research-article

Release planning of mobile apps based on user reviews

Authors:

Lorenzo Villarroel,

Gabriele Bavota,

Rocco Oliveto, and

Massimiliano Di PentaAuthors Info & Claims

ICSE '16: Proceedings of the 38th International Conference on Software Engineering

May 2016

Pages 14 - 24

https://doi.org/10.1145/2884781.2884818

Published: 14 May 2016 Publication History

Abstract

Developers have to to constantly improve their apps by fixing critical bugs and implementing the most desired features in order to gain shares in the continuously increasing and competitive market of mobile apps. A precious source of information to plan such activities is represented by reviews left by users on the app store. However, in order to exploit such information developers need to manually analyze such reviews. This is something not doable if, as frequently happens, the app receives hundreds of reviews per day. In this paper we introduce CLAP (Crowd Listener for releAse Planning), a thorough solution to (i) categorize user reviews based on the information they carry out (e.g., bug reporting), (ii) cluster together related reviews (e.g., all reviews reporting the same bug), and (iii) automatically prioritize the clusters of reviews to be implemented when planning the subsequent app release. We evaluated all the steps behind CLAP, showing its high accuracy in categorizing and clustering reviews and the meaningfulness of the recommended prioritizations. Also, given the availability of CLAP as a working tool, we assessed its practical applicability in industrial environments.

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Cited By

Jiang ZLiu VErne M(2024)Examining the Usefulness of Customer Reviews for Mobile ApplicationsJournal of Database Management10.4018/JDM.34354335:1(1-23)Online publication date: 17-May-2024
https://doi.org/10.4018/JDM.343543
Abedini YHajihosseini MHeydarnoori ASpinellis DConstantinou EBacchelli A(2024)DATAR: A Dataset for Tracking App ReleasesProceedings of the 21st International Conference on Mining Software Repositories10.1145/3643991.3644892(384-388)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1145/3643991.3644892
Wang XZhang TTan YShang WLi Y(2024)How to effectively mine app reviews concerning software ecosystem? A survey of review characteristicsJournal of Systems and Software10.1016/j.jss.2024.112040213(112040)Online publication date: Jul-2024
https://doi.org/10.1016/j.jss.2024.112040
Show More Cited By

Index Terms

Release planning of mobile apps based on user reviews
1. Software and its engineering
  1. Software notations and tools
    1. Software maintenance tools

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Published In

cover image ACM Conferences

ICSE '16: Proceedings of the 38th International Conference on Software Engineering

May 2016

1235 pages

ISBN:9781450339001

DOI:10.1145/2884781

General Chair:
Laura Dillon
Michigan State University
,
Program Chairs:
Willem Visser
Stellenbosch University, South Africa
,
Laurie Williams
North Carolina State University

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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ACM: Association for Computing Machinery
SIGSOFT: ACM Special Interest Group on Software Engineering
IEEE-CS\TCSE: TC on Software Engineering
IEEE-CS\DATC: IEEE Computer Society

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 May 2016

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ICSE '16

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ACM
SIGSOFT
IEEE-CS\TCSE
IEEE-CS\DATC

ICSE '16: 38th International Conference on Software Engineering

May 14 - 22, 2016

Texas, Austin

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Overall Acceptance Rate 276 of 1,856 submissions, 15%

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Cited By

Jiang ZLiu VErne M(2024)Examining the Usefulness of Customer Reviews for Mobile ApplicationsJournal of Database Management10.4018/JDM.34354335:1(1-23)Online publication date: 17-May-2024
https://doi.org/10.4018/JDM.343543
Abedini YHajihosseini MHeydarnoori ASpinellis DConstantinou EBacchelli A(2024)DATAR: A Dataset for Tracking App ReleasesProceedings of the 21st International Conference on Mining Software Repositories10.1145/3643991.3644892(384-388)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1145/3643991.3644892
Wang XZhang TTan YShang WLi Y(2024)How to effectively mine app reviews concerning software ecosystem? A survey of review characteristicsJournal of Systems and Software10.1016/j.jss.2024.112040213(112040)Online publication date: Jul-2024
https://doi.org/10.1016/j.jss.2024.112040
Specht AObaidi MNagel LStess MKlünder J(2024)What is Needed to Apply Sentiment Analysis in Real Software Projects: A Feasibility Study in IndustryHuman-Centered Software Engineering10.1007/978-3-031-64576-1_6(105-129)Online publication date: 1-Jul-2024
https://doi.org/10.1007/978-3-031-64576-1_6
Chen LPei YWan MFei ZLiang TMa G(2024)Smart Issue Detection for Large-Scale Online Service Systems Using Multi-Channel DataFundamental Approaches to Software Engineering10.1007/978-3-031-57259-3_8(165-187)Online publication date: 6-Apr-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
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Chhabra MSharan BKumar M(2023)A fuzzy cognitive map of the quality of user experience determinants in mobile application designJournal of Intelligent & Fuzzy Systems10.3233/JIFS-22211144:2(2957-2979)Online publication date: 30-Jan-2023
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Ferrari AHuichapa TSpoletini PNovielli NFucci DGirardi D(2023)Using Voice and Biofeedback to Predict User Engagement during Product Feedback InterviewsACM Transactions on Software Engineering and Methodology10.1145/363571233:4(1-36)Online publication date: 6-Dec-2023
https://dl.acm.org/doi/10.1145/3635712
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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Tan YChen JShang WZhang TFang SLuo XChen ZQi S(2023)STRE: An Automated Approach to Suggesting App Developers When to Stop Reading ReviewsIEEE Transactions on Software Engineering10.1109/TSE.2023.328574349:8(4135-4151)Online publication date: Aug-2023
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