Google Play, Apple App Store and Windows Phone Store are well known distribution platforms where users can download mobile apps, rate them and write review comments about the apps they are using. Previous research studies demonstrated that these reviews contain important information to help developers improve their apps. However, analyzing reviews is challenging due to the large amount of reviews posted every day, the unstructured nature of reviews and its varying quality. In this demo we present ARdoc, a tool which combines three techniques: (1) Natural Language Parsing (NLP), (2) Text Analysis (TA) and (3) Sentiment Analysis (SA) to automatically classify useful feedback contained in
app reviews important for performing software maintenance and evolution tasks. Our quantitative and qualitative analysis (involving mobile professional developers) demonstrate that ARdoc correctly classi�es feedback useful for maintenance perspectives in user reviews with high precision (ranging between 84% and 89%), recall (ranging between 84% and 89%), and an F-Measure (ranging between 84% and 89%).
While evaluating our tool we also found that ARdoc substantially helps to extract important maintenance tasks for real world applications.
Demo URL: https://youtu.be/Baf18V6sN8E
Demo Web Page: http://www.ifi.uzh.ch/seal/people/panichella/tools/ARdoc.html