Big data. Little Brain. Little data. Big brain. (with apologies to Dr. Seuss)

1. Big data. Little Brain.Little data. Big brain.(with apologies to Dr. Seuss) Gail C. MurphyUniversity of British ColumbiaTasktop Technologies Unless otherwise indicated on a particular slide, this work is licensed under a Creative Commons Attribution-Share Alike 2.5 Canada License

2. frustruation

3. problem big data >> little data little brain * situationalfactors transformationalfactors result *big data refers to much much more data than humans can consume

4. fact #1: software engineering is about solvinghuman problems

5. fact #2: software developments generate big data over time

6. fact #3: human cognition has limits

7. premise: our “solutions” often exceed human cognition <cartoon removed dueto license restrictions>

8. big data. little brain.big brain. little data. duplicate bug examples to derive “formula” big data >> little data little brain stories about formula propertiestransformational & situational factors take away h

9. duplicate bug problem

10. how does a developer realize/find a new bug is a duplicate?

11. big data big brain 1. duplicate bug search with keywords bug #56162:reported in 200016 comments hundreds of words bug #136422:reported in 200032 comments2x as long as #56162

12. big data >> little data big brain 2. duplicate bug search with natural language recommender 36-50% recall [Runenson et. Al, ICSE 2007][Hiew, MSc Thesis, 2006]

13. big data >> little data big brain 3. duplicate bug search with natural language +execution recommender 67-93% recall [Wang et. Al, ICSE 2008]

14. big data >> little data little brain 4. duplicate bug search with recommender & summarizer Summary:View source is broken. The second time I use it, the View source window fails to open, but I get an hourglass-and-pointer cursor. I can’t reproduce comment 0 with Mozilla/5.0 [Rastkar & Murphy, ICSE 2010]

15. interludesbig data >> little data significant reduction in data a human considers big brain versus little brain amount of cognitive and physical activity required to perform task

16. big data >> little data little brain

17. big data. little brain.big brain. little data. duplicate bug examples to “derive formula” big data >> little data little brain stories about formula propertiestransformation & situational factors take away

18. stories software reflexion modelhipikatmylyn spyglassto get at formula properties h

19. story #1: software reflexion model three case studies, including one at Microsoft [Murphy, Notkin, Sullivan, FSE 1995]

20. high-level model of part of Microsoft Excel [Murphy & Notkin, Computer 1997]

21. example (not Excel) low-level model(Excel : 15,000 functions/77,746 calls) Diagram from http://rixstep.com/1/1/20070206,00.shtml

22. mapping [ file = ^shtreal\.c function=foo mapTo=Graph ] [ file = ^shtreal\.c mapTo=Sheet ][ file = ^textfl1[ez]\.c$ mapTo=File ] blue entry is illustrative

23. reflexion model(partial)

24. big data >> little data little brainbig data = low-level modellittle data = high-level model transformational properties low-cost (albeit manual) assessable completeness predictable

25. low-cost because mapping can be specified simply,incrementally and partially mapping low-levelmodel high-levelmodel reflexionmodel easy to assesscompleteness mappedlow-levelmodel unmappedlow-levelmodel predictable

26. story #1: software reflexion models low-cost (manual)assessable completenesspredictable big data >> little data little brain

27. story #2: hipikat wizard-of-oz case study multiple case study (long sessions) manual precision/recall [Cubranic & Murphy, ICSE 2003]

28. [Cubranic, Murphy, Booth and Singer, TSE 2005]

30. + text(e.g., exception trace)

31. big data >> little data bigbrainbig data = project repositorieslittle data = recommendations transformational properties low-cost (automatic) situational factors context (automatic)

32. low-cost because project memoryauto-updates multiple entry points provide auto-context

33. story #2: hipikat big data >> little data bigbrain low-cost (automatic) context (automatic)

34. story #3: mylyn three field studies adoption in practice [Kersten & Murphy, FSE 2006]

35. demo

36. big data >> little data little brainbig data = workspace informationlittle data = focused workspace information transformational properties low-cost (automatic) assessable completeness predictable situational factors pervasive fits in workflow

37. complete, predictable,automatic (with manual override) pervasive

38. folding/ & content assist test workflow change sets

39. story #3: mylyn big data >> little data little brain low-cost (automatic)assessable completenesspredictable pervasivefits in workflow

40. story #4: spyglass longitudinal case study controlled lab study [Viriyakattiyaporn & Murphy, CASCON 2010]

42. big data >> little data bigbrainbig data = user interactions in workspacelittle data = recommended commands transformational properties low-cost (automatic) situational factors (mostly) fits in workflow

43. (mostly) fits in workflow automatic

44. story #4: spyglass big data >> little data bigbrain low-cost (automatic) (mostly) fits in workflow

45. lessons

46. lessons about transformational factorsbig data >> little datalow-cost (manual or automatic) assessable completeness predictable

47. need sufficiently high precision/recall?

48. lessons about situational factorslittle data little braincontext (automatic)pervasive workflow

49. big brain provide intent automatically? how to handle specialized tools?