Emerging mobile apps: challenges and open problems

Smartphone applications are increasingly popular for use in all aspects of life, from work to entertainment to health and education. The latest generation of smartphone apps exhibit three characteristics that introduce serious implications for developers and end-users, namely, incorporation of non-traditional interactions, interfaces, and environments; utilization of machine learning for core functionality; and use of commercial libraries and APIs to supply this functionality. In this paper, we explore these characteristics in the context of two case study applications: an audio-enabled application for Amazon Alexa, and a mobile augmented reality (AR) application using PTC’s Vuforia. Based on these case studies, we explore how each characteristic impacts both the developers and end-users of such applications, such as the difficulty in managing the open-endedness of real-world environments, clarifying system functionality to users, acquiring sufficient data sets for training the machine learning functions, and the privacy implications of utilizing third-party APIs and libraries. We go on to discuss open research problems that arise from these challenges, and how solutions in these areas would benefit developers and end-users.