We are happy to announce that the paper “ARdoc: App Reviews Development Oriented Classifier” got accepted at the FSE 2016 Demonstrations Track! The authors of the paper are: Sebastiano Panichella, Andrea Di Sorbo, Emitza Guzman, Corrado Aaron Visaggio, Gerardo Canfora and Harald Gall.
The paper presents ARdoc (App Reviews Development Oriented Classifier) a Java tool that automatically recognizes natural language fragments in user reviews that are relevant for developers to evolve their applications. Specifically, natural language fragments are extracted according to a taxonomy of app reviews categories that are relevant to software maintenance and evolution. The categories were defined in our previous paper entitled “How Can I Improve My App? Classifying User Reviews for Software Maintenance and Evolution” ) and are: (i) Information Giving, (ii) Information Seeking, (iii) Feature Request and (iv) Problem Discovery. ARdoc implements an approach that merges three techniques: (1) Natural Language Processing, (2) Text Analysis 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 classifies 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.
This video provides a short demonstration of ARdoc:
ARdoc is available for download at http://www.ifi.uzh.ch/en/seal/people/panichella/tools/ARdoc.html