trigger-actionprogramming (TAP) platforms allow users to personalize their digital ecosystems through the definition of trigger-action rules such as "if I'm leaving home, then turn the smart thermostat off.&...
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trigger-actionprogramming (TAP) platforms allow users to personalize their digital ecosystems through the definition of trigger-action rules such as "if I'm leaving home, then turn the smart thermostat off." Yet, little is known about whether such a paradigm can be used to support users' wellbeing. To bridge this gap, we scraped 6590 trigger-action programs from iOS shortcuts, and analyzed the dataset to understand what aspects of their wellbeing users are already programming and what opportunities remain untapped. Findings show that users are only capturing a fraction of this opportunity, with a majority of wellbeing-related programs targeting health and physical exercise. To shed light on an underexploited use case, we showcase two interventions for digital self-control developed through iOS shortcuts, highlighting challenges and opportunities to use TAP as a viable option to improve existing digital habits and self-regulate technology use, thus mitigating the negative effects of excessive digital engagement.
trigger-actionprogramming (TAP) is a popular end-user programming paradigm for constructing automation applications to orchestrate smart device collaboration. Existing TAP platforms employ a device-centric approach t...
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ISBN:
(纸本)9798400707056
trigger-actionprogramming (TAP) is a popular end-user programming paradigm for constructing automation applications to orchestrate smart device collaboration. Existing TAP platforms employ a device-centric approach to programming and executing TAP rules, which suffers limited flexibility and reusability when a same automation requirement is effective in different location. To this end, we develop a tool named laTAPE to support location-aware trigger-actionprogramming and executing. laTAPE supports users to specify triggers, condition states and actions involving locations which refer to either runtime user location or a predefined location. During runtime, laTAPE achieves the rule execution by leveraging corresponding environment devices determined by the user location obtained from smartphone. Our evaluation on real-world case study demonstrates usability and feasibility of laTAPE in rule programming and executing.
In this paper, we present the design and the evaluation of an authoring tool for End-User Development, which supports the definition of trigger-actions rules that combines events and states in the triggers. The possib...
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ISBN:
(纸本)9783030856168;9783030856151
In this paper, we present the design and the evaluation of an authoring tool for End-User Development, which supports the definition of trigger-actions rules that combines events and states in the triggers. The possibility of using either states or events in triggers has already been discussed in the literature. However, it is recognized that the state/event distinction is difficult to manage for users. In this paper, we propose an authoring tool that provides explicit support for managing this distinction. We compare it with a state-of-the-art authoring tool that implements the classical event-event paradigm.
To customize the behavior of a smart home, an end user writes rules. When an external event satisfies the rule's trigger, the rule's action executes;for example, when the temperature is above a certain thresho...
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ISBN:
(纸本)9781450345743
To customize the behavior of a smart home, an end user writes rules. When an external event satisfies the rule's trigger, the rule's action executes;for example, when the temperature is above a certain threshold, then window awnings might be extended. End users often write incorrect rules [16]. This paper presents a technique that prevents errors due to too few triggers in the rules. The technique statically analyzes a rule's actions to determine what triggers are necessary. We implemented the technique in a tool called TrigGen and tested it on 96 end user written rules for openHAB, an opensource home automation platform. It identified that 80% of the rules had fewer triggers than required for correct behavior. The missing triggers could lead to unexpected behavior and security vulnerabilities in a smart home.
This paper describes two empirical research studies that investigated how to improve naive users' mental models to support end-user development (EUD) of Internet-of-Things (IoT). Specifically, we intended to evalu...
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This paper describes two empirical research studies that investigated how to improve naive users' mental models to support end-user development (EUD) of Internet-of-Things (IoT). Specifically, we intended to evaluate the effectiveness of two different strategies, namely nudging and informing, to support trigger-action (TA) rule programming. To this aim, we analyzed non-expert users' performance and their verbal reports (Studies 1 and 2, respectively) in a task requiring the identification of the outcomes of the execution of specific sets of TA rules in different IoT scenarios. The triggering part of TA rules typically involves instantaneous and/or protracted events, and previous studies have shown that users' poor understanding of the distinction between these two types of events, as well as of the way in which the rules interact with each other, can result in poor TA programming performances. The first (experimental and quantitative) study shows that a nudging strategy (i.e., using two different temporal conjunctions, WHEN and WHILE, to introduce the rules' triggering conditions that refer to the two types of events instead of using the more common and generical IF) improves participants' understanding of the rules' behavior. It also provides some evidence that an informing strategy (i.e., providing participants with an explicit description of how the rules are evaluated and activated) can improve participants' accuracy in identifying the rules that did not realize the desired situation. The second (observational and qualitative) study suggests that the use of WHEN and WHILE in the triggering part of the rule helps participants distinguish the two types of events and understand their semantics. This work extends the current literature in EUD by providing both critical information about users' mental models in IoT and useful suggestions to make appropriate (linguistic and structural) choices when designing the interface that guides users in defining the rules.
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