Abstract
Facing the increasingly fierce competition, app developers have to update features of their products continually. In this process, developers need to not only consider users’ demands but also pay attention to what other similar apps do so that they can stay one step ahead in the competition. App stores provide large-scale useable data for achieving this goal while how to use them efficiently becomes a new challenge for developers. In this paper, we aim at helping developers make feature updating strategies of their apps by analyzing data of similar products in App stores. Firstly, we identify similar apps by using texts in app descriptions and UI. Then, we gain and integrate the information of updated features in these apps from their release texts. Furthermore, we match reviews with the related updated features, which helps developers predict the payback if they adopt a similar updating strategy. To validate the proposed approach, we conducted a series of experiments based on Google Play. The results show that our approach can analyze the data reasonably and provide useful information for developers making feature updating strategy in the evolution of their own products.
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