Supporting mental model accuracy in trigger-action programming

Abstract

Trigger-action programming is a simple programming model that enables users to create rules that automate behavior of smart homes, devices, and online services. Existing trigger-action programming systems, such as if-this-then-that (IFTTT), already have millions of users worldwide; however, their oversimplification limits the expressivity of the programs that can be created. While extensions of IFTTT to allow more complex programs have been proposed, previous work neglects a key distinction between different trigger types (states and events) and action types (instantaneous, extended, and sustained actions). In this paper, we systematically study the impact of these differences through two user studies that reveal: (i) inconsistencies in interpreting the behavior of trigger-action programs and (ii) errors made in creating programs with a desired behavior. Based on a characterization of these issues, we offer recommendations for improving the IFTTT interface so as to mitigate issues that arise from mental model inaccuracies.