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ARdoc: app reviews development oriented classifier

Authors:

Sebastiano Panichella,

Andrea Di Sorbo,

Corrado A. Visaggio,

Gerardo Canfora,

Harald C. GallAuthors Info & Claims

FSE 2016: Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering

Pages 1023 - 1027

https://doi.org/10.1145/2950290.2983938

Published: 01 November 2016 Publication History

Abstract

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.

Demo URL: https://youtu.be/Baf18V6sN8E

Demo Web Page: http://www.ifi.uzh.ch/seal/people/panichella/tools/ARdoc.html

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Cited By

Liu YZhang YLiu BDuan HLi QLiu MLi RYao JBalzarotti DXu W(2024)Tickets or privacy? understand the ecosystem of chinese ticket grabbing appsProceedings of the 33rd USENIX Conference on Security Symposium10.5555/3698900.3699186(5107-5124)Online publication date: 14-Aug-2024
https://dl.acm.org/doi/10.5555/3698900.3699186
Fischer RTabbassum SGuzmán E(2024)Do User Backgrounds Matter? Impact of Gender and Countries on App Reviews and Tweets2024 IEEE 32nd International Requirements Engineering Conference Workshops (REW)10.1109/REW61692.2024.00022(119-127)Online publication date: 24-Jun-2024
https://doi.org/10.1109/REW61692.2024.00022
Huang YWang JWang SZhang RWang Q(2024)Which API is Faster: Mining Fine-grained Performance Opinion from Online Discussions2024 IEEE 24th International Conference on Software Quality, Reliability and Security (QRS)10.1109/QRS62785.2024.00066(608-619)Online publication date: 1-Jul-2024
https://doi.org/10.1109/QRS62785.2024.00066
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Index Terms

ARdoc: app reviews development oriented classifier
1. Software and its engineering
  1. Software notations and tools
    1. Software maintenance tools

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Information & Contributors

Information

Published In

cover image ACM Conferences

FSE 2016: Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering

November 2016

1156 pages

ISBN:9781450342186

DOI:10.1145/2950290

General Chair:
Thomas Zimmermann
Microsoft Research, USA
,
Program Chairs:
Jane Cleland-Huang
University of Notre Dame, USA
,
Zhendong Su
University of California at Davis, USA

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGSOFT: ACM Special Interest Group on Software Engineering

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 November 2016

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FSE'16

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SIGSOFT

FSE'16: 24nd ACM SIGSOFT International Symposium on the Foundations of Software Engineering

November 13 - 18, 2016

WA, Seattle, USA

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Overall Acceptance Rate 17 of 128 submissions, 13%

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Cited By

Liu YZhang YLiu BDuan HLi QLiu MLi RYao JBalzarotti DXu W(2024)Tickets or privacy? understand the ecosystem of chinese ticket grabbing appsProceedings of the 33rd USENIX Conference on Security Symposium10.5555/3698900.3699186(5107-5124)Online publication date: 14-Aug-2024
https://dl.acm.org/doi/10.5555/3698900.3699186
Fischer RTabbassum SGuzmán E(2024)Do User Backgrounds Matter? Impact of Gender and Countries on App Reviews and Tweets2024 IEEE 32nd International Requirements Engineering Conference Workshops (REW)10.1109/REW61692.2024.00022(119-127)Online publication date: 24-Jun-2024
https://doi.org/10.1109/REW61692.2024.00022
Huang YWang JWang SZhang RWang Q(2024)Which API is Faster: Mining Fine-grained Performance Opinion from Online Discussions2024 IEEE 24th International Conference on Software Quality, Reliability and Security (QRS)10.1109/QRS62785.2024.00066(608-619)Online publication date: 1-Jul-2024
https://doi.org/10.1109/QRS62785.2024.00066
Abdelaziz OCodabux ZSchneider K(2024)Review-Pulse: A Dashboard for Managing User Feedback for Android Applications2024 IEEE International Conference on Software Maintenance and Evolution (ICSME)10.1109/ICSME58944.2024.00095(878-882)Online publication date: 6-Oct-2024
https://doi.org/10.1109/ICSME58944.2024.00095
Khan NAli Khan JLi JUllah TAlwadain AYasin AZhao Q(2024)How Do Crowd-Users Express Their Opinions Against Software Applications in Social Media? A Fine-Grained Classification ApproachIEEE Access10.1109/ACCESS.2024.342583012(98004-98028)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3425830
Tjikhoeri NOlson LGuzmán E(2024)The best ends by the best means: ethical concerns in app reviewsEmpirical Software Engineering10.1007/s10664-024-10463-729:6Online publication date: 17-Aug-2024
https://doi.org/10.1007/s10664-024-10463-7
Nayebi MKuznetsov KZeller ARuhe G(2024)Recommending and release planning of user-driven functionality deletion for mobile appsRequirements Engineering10.1007/s00766-024-00430-529:4(459-480)Online publication date: 10-Sep-2024
https://doi.org/10.1007/s00766-024-00430-5
Tizard JRietz TBlincoe K(2024)Elicitation Revisited for More Inclusive Requirements EngineeringEquity, Diversity, and Inclusion in Software Engineering10.1007/978-1-4842-9651-6_6(91-104)Online publication date: 21-Sep-2024
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Yahya AGharbi AYafooz WAl-Dhaqm A(2023)A Novel Hybrid Deep Learning Model for Detecting and Classifying Non-Functional Requirements of Mobile Apps IssuesElectronics10.3390/electronics1205125812:5(1258)Online publication date: 6-Mar-2023
https://doi.org/10.3390/electronics12051258
Sun RXue MTyson GWang SCamtepe SNepal S(2023)Not Seen, Not Heard in the Digital World! Measuring Privacy Practices in Children’s AppsProceedings of the ACM Web Conference 202310.1145/3543507.3583327(2166-2177)Online publication date: 30-Apr-2023
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