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.
more »
« less
AutoTap: Synthesizing and Repairing Trigger-Action Programs Using LTL Properties
End-user programming, particularly trigger-action programming (TAP), is a popular method of letting users express their intent for how smart devices and cloud services interact. Unfortunately, sometimes it can be challenging for users to correctly express their desires through TAP. This paper presents AutoTap, a system that lets novice users easily specify desired properties for devices and services. AutoTap translates these properties to linear temporal logic (LTL) and both automatically synthesizes property-satisfying TAP rules from scratch and repairs existing TAP rules. We designed AutoTap based on a user study about properties users wish to express. Through a second user study, we show that novice users made significantly fewer mistakes when expressing desired behaviors using AutoTap than using TAP rules. Our experiments show that AutoTap is a simple and effective option for expressive end-user programming.
more »
« less
- NSF-PAR ID:
- 10106414
- Date Published:
- Journal Name:
- Proceedings of the 41st International Conference on Software Engineering
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
The field of end-user robot programming seeks to develop methods that empower non-expert programmers to task and modify robot operations. In doing so, researchers may enhance robot flexibility and broaden the scope of robot deployments into the real world. We introduce PRogramAR (Programming Robots using Augmented Reality), a novel end-user robot programming system that combines the intuitive visual feedback of augmented reality (AR) with the simplistic and responsive paradigm of trigger-action programming (TAP) to facilitate human-robot collaboration. Through PRogramAR, users are able to rapidly author task rules and desired reactive robot behaviors, while specifying task constraints and observing program feedback contextualized directly in the real world. PRogramAR provides feedback by simulating the robot’s intended behavior and providing instant evaluation of TAP rule executability to help end users better understand and debug their programs during development. In a system validation, 17 end users ranging from ages 18 to 83 used PRogramAR to program a robot to assist them in completing three collaborative tasks. Our results demonstrate how merging the benefits of AR and TAP using elements from prior robot programming research into a single novel system can successfully enhance the robot programming process for non-expert users.more » « less
-
Trigger-action programming lets end-users automate and connect IoT devices and online services through if-this-then-that rules. Early research demonstrated this paradigm's usability, but more recent work has highlighted complexities that arise in realistic scenarios. As users manually modify or debug their programs, or as they use recently proposed automated tools to the same end, they may struggle to understand how modifying a trigger-action program changes its ultimate behavior. To aid in this understanding, we prototype user interfaces that visualize differences between trigger-action programs in syntax, behavior, and properties.more » « less
-
Two common approaches for automating IoT smart spaces are having users write rules using trigger-action programming (TAP) or training machine learning models based on observed actions. In this paper, we unite these approaches. We introduce and evaluate Trace2TAP, a novel method for automatically synthesizing TAP rules from traces (time-stamped logs of sensor readings and manual actuations of devices). We present a novel algorithm that uses symbolic reasoning and SAT-solving to synthesize TAP rules from traces. Compared to prior approaches, our algorithm synthesizes generalizable rules more comprehensively and fully handles nuances like out-of-order events. Trace2TAP also iteratively proposes modified TAP rules when users manually revert automations. We implemented our approach on Samsung SmartThings. Through formative deployments in ten offices, we developed a clustering/ranking system and visualization interface to intelligibly present the synthesized rules to users. We evaluated Trace2TAP through a field study in seven additional offices. Participants frequently selected rules ranked highly by our clustering/ranking system. Participants varied in their automation priorities, and they sometimes chose rules that would seem less desirable by traditional metrics like precision and recall. Trace2TAP supports these differing priorities by comprehensively synthesizing TAP rules and bringing humans into the loop during automation.more » « less
-
Research has shown that trigger-action programming (TAP) is an intuitive way to automate smart home IoT devices, but can also lead to undesirable behaviors. For instance, if two TAP rules have the same trigger condition, but one locks a door while the other unlocks it, the user may believe the door is locked when it is not. Researchers have developed tools to identify buggy or undesirable TAP programs, but little work investigates the usability of the different user-interaction approaches implemented by the various tools. This paper describes an exploratory study of the usability and utility of techniques proposed by TAP security analysis tools. We surveyed 447 Prolific users to evaluate their ability to write declarative policies, identify undesirable patterns in TAP rules (anti-patterns), and correct TAP program errors, as well as to understand whether proposed tools align with users’ needs. We find considerable variation in participants’ success rates writing policies and identifying anti-patterns. For some scenarios over 90% of participants wrote an appropriate policy, while for others nobody was successful. We also find that participants did not necessarily perceive the TAP anti-patterns flagged by tools as undesirable. Our work provides insight into real smart-home users’ goals, highlights the importance of more rigorous evaluation of users’ needs and usability issues when designing TAP security tools, and provides guidance to future tool development and TAP research.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICSE.2019.00043
