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MediaVis VISSOFT 2017

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On the Impact of the Medium in the Effectiveness of 3D Software Visualization.
Leonel Merino, Johannes Fuchs, Michael Blumenschein, Mohammad Ghafari, Oscar Nierstrasz, Craig Anslow, Michael Behrisch, Daniel Keim

Published in: Data & Analytics
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MediaVis VISSOFT 2017

  1. 1. On the Impact of the Medium in the Effectiveness of 3D Software Visualization merino@inf.unibe.ch @leonel_merino Leonel Merino, Johannes Fuchs, Michael Blumenschein, Mohammad Ghafari, Oscar Nierstrasz, Craig Anslow, Michael Behrisch, Daniel Keim Software Composition Group University of Bern Data Analysis and Visualization Group University of Konstanz School of Engineering and Computer Science Victoria University of Wellington
  2. 2. The Medium There are multiple media available to display software visualizations 2
  3. 3. The Medium 3 However, the most popular medium is standard computer screen (SCS). There are multiple media available to display software visualizations
  4. 4. 4 How does using different media for software visualization affect the effectiveness? media effectiveness
  5. 5. Performance Recollection User Experience 5 mediaeffectiveness Medium vs Effectiveness
  6. 6. Task Software Comprehension Experiment Design
  7. 7. Task Software Comprehension Audience Software Maintainer Experiment Design
  8. 8. Task Software Comprehension Audience Software Maintainer Medium 8 Experiment Design
  9. 9. Task Software Comprehension Audience Software Maintainer Medium 9 Experiment Design
  10. 10. Task Software Comprehension Audience Software Maintainer Medium Technique 10 Experiment Design
  11. 11. Task Software Comprehension Audience Software Maintainer Medium Technique 11 Experiment Design
  12. 12. Task Software Comprehension Audience Software Maintainer Medium Technique Data Real-world Open Source Small / Medium / Large Size 12 Experiment Design Qualitas Corpus
  13. 13. Task Software Comprehension Audience Software Maintainer Medium Technique Data Real-world Open Source Small / Medium / Large Size 13 Axion Experiment Design
  14. 14. In-Between Subject Groups 14
  15. 15. 15 Visualization CodeCity in Moose 5 Apparatus Apple MacBook Pro with a resolution of 1440 x 900 pixels Location Konstanz (4) + Bern (5) Participants 1 PostDoc, 3 BA/MA, 5 PhD Subject Freemind, Azureus Deployment: SCS
  16. 16. 16 Visualization Custom development in Unity 5.5 Apparatus HTC Vive VR Headset with a 2160 x 1200 combined resolution, 90 Hz refresh rate and 110° field of view Location Konstanz (9) Participants 1 PostDoc, 3 BA/MA, 5 PhD Subject Freemind, Azureus Deployment: I3D
  17. 17. 17 Visualization Model exported to the Stereo Lithography (STL) format from the I3D implementation (in Unity) required by the printer using the pb_Stl library Apparatus Form 2 3D printer by formlabs based on stereolithography (SLA) technology Location Bern (9) Participants 1 PostDoc, 3 BA/MA, 5 PhD Subject Freemind, Azureus Deployment: P3D
  18. 18. 18 Deployment: P3D
  19. 19. 19 Deployment: P3D
  20. 20. 20 Deployment: P3D
  21. 21. 21 Deployment: P3D
  22. 22. 22 Visualization Model exported to the Stereo Lithography (STL) format from the I3D implementation (in Unity) required by the printer using the pb_Stl library Apparatus Form 2 3D printer by formlabs based on stereolithography (SLA) technology Location Bern (9) Participants 1 PostDoc, 3 BA/MA, 5 PhD Subject Freemind, Azureus Deployment: P3D
  23. 23. 23 Performance User Experience Recollection Evaluation Methods
  24. 24. Results 24
  25. 25. Freemind (600 classes) Azureus (6600 classes) Performance Accuracy Finding Outliers Finding Patterns Location and Quantification Completion Time Finding Outliers Finding Patterns Location and Quantification User Experience Difficulty Finding Outliers Finding Patterns Location and Quantification Feelings Recollection Quantitative Analysis 25
  26. 26. Freemind (600 classes) Azureus (6600 classes) Performance Accuracy Finding Outliers Finding Patterns Location and Quantification Completion Time Finding Outliers Finding Patterns Location and Quantification User Experience Difficulty Finding Outliers Finding Patterns Location and Quantification Feelings Recollection Quantitative Analysis 26
  27. 27. Freemind (600 classes) Azureus (6600 classes) Performance Accuracy Finding Outliers Finding Patterns Location and Quantification Completion Time Finding Outliers Finding Patterns Location and Quantification User Experience Difficulty Finding Outliers Finding Patterns Location and Quantification Feelings Recollection Quantitative Analysis 27
  28. 28. Freemind (600 classes) Azureus (6600 classes) Performance Accuracy Finding Outliers Finding Patterns Location and Quantification Completion Time Finding Outliers Finding Patterns Location and Quantification User Experience Difficulty Finding Outliers Finding Patterns Location and Quantification Feelings Recollection Quantitative Analysis 28
  29. 29. Freemind (600 classes) Azureus (6600 classes) Performance Accuracy Finding Outliers Finding Patterns Location and Quantification Completion Time Finding Outliers Finding Patterns Location and Quantification User Experience Difficulty Finding Outliers Finding Patterns Location and Quantification Feelings Recollection Quantitative Analysis 29
  30. 30. Freemind (600 classes) Azureus (6600 classes) Performance Accuracy Finding Outliers Finding Patterns Location and Quantification Completion Time Finding Outliers Finding Patterns Location and Quantification User Experience Difficulty Finding Outliers Finding Patterns Location and Quantification Feelings Recollection Quantitative Analysis 30
  31. 31. FreemindAzureus 31
  32. 32. Freemind (600 classes) Azureus (6600 classes) Performance Accuracy Finding Outliers Finding Patterns Location and Quantification Completion Time Finding Outliers Finding Patterns Location and Quantification User Experience Difficulty Finding Outliers Finding Patterns Location and Quantification Feelings Recollection Quantitative Analysis 32
  33. 33. Performance Recollection User experience Completion Time Accuracy • Navigation is difficult in SCS • Outliers are easily detected in P3D • Highly accurate • Several strategies to estimate • Better recollection of unexpected aspects • Isolated aspects are forgotten if users cannot relate them to something meaningful • I3D participants were highly motivated (a participant of SCS asked to use the visualization in I3D just for fun) • Users of I3D felt “quiet”, users of SCS felt “sure”, and users of P3D felt “sensitive” and “touched", but also “dissatisfied” and “powerless” Qualitative Analysis 33
  34. 34. Future Work 34 Impact of media using a different: • Technique: 2D visualization techniques • Task: collaborative visualization
  35. 35. Ongoing Work 35 • Exploiting I3D for boosting engagement in software visualization
  36. 36. Summary merino@inf.unibe.ch @leonel_merinoscg.unibe.ch/research/mediavis

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