UX-MAPPER: A User eXperience Method to Analyze App Store Reviews

Resumo


The mobile app market has grown over the last decades. With the rise of app stores, users can easily choose an app from thousands, making them less tolerant of low-quality apps. More than ever, users are looking for apps that provide not only valuable functionalities but pleasurable experiences. Hence, User eXperience (UX) became the differential to stand out from competitors. By understanding what factors affect UX, practitioners could focus on factors that lead to positive UX while mitigating those that affect UX negatively. In this context, reviews from app stores emerged as a valuable source of information to investigate such factors. However, analyzing millions of reviews is costly and time-consuming. This paper presents UX-MAPPER, an approach to analyzing app store reviews and supporting practitioners in identifying factors affecting UX. We applied the Design Science Research method to design UX-MAPPER iteratively and grounded on a solid theoretical background. We performed exploratory studies to investigate the problem, a systematic mapping study to identify factors that affect UX, and an empirical study with 14 participants with experience in requirements engineering to determine the relevance and acceptance of our proposal from practitioners’ perspectives. The participants considered it useful to improve the quality of existing apps and explore the reviews of competing apps to identify functionalities and features that users are requesting, liking, or hating. They were also willing to use it when it became available, highlighting our proposal’s usefulness and relevance in software development.
Palavras-chave: user experience, user reviews, machine learning, app stores

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Publicado
16/10/2023
NAKAMURA, Walter T.; C. DE OLIVEIRA, Edson César; H. T. DE OLIVEIRA, Elaine; CONTE, Tayana. UX-MAPPER: A User eXperience Method to Analyze App Store Reviews. In: SIMPÓSIO BRASILEIRO SOBRE FATORES HUMANOS EM SISTEMAS COMPUTACIONAIS (IHC), 22. , 2023, Maceió/AL. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 .