Visualization for Software Analytics

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Keynote for VISSOFT 2014

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Visualization for Software Analytics

  1. 1. Visualization for Software Analytics Margaret-Anne (Peggy) Storey @margaretstorey #vissoft14 #icsme14 @margaretstorey #vis4se
  2. 2. Why visualization? Provide insights Answer questions Support wayfinding Tell stories Communicate knowledge, awareness ! ! ! !
  3. 3. For software… Visualize what?
  4. 4. Design UML
  5. 5. Algorithms bost.ocks.org/mike/algorithms/
  6. 6. Code, dependencies http://thechiselgroup.org/2005/07/06/zest/ http://swerl.tudelft.nl/bin/view/Main/ExTraVis
  7. 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. 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. 9. Gourse: visualizing commits Human activities
  10. 10. Software ecosystems
  11. 11. ADOPTION Lessons learned? THEORIES METHODS FLOW USERS TASKS
  12. 12. Diver: Myers, D. & Storey, M. "Focusing on Execution Traces Using Diver," 18th Working Conference on Reverse Engineering (WCRE), 2011, pp.439-440
  13. 13. A theory of cognitive support for Diver…
  14. 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. 15. What’s next?
  16. 16. Three trends to consider… Developers: solo coder -> social coder Software development: code centric -> data centric Visualization: standalone -> ubiquitous
  17. 17. Three trends to consider… Developers: solo coder -> social coder Software development: code centric -> data centric Visualization: standalone -> ubiquitous
  18. 18. “I know how this was done because I did it” “I need complete understanding” Peter Norvig, Coders at Work
  19. 19. “How is this likely done?” “Can I quickly get an understanding of what I need?” Peter Norvig, Coders at Work “Google team?”
  20. 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. 21. Developer tools… 1968 1970 1980 1990 2000 2010
  22. 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. 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. 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. 25. Three trends to consider… Developers: solo coder -> social coder Software development: code centric -> data centric Visualization: standalone -> ubiquitous
  26. 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. 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. 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. 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. 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. 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. 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. 33. Three trends to consider… Developers: solo coder -> social coder Software development: code centric -> data centric Visualization: standalone -> ubiquitous
  34. 34. Recap: Why software visualization? Provide insights Answer questions Support wayfinding Tell stories Communicate knowledge, awareness ! ! ! !
  35. 35. Visualization ubiquity Visual analytics (gain insights) Deep integration (cognitive support in context) Infographics (tell a story) Dashboards (awareness) !
  36. 36. Visualization ubiquity Visual analytics (gain insights) Deep integration (cognitive support in context) Infographics (tell a story) Dashboards (awareness) !
  37. 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. 38. Visualization ubiquity Visual analytics (gain insights) Deep integration (cognitive support in context) Infographics (tell a story) Dashboards (awareness) !
  39. 39. Visual debugging: Debugger Canvas http://www.youtube.com/watch?v=3p9XUwIlhJg
  40. 40. Visualization ubiquity Visual analytics (gain insights) Deep integration (cognitive support in context) Infographics (tell a story) Dashboards (awareness) !
  41. 41. Infographics Tells a story, quickly Shared socially Interactive ! Popular, accessible: visual.ly, Tableau Public ! Examples: New York Times, Tagging, Stackoverflow, Twitter… !
  42. 42. http://www.nytimes.com/newsgraphics/2013/07/21/silk-road/
  43. 43. 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.
  44. 44. ConcernLines
  45. 45. http://githut.info
  46. 46. 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.
  47. 47. Crowd authored API documentation! http://latest-print.crowd-documentation.appspot.com/?api=android
  48. 48. http://graphoverflow.com
  49. 49. 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.
  50. 50. Sentiments on Twitter for: shellshock http://www.csc.ncsu.edu/faculty/healey/tweet_viz/tweet_app/
  51. 51. Visualization ubiquity Visual analytics (gain insights) Deep integration (cognitive support in context) Infographics (tell a story) Dashboards (awareness) !
  52. 52. Dashboards Awareness Making informed decisions Live data Business intelligence
  53. 53. 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.
  54. 54. 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
  55. 55. Recap… Developers: solo coder -> social coder Visualization for software analytics Software development: code centric -> data centric Visualization: standalone -> ubiquitous
  56. 56. Opportunities and challenges TL;DR ! Mobile ! Scale ! Visualizations as social media ! Visual software analytics should be actionable!
  57. 57. Visualize and share your research results! http://think.withgoogle.com/databoard/
  58. 58. Takeaways Software developers are the prototype knowledge workers of tomorrow ! Software visualization has come of age: social coder software analytics ubiquitous visualization
  59. 59. 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
  60. 60. 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 ! !
  61. 61. 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. !!!!!
  62. 62. 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. !
  63. 63. 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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