ARdoc: App Reviews Development Oriented Classifier

被引：81

作者：

Panichella, Sebastiano ^{[1
]}

Di Sorbo, Andrea ^{[2
]}

Guzman, Emitza ^{[1
]}

Visaggio, Corrado A. ^{[2
]}

Canfora, Gerardo ^{[2
]}

Gall, Harald ^{[1
]}

机构：

[1] Univ Zurich, Dept Informat, Zurich, Switzerland

[2] Univ Sannio, Dept Engn, Benevento, BN, Italy

来源：

FSE'16: PROCEEDINGS OF THE 2016 24TH ACM SIGSOFT INTERNATIONAL SYMPOSIUM ON FOUNDATIONS OF SOFTWARE ENGINEERING | 2016年

关键词：

User Reviews; Mobile Applications; Natural Language Processing; Sentiment Analysis; Text Classification;

D O I：

10.1145/2950290.2983938

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

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.

引用

页码：1023 / 1027

页数：5

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