TY - GEN
T1 - ARdoc
T2 - 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering, FSE 2016
AU - Panichella, Sebastiano
AU - Di Sorbo, Andrea
AU - Guzman, Emitza
AU - Visaggio, Corrado A.
AU - Canfora, Gerardo
AU - Gall, Harald
PY - 2016/11/1
Y1 - 2016/11/1
N2 - 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 classiffes 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.
AB - 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 classiffes 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.
KW - Mobile Applications
KW - Natural Language Processing
KW - Sentiment Analysis
KW - Text Classification
KW - User Reviews
UR - http://www.scopus.com/inward/record.url?scp=84997501279&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84997501279&partnerID=8YFLogxK
U2 - 10.1145/2950290.2983938
DO - 10.1145/2950290.2983938
M3 - Conference contribution
AN - SCOPUS:84997501279
T3 - Proceedings of the ACM SIGSOFT Symposium on the Foundations of Software Engineering
SP - 1023
EP - 1027
BT - FSE 2016 - Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering
A2 - Su, Zhendong
A2 - Zimmermann, Thomas
A2 - Cleland-Huang, Jane
PB - Association for Computing Machinery
Y2 - 13 November 2016 through 18 November 2016
ER -