How Users Interpret Bugs in Trigger-Action Programming
Authors
Trigger-action programming (TAP) is a programming model enabling users to connect services and devices by writing if-then rules. As such systems are deployed in increasingly complex scenarios, users must be able to identify programming bugs and reason about how to fix them. We first systematize the temporal paradigms through which TAP systems could express rules. We then identify ten classes of TAP programming bugs related to control flow, timing, and inaccurate user expectations. We report on a 153-participant online study where participants were assigned to a temporal paradigm and shown a series of pre-written TAP rules. Half of the rules exhibited bugs from our ten bug classes. For most of the bug classes, we found that the presence of a bug made it harder for participants to correctly predict the behavior of the rule. Our findings suggest directions for better supporting end-user programmers.
Research Questions / Practical Problems
Question signals indexed for this paper.
- 100%
Prototyping with Uncertainties: Data, Algorithms, and Research through Design
DIS '25· Prototyping & User Testing +1
- 100%
NotePlayer: Engaging Jupyter Notebooks for Dynamic Presentation of Analytical Processes
UIST '24· Prototyping & User Testing +1
- 80%
TmoTA: Simple, Highly Responsive Tool for Multiple Object Tracking Annotation
CHI '23· Customizable & Personalized Objects +2
- 80%
MyWebstrates: Webstrates as Local-first Software
UIST '24· Distributed Team Collaboration +2
- 75%
Computational Interaction: Theory and Practice
CHI '18· Computational Methods in HCI
- 75%
Model-based Evaluation of Recall-based Interaction Techniques
CHI '24· Computational Methods in HCI
- 75%
Verifying Finger-Fitts Models for Normalizing Subjective Speed-Accuracy Biases
MobileHCI '24· Prototyping & User Testing +1
- 75%
Small-Step Live Programming by Example
UIST '20· Computational Methods in HCI
- 67%
GPkit: A Human-Centered Approach to Convex Optimization in Engineering Design
CHI '20· User Research Methods (Interviews, Surveys, Observation) +2
- 67%
Screen2Vec: Semantic Embedding of GUI Screens and GUI Components
CHI '21· Explainable AI (XAI) +2
Based on Jaccard similarity of research subtopics & professions (≥60%)