research-article

Release planning of mobile apps based on user reviews

Authors:

Lorenzo Villarroel,

Gabriele Bavota,

Massimiliano Di PentaAuthors Info & Claims

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

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

Wang ZLiu WChen JChen T(2025)RPerf: Mining user reviews using topic modeling to assist performance testing: An industrial experience reportJournal of Systems and Software10.1016/j.jss.2024.112283222(112283)Online publication date: Apr-2025
https://doi.org/10.1016/j.jss.2024.112283
Prasetyo MPeranginangin RMartinovic NIchsan MWicaksono H(2025)Artificial intelligence in open innovation project management: A systematic literature review on technologies, applications, and integration requirementsJournal of Open Innovation: Technology, Market, and Complexity10.1016/j.joitmc.2024.10044511:1(100445)Online publication date: Mar-2025
https://doi.org/10.1016/j.joitmc.2024.100445
Wang YWang JZhang HMing XWang Q(2025)Better together: Automated app review analysis with deep multi-task learningInformation and Software Technology10.1016/j.infsof.2024.107597177(107597)Online publication date: Jan-2025
https://doi.org/10.1016/j.infsof.2024.107597
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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
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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

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https://doi.org/10.1016/j.jss.2024.112283
Prasetyo MPeranginangin RMartinovic NIchsan MWicaksono H(2025)Artificial intelligence in open innovation project management: A systematic literature review on technologies, applications, and integration requirementsJournal of Open Innovation: Technology, Market, and Complexity10.1016/j.joitmc.2024.10044511:1(100445)Online publication date: Mar-2025
https://doi.org/10.1016/j.joitmc.2024.100445
Wang YWang JZhang HMing XWang Q(2025)Better together: Automated app review analysis with deep multi-task learningInformation and Software Technology10.1016/j.infsof.2024.107597177(107597)Online publication date: Jan-2025
https://doi.org/10.1016/j.infsof.2024.107597
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
de Lima VMarcacini R(2024)Opinion Mining for App Reviews: Identifying and Prioritizing Emerging Issues for Software Maintenance and EvolutionProceedings of the XXIII Brazilian Symposium on Software Quality10.1145/3701625.3701700(687-696)Online publication date: 5-Nov-2024
https://dl.acm.org/doi/10.1145/3701625.3701700
Sorathiya AGinde GFilkov VRay BZhou M(2024)Towards Extracting Ethical Concerns-related Software Requirements from App ReviewsProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695294(2251-2255)Online publication date: 27-Oct-2024
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Abedini YHeydarnoori A(2024)Can GitHub Issues Help in App Review Classifications?ACM Transactions on Software Engineering and Methodology10.1145/367817033:8(1-42)Online publication date: 18-Jul-2024
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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
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
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Biswas MAnish PGhaisas S(2024)Interpretable App Review Classification with Transformers2024 IEEE 32nd International Requirements Engineering Conference Workshops (REW)10.1109/REW61692.2024.00009(26-34)Online publication date: 24-Jun-2024
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