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ARdoc: App Reviews Development Oriented Classifier by Sebastiano Panichella, Andrea Di Sorbo, Emitza Guzman, Corrado A. Visaggio, Gerardo Canfora, Harald Gall

pubblicato 11 set 2016, 10:46 da Gerardo Canfora   [ aggiornato in data 17 nov 2016, 15:02 ]
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, (2) Text Analysis and (3) Sentiment Analysis 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) demonstrates that ARdoc correctly classifies feedback useful for maintenance perspectives in user reviews with high precision (ranging between 84% and 89%), recall (ranging between 84% and 89%), and F-Measure (ranging between 84% and 89%). While evaluating our tool developers of our study confirmed the usefulness of ARdoc in extracting important maintenance tasks for their mobile applications.
A tool demo at 24th ACM SIGSOFT International Symposium on the Foundations of Software Engineering (FSE'16)

Download the final version:
ACM DL Author-ize serviceARdoc: app reviews development oriented classifier
Sebastiano Panichella, Andrea Di Sorbo, Emitza Guzman, Corrado A. Visaggio, Gerardo Canfora, Harald C. Gall
FSE 2016 Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering, 2016
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Gerardo Canfora,
11 set 2016, 10:51
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