Developers have to to constantly improve their apps by fixing critical bugs and implementing the most desired features in order to gain shares in the continuously increasing and competitive market of mobile apps. A precious source of information to plan such activities is represented by reviews left by users on the app store. However, in order to exploit such information developers need to manually analyze such reviews. This is something not doable if, as frequently happens, the app receives hundreds of reviews per day. In this paper we introduce CLAP ( C rowd  L istener for rele A se  P lanning), a thorough solution to (i) categorize user reviews based on the information they carry out ( e.g. , bug reporting), (ii) cluster together related reviews ( e.g. , all reviews reporting the same bug), and (iii) automatically prioritize the clusters of reviews to be implemented when planning the subsequent app release. We evaluated all the steps behind CLAP, showing its high accuracy in categorizing and clustering reviews and the meaningfulness of the recommended prioritizations. Also, given the availability of CLAP as a working tool, we assessed its practical applicability in industrial environments.
['Lorenzo Villarroel', 'Gabriele Bavota', 'Barbara Russo', 'Rocco Oliveto', 'Massimiliano Di Penta']
Release planning of mobile apps based on user reviews