DOI: 10.1145/3816769 ISSN: 2573-0142

Gestural Annotation of User Interface Requirements in Crowd Elicitation EICS017

Andrew Arnita, Emilio Insfran, Nuwan T Attygalle, Jean Vanderdonckt

User interface requirements elicitation is an early and critical stage in the software development life cycle: requirements are elicited from stakeholders, and user interface elements that should satisfy these requirements are also created, shared, and edited. To effectively capture the diverse perspectives of stakeholders on these requirements, crowd elicitation is a promising approach to leverage the collective knowledge of a time- and space-distributed group of stakeholders to manage user interface requirements and their associated user interface elements. Annotating these elements manually in freeform digital ink leads to results that are difficult to interpret and time-consuming to exploit by designers and developers. Instead, we propose that stakeholders annotate user interface elements by commonly-agreed gestures that will be automatically recognized to populate 10 annotation metrics. To this end, we present

Crowditivity
, a publicly available web application that effectively harnesses crowd elicitation in three related activities: (1) collecting and managing user interface functional and non-functional requirements according to an adapted Volere Shell template; (2) assigning user interface elements of a mockup or prototype to requirements to satisfy them; and (3) enabling stakeholders to annotate these elements by ink-based gestures. A 2D multi-stroke gesture classifier is specifically tailored to recognize gesture classes corresponding to 18 annotation types, that are empirically validated through a gesture elicitation study ( n 1 = 26) confirmed by an identification study ( n 2 = 59). Through an evaluation of a case study for a web application, we investigated how eight representative designers valued the
Crowditivity
platform. We finally define and illustrate a set of ten metrics to quantify the output of
Crowditivity
in terms of annotations and annotators resulting from the gesture recognition.

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