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Trigger-Action Programming in the Wild: An Analysis of 200,000 IFTTT Recipes Blase Ur, Melwyn Pak Yong Ho, Stephen Brawner, Jiyun Lee, Sarah Mennicken, Noah Picard, Diane Schulze, Michael Littman 1 2 Trigger-Action Programming 3


  1. Trigger-Action Programming in the Wild: An Analysis of 200,000 IFTTT Recipes Blase Ur, Melwyn Pak Yong Ho, Stephen Brawner, Jiyun Lee, Sarah Mennicken, Noah Picard, Diane Schulze, Michael Littman 1

  2. 2

  3. Trigger-Action Programming 3

  4. Trigger-Action Programming 4

  5. Trigger-Action Programming 5

  6. Trigger-Action Programming 6

  7. Trigger-Action Programming 7

  8. Trigger-Action Programming 8

  9. Trigger-Action Programming 9

  10. Trigger-Action Programming 10

  11. Trigger-Action Programming 11

  12. What are people creating on ? 12

  13. Methodology • Collect all publicly shared IFTTT recipes 13

  14. Methodology • Collect all publicly shared IFTTT recipes 14

  15. Methodology • Collect all publicly shared IFTTT recipes – Using Selenium & ChromeDriver 15

  16. Methodology • Collect all publicly shared IFTTT recipes – Using Selenium & ChromeDriver – As of September 6, 2015 16

  17. Methodology • Collect all publicly shared IFTTT recipes – Using Selenium & ChromeDriver – As of September 6, 2015 – Compare to 2013 dataset (CHI 2014) 17

  18. Methodology • Collect all publicly shared IFTTT recipes – Using Selenium & ChromeDriver – As of September 6, 2015 – Compare to 2013 dataset (CHI 2014) • Characterize recipes & ecosystem 18

  19. Methodology • Collect all publicly shared IFTTT recipes – Using Selenium & ChromeDriver – As of September 6, 2015 – Compare to 2013 dataset (CHI 2014) • Characterize recipes & ecosystem • Share dataset with other researchers 19

  20. Example 20

  21. Example: Trigger Channel 21

  22. Example: Trigger 22

  23. Example: Action Channel 23

  24. Example: Action 24

  25. Example: Author 25

  26. Example: Title 26

  27. Example: Adoptions 27

  28. Key Characteristics 28

  29. Key Characteristics • Huge growth from 2013  2015 29

  30. Key Characteristics • Huge growth from 2013  2015 – 67,820 recipes  224,590 recipes 30

  31. Key Characteristics • Huge growth from 2013  2015 – 67,820 recipes  224,590 recipes – 35,495 authors  106,452 authors 31

  32. Key Characteristics • Huge growth from 2013  2015 – 67,820 recipes  224,590 recipes – 35,495 authors  106,452 authors • Many authors, but few are prolific – Only 2.5% of authors shared 10+ recipes 32

  33. Key Characteristics • Huge growth from 2013  2015 – 67,820 recipes  224,590 recipes – 35,495 authors  106,452 authors • Many authors, but few are prolific – Only 2.5% of authors shared 10+ recipes • Many connections are being made 33

  34. Key Characteristics • Huge growth from 2013  2015 – 67,820 recipes  224,590 recipes – 35,495 authors  106,452 authors • Many authors, but few are prolific – Only 2.5% of authors shared 10+ recipes • Many connections are being made – 15,961 unique trigger-action combinations 34

  35. Key Insights 35

  36. Key Insights • IFTTT is big…and growing 36

  37. Key Insights • IFTTT is big…and growing 37

  38. Key Insights • IFTTT is big…and growing • Adoptions vs. duplicated functionality 38

  39. Key Insights • IFTTT is big…and growing • Adoptions vs. duplicated functionality 39

  40. Key Insights • IFTTT is big…and growing • Adoptions vs. duplicated functionality • Many diverse connections 40

  41. Key Insights • IFTTT is big…and growing • Adoptions vs. duplicated functionality • Many diverse connections Trigger channels Action channels 41

  42. Key Insights • IFTTT is big…and growing • Adoptions vs. duplicated functionality • Many diverse connections • ???????????? 42

  43. Trigger-Action Programming in the Wild: An Analysis of 200,000 IFTTT Recipes Download the dataset at www.upod.io/datasets Blase Ur, Melwyn Pak Yong Ho, Stephen Brawner, Jiyun Lee, Sarah Mennicken, Noah Picard, Diane Schulze, Michael Littman 43

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