Optimized Functionality for Super Mobile Apps

Functionality of software products often does not match user needs and expectations. The closed set-up of systems and information is replaced by wide access to data of users and competitor products. This shift offers completely new opportunities to approach requirements elicitation and subsequent planning of software functionality. This is, in particular true for app store markets. App stores are markets for many small sized software products which provide an open platform for users to provide feedback on using apps. Moreover, the functionality and status of similar software products can be retrieved. While this is a competitive risk, it is at the same time an opportunity.In this paper, we envision a new release planning approach that leverages the new opportunities for decision making. We propose a new model using bi-criterion integer programming. We make suggestions for optimized super app functionality that are based on two key aspects: (i) the estimated value of features, and (ii) the cohesiveness between newly added features and cohesiveness between existing and the features to be added. The information on these attributes comes from reasoning on feature composition of existing similar apps. The approach is applicable to the development of new product releases as well as to the creation of completely new apps. We illustrate the applicability of our model by a small example and outline directions for future research.