social media 
! 
Visual software analytics should be 
actionable!
Visualize and share your research results! 
http://think.withgoogle.com/databoard/
Takeaways 
Software developers are the prototype 
knowledge workers of tomorrow 
! 
Software visualization has come of age: 
social coder 
software analytics 
ubiquitous visualization
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
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 
! 
!
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. 
!!!!!
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. 
!
Takeaways 
Software developers are the prototype 
knowledge workers of tomorrow 
! 
Software visualization has come of age: 
social coder 
software analytics 
ubiquitous visualization
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Visualization for Software Analytics

  • 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 ! ! ! !
  • 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
  • 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.
  • 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… !
  • 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.
  • 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
  • 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