TY - GEN
T1 - How can i improve my app? Classifying user reviews for software maintenance and evolution
AU - Panichella, Sebastiano
AU - Di Sorbo, Andrea
AU - Guzman, Emitza
AU - Visaggio, Corrado A.
AU - Canfora, Gerardo
AU - Gall, Harald C.
PY - 2015/11/19
Y1 - 2015/11/19
N2 - App Stores, such as Google Play or the Apple Store, allow users to provide feedback on apps by posting review comments and giving star ratings. These platforms constitute a useful electronic mean in which application developers and users can productively exchange information about apps. Previous research showed that users feedback contains usage scenarios, bug reports and feature requests, that can help app developers to accomplish software maintenance and evolution tasks. However, in the case of the most popular apps, the large amount of received feedback, its unstructured nature and varying quality can make the identification of useful user feedback a very challenging task. In this paper we present a taxonomy to classify app reviews into categories relevant to software maintenance and evolution, as well as an approach that merges three techniques: (1) Natural Language Processing, (2) Text Analysis and (3) Sentiment Analysis to automatically classify app reviews into the proposed categories. We show that the combined use of these techniques allows to achieve better results (a precision of 75% and a recall of 74%) than results obtained using each technique individually (precision of 70% and a recall of 67%).
AB - App Stores, such as Google Play or the Apple Store, allow users to provide feedback on apps by posting review comments and giving star ratings. These platforms constitute a useful electronic mean in which application developers and users can productively exchange information about apps. Previous research showed that users feedback contains usage scenarios, bug reports and feature requests, that can help app developers to accomplish software maintenance and evolution tasks. However, in the case of the most popular apps, the large amount of received feedback, its unstructured nature and varying quality can make the identification of useful user feedback a very challenging task. In this paper we present a taxonomy to classify app reviews into categories relevant to software maintenance and evolution, as well as an approach that merges three techniques: (1) Natural Language Processing, (2) Text Analysis and (3) Sentiment Analysis to automatically classify app reviews into the proposed categories. We show that the combined use of these techniques allows to achieve better results (a precision of 75% and a recall of 74%) than results obtained using each technique individually (precision of 70% and a recall of 67%).
KW - Mobile Applications
KW - Natural Language Processing
KW - Sentiment Analysis
KW - Text classification
KW - User Reviews
UR - https://www.scopus.com/pages/publications/84961642176
UR - https://www.scopus.com/inward/citedby.url?scp=84961642176&partnerID=8YFLogxK
U2 - 10.1109/ICSM.2015.7332474
DO - 10.1109/ICSM.2015.7332474
M3 - Conference contribution
AN - SCOPUS:84961642176
T3 - 2015 IEEE 31st International Conference on Software Maintenance and Evolution, ICSME 2015 - Proceedings
SP - 281
EP - 290
BT - 2015 IEEE 31st International Conference on Software Maintenance and Evolution, ICSME 2015 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 31st IEEE International Conference on Software Maintenance and Evolution, ICSME 2015
Y2 - 29 September 2015 through 1 October 2015
ER -