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Evolutionary visual software analytics

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Evolutionary visual software analytics

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Evolutionary visual software analytics

  1. 1. EVOLUTIONARY VISUAL SOFTWARE ANALYTICS Antonio González Torres Doctoral Advisors Roberto Therón Sánchez Francisco J. García Peñalvo Ph.D. Dissertation Faculty of Science Department of Computer Science and Automation May 21, 2015
  2. 2. Presentation Outline
  3. 3. Research Problem
  4. 4. Research Problem
  5. 5. Research Problem
  6. 6. Research Problem
  7. 7. Research Problem
  8. 8. Research Problem
  9. 9. Research Problem
  10. 10. Research Problem
  11. 11. Research Problem
  12. 12. Research Questions Main Research Question Secondary Research Questions
  13. 13. Concepts §  Software Configuration Management (SCM) §  Software Evolution §  Software Evolution Analysis
  14. 14. Visual Analysis Process
  15. 15. Visual Analysis Process Keim et al.
  16. 16. Visual Analysis Process Keim et al.
  17. 17. Systematic Mapping Study Research Question Inclusion and Exclusion Criteria § Time period of the study: 2007 – 2013 § Papers studied: 219 filtered and revised, 149 papers were evaluated § Relevance of papers § Full papers § Type of proposal or evaluation § Types of data analysis supported § Time period of data under analysis
  18. 18. Survey to Practioners §  Research Question §  Participants
  19. 19. Survey to Practioners Reason for not using visualization tools during software development
  20. 20. Survey to Practioners Perceived adoption blockers for visualization tools
  21. 21. Focused Discussion §  Scalability and easy to use tools for team awareness and system structure §  66% of the respondents use the basic visualizations included in IDE tools to examine class hierarchies and navigating dependencies §  44.5% make use of the basic version-tree visualization included in SCM tools §  Visualization and VA tools must be integrated into IDEs and SCM, in the form of plugins
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  31. 31. Timelines
  32. 32. Granular Timeline
  33. 33. Linear Timeline
  34. 34. Linear Timeline
  35. 35. Linear Timeline
  36. 36. Linear Timeline
  37. 37. Linear Timeline
  38. 38. Revision Tree
  39. 39. Revision Tree
  40. 40. Revision Tree
  41. 41. Granular Timeline
  42. 42. Gridmaster
  43. 43. Gridmaster
  44. 44. Socio Technical Graph
  45. 45. Vídeo https://youtu.be/ojACBL04XJM
  46. 46. User Study
  47. 47. User Study
  48. 48. User Study
  49. 49. Conclusions 1.  Companies use SCM and bug tracking tools to record and manage data related to maintenance and development processes 2.  Simple visualizations are used by the software industry, in their majority, integrated into SCM and IDE tools, but programmers are not aware of the options that these tools have available 3.  Research works mostly use software evolution visualization approaches and do not involve user abilities actively 4.  Tools needs to be prepared for the analysis of large datasets associated to the evolution of systems
  50. 50. Conclusions 5.  The definition and description of the application of Visual Analytics to Software Evolution was identified, thus it was successfuly defined and validated 6.  It is recommended to involve users throughout the design of tools based on the Evolutionary Visual Software Analytics process ü  Requirement elicitation ü  Design ü  Brainstorming reviews ü  Evaluations and usability studies
  51. 51. Future Research 1.  Software development is increasingly being developed using cloud based IDEs, SCM and bug tracking tools 2.  Programmers and managers require support to understand software system changes and evolution in a similar manner to desktop based development 3.  Visual Analytics tools should be integrated into these cloud based tools in the form of plugins or core components
  52. 52. Publications v Journals: 4 -- Conferences: 9 1.  González-Torres, A., García-Peñalvo, F. J., Therón, R, Colomo-Palacios R. Science of Computer Programming, accepted, ISSN: 0167-6423 (Impact factor: 0.548) 2.  González-Torres, A., García-Peñalvo, F. J., Therón, R. Human Computer Interaction in Evolutionary Visual Software Analytics. Computers in Human Behavior, vol. 29, no. 2, pages 486-495 (March 2013) ISSN: 0747-5632 (Impact factor: 2:273) 3.  González-Torres, A., García-Peñalvo, F. J., Therón, R. How Evolutionary Visual Software Analytics Supports Knowledge Discovery. Journal of Information Science and Engineering, vol. 29, no. 1, pages 17-34 (January, 2013) ISSN: 1016-2364 (Impact factor: 0:333) 4.  García, J., Gómez-Aguilar, D. A., González-Torres, A., García-Peñalvo, F. J., Therón, R. A Middleware Framework to Create Data Structures for a Visual Analytics Object Oriented Approach. International Journal of Knowledge and Learning, Vol. 6, no. 2/3 pages 256-267 (2010) ISSN: 1741-1009
  53. 53. QUESTIONS?
  54. 54. THANKS!
  55. 55. EVOLUTIONARY VISUAL SOFTWARE ANALYTICS Antonio González Torres Doctoral Advisors Roberto Therón Sánchez Francisco J. García Peñalvo Ph.D. Dissertation Faculty of Science Department of Computer Science and Automation May 21, 2015

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