Review of mobile application performance evaluation to enhance selection and prediction in mobile app development - Aleem, Muhammad; Noorhuzaimi, Mohd Noor; Ali, Ehtesham; Ibrahim, Hassan; Al Mamun, Mohd Abdullah; Bashir, Muhammad Talha (Institute of Information Technology, Kohat University of Science and Technology, 2024)
In the rapidly evolving mobile application landscape, understanding user preferences and optimizing application performance are critical factors for developers and consumers alike. This study aims to provide a structured framework for analyzing mobile application research conducted between 2019 and 2023, categorizing efforts into predictive analytics, user sentiment analysis, and feature prioritization to streamline the processes of application selection and development. The proliferation of mobile apps and diverse user needs have created a complex environment, with users struggling to identify suitable applications and developers facing challenges in ensuring functionality and profitability. This comprehensive review synthesizes findings from the past four years, proposing an integrated structure to leverage predictive analytics for anticipating user needs, user sentiment analysis for understanding customer preferences, and feature prioritization for optimizing application development. By adopting this holistic approach, the research aims to enhance the selection process for users and improve the overall performance and profitability of mobile applications.