Yes, UI/UX Is Worth It: Investigating Scalable Productivity of UX Research Data with Machine Learning

Abstract

UI/UX is one of the most relevant fields of the decade, taking the tech industry and general public by storm. The popularization of UI/UX following historical deprioritization of the user has proven to yield immense returns for users and businesses. However, many companies still show reluctance to invest in UI/UX due to perceived unimportance, cost, and unscalability of UX research methods central to learning about the user’s perspective. This paper proposes a novel approach to increasing the scalability and productivity of the UI/UX field by capitalizing on large repositories of unused UX research data. This investigation utilizes the Rico UI image dataset and implements a 99-participant survey that labels UI screens with a usability score, in order to emulate the kind of data generated by the most prevalent UX research practice: usability testing. The feasibility of modeling relationships between whole UI screens and user experience data given existing UI representation methods is evaluated using linear regression on layout-based, pixel-based, and structure-based UI vectorizations. This paper’s research process offers an explorative assessment on the sufficiency of current literature for large-scale, cross-context machine learning applications on UI data, and brings to light the future research necessary to support this imminent but unexplored task.