Abstract
The Trigger-Action programming paradigm has been widely adopted in the last few years, especially in the Internet of Things (IoT) domain because it allows end users without programming experience to describe how their applications should react to the many events that can occur in such very dynamic contexts. Several end user development tools exist, in both the research and industrial fields, which aim to support the increasing need to specify such rules. Thus, it becomes important for application developers and domain experts to enrich such environments with functionalities able to monitor how users actually interact with such rule editors, and show useful information to analyse the end user activity. In this paper, we present a visual tool for monitoring and analysing how users interact with a trigger-action rule editor. The goal is to provide a tool useful to better understand what end users’ personalization needs are, how they are expressed, how users actually specify rules, and whether users encounter any issues in interacting with the personalization features offered by the editors. The proposed solution supports the analysis through a dashboard and a set of timelines describing the actual use of the personalization tool, with the possibility to select specific events of interest. It moreover provides data useful for understanding the types of triggers, actions and rules actually composed by users, and whether they effectively exploit the personalization features offered.
Highlights
A consequence of the rapid spread of the Internet of Things (IoT) is that the environments where we live and act are increasingly characterized by the presence of a multitude of interactive devices and smart objects interconnected with each other
HistoryViewer [15] is a system that aims to support exploration of log data obtained from user interactions. In this case the goal is to support final users for communication purposes, and not usability evaluators, by describing the interactions that took place in a way they can recall and communicate their own discoveries about the data, not focused on the interaction mechanisms or on difficulties they may have encountered. From such proposals, in this work we focus on providing designers of trigger-action rule editors and IoT application developers with interactive visualisations supporting exploration and filtering of the logged relevant interaction data, so as to derive higher-level information such as the types of rules that users were interested in creating with the tool, the most popular trigger and action types used, and the types of usage patterns followed by users while interacting with the tool
We first introduce the trigger-action programming environment that has been considered in this study, and we report on the initial set of requirements that have been identified for the visualizations to provide
Summary
A consequence of the rapid spread of the Internet of Things (IoT) is that the environments where we live and act are increasingly characterized by the presence of a multitude of interactive devices and smart objects interconnected with each other. End users can know the most appropriate ways their applications should react to dynamic contextual events For such reasons, in order to obtain applications able to adapt to the context of use in an effective way it becomes important to allow end users themselves to ‘program’ the behaviour of their applications. In order to obtain applications able to adapt to the context of use in an effective way it becomes important to allow end users themselves to ‘program’ the behaviour of their applications In this trend, trigger-action programming has emerged as a useful and intuitive approach. Triggers can be instantaneous events (corresponding to context changes) and/or conditions that, if satisfied, activate the execution of specific actions This type of approach has stimulated several contributions both from the research [e.g. 2, 3, 4, 5, 9] and industrial viewpoints (IFTTT, Tasker, Zapier, Resonance AI, ...). Huang and Cakmak [8] found that users may encounter difficulties interpreting the differences between events and conditions or between action types, and such misunderstandings can cause undesired behaviours
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