While researchers have long investigated end-user programming using a trigger-action (if-then) model, the website IFTTT is among the first instances of this paradigm being used on a large scale. To understand what IFTTT users are creating, we scraped the 224,590 programs shared publicly on IFTTT as of September 2015 and are releasing this dataset to spur future research. We characterize aspects of these programs and the IFTTT ecosystem over time. We find a large number of users are crafting a diverse set of end-user programs---over 100,000 different users have shared programs. These programs represent a very broad array of connections that appear to fill gaps in functionality, yet users often duplicate others' programs.

We investigate the practicality of letting average users customize smart-home devices using trigger-action ("if, then") programming. We find trigger-action programming can express most desired behaviors submitted by participants in an online study. We identify a class of triggers requiring machine learning that has received little attention. We evaluate the uniqueness of the 67,169 trigger-action programs shared on IFTTT.com, finding that real users have written a large number of unique trigger-action interactions. Finally, we conduct a 226-participant usability test of trigger-action programming, finding that inexperienced users can quickly learn to create programs containing multiple triggers or actions.