invited-talk

App store mining and analysis

Authors:

Afnan Al-Subaihin,

Anthony Finkelstein,

William Martin,

Federica Sarro,

Yuanyuan ZhangAuthors Info & Claims

DeMobile 2015: Proceedings of the 3rd International Workshop on Software Development Lifecycle for Mobile

Pages 1 - 2

https://doi.org/10.1145/2804345.2804346

Published: 31 August 2015 Publication History

Abstract

App stores are not merely disrupting traditional software deployment practice, but also offer considerable potential benefit to scientific research. Software engineering researchers have never had available, a more rich, wide and varied source of information about software products. There is some source code availability, supporting scientific investigation as it does with more traditional open source systems. However, what is important and different about app stores, is the other data available. Researchers can access user perceptions, expressed in rating and review data. Information is also available on app popularity (typically expressed as the number or rank of downloads). For more traditional applications, this data would simply be too commercially sensitive for public release. Pricing information is also partially available, though at the time of writing, this is sadly submerging beneath a more opaque layer of in-app purchasing. This talk will review research trends in the nascent field of App Store Analysis, presenting results from the UCL app Analysis Group (UCLappA) and others, and will give some directions for future work.

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Cited By

Pandey MLitoriya RPandey P(2023)An integrated MCDM approach for mobile app cost predictor based on DEMATEL extended with choquet integralMultimedia Tools and Applications10.1007/s11042-023-16856-y83:12(34943-34962)Online publication date: 28-Sep-2023
https://doi.org/10.1007/s11042-023-16856-y
Ranjan SMishra S(2021)Perceiving university students' opinions from Google app reviewsConcurrency and Computation: Practice and Experience10.1002/cpe.680034:10Online publication date: 30-Dec-2021
https://doi.org/10.1002/cpe.6800
Karim AAzhari ABelhaouri SQureshi AAhmad M(2020)Methodology for Analyzing the Traditional Algorithms Performance of User Reviews Using Machine Learning TechniquesAlgorithms10.3390/a1308020213:8(202)Online publication date: 18-Aug-2020
https://doi.org/10.3390/a13080202
Show More Cited By

Index Terms

App store mining and analysis
1. General and reference
  1. Cross-computing tools and techniques
    1. Metrics
2. Software and its engineering
  1. Software creation and management
    1. Designing software
      1. Requirements analysis

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Published In

cover image ACM Conferences

DeMobile 2015: Proceedings of the 3rd International Workshop on Software Development Lifecycle for Mobile

August 2015

36 pages

ISBN:9781450338158

DOI:10.1145/2804345

Copyright © 2015 Owner/Author.

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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SIGSOFT: ACM Special Interest Group on Software Engineering

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 31 August 2015

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ESEC/FSE'15

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SIGSOFT

ESEC/FSE'15: Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering

August 31, 2015

Bergamo, Italy

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Cited By

Pandey MLitoriya RPandey P(2023)An integrated MCDM approach for mobile app cost predictor based on DEMATEL extended with choquet integralMultimedia Tools and Applications10.1007/s11042-023-16856-y83:12(34943-34962)Online publication date: 28-Sep-2023
https://doi.org/10.1007/s11042-023-16856-y
Ranjan SMishra S(2021)Perceiving university students' opinions from Google app reviewsConcurrency and Computation: Practice and Experience10.1002/cpe.680034:10Online publication date: 30-Dec-2021
https://doi.org/10.1002/cpe.6800
Karim AAzhari ABelhaouri SQureshi AAhmad M(2020)Methodology for Analyzing the Traditional Algorithms Performance of User Reviews Using Machine Learning TechniquesAlgorithms10.3390/a1308020213:8(202)Online publication date: 18-Aug-2020
https://doi.org/10.3390/a13080202
Ranjan SMishra S(2020)Comparative Sentiment Analysis of App Reviews2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT49239.2020.9225348(1-7)Online publication date: Jul-2020
https://doi.org/10.1109/ICCCNT49239.2020.9225348
Botacin MBertão Gde Geus PGrégio AKruegel CVigna G(2020)On the Security of Application Installers and Online Software RepositoriesDetection of Intrusions and Malware, and Vulnerability Assessment10.1007/978-3-030-52683-2_10(192-214)Online publication date: 7-Jul-2020
https://doi.org/10.1007/978-3-030-52683-2_10
Harty J(2019)Google Play Console: Insightful Development Using Android Vitals and Pre-Launch Reports2019 IEEE/ACM 6th International Conference on Mobile Software Engineering and Systems (MOBILESoft)10.1109/MOBILESoft.2019.00019(62-65)Online publication date: May-2019
https://doi.org/10.1109/MOBILESoft.2019.00019
Al-Subaihin ASarro FBlack SCapra L(2019)Empirical comparison of text-based mobile apps similarity measurement techniquesEmpirical Software Engineering10.1007/s10664-019-09726-524:6(3290-3315)Online publication date: 24-Jun-2019
https://doi.org/10.1007/s10664-019-09726-5
Albrecht UHasenfuß Gvon Jan U(2018)Description of Cardiological Apps From the German App Store: Semiautomated Retrospective App Store AnalysisJMIR mHealth and uHealth10.2196/117536:11(e11753)Online publication date: 20-Nov-2018
https://doi.org/10.2196/11753
Majchrzak TDageförde JErnsting JRieger CReischmann T(2017)How Cross-Platform Technology Can Facilitate Easier Creation of Business AppsApps Management and E-Commerce Transactions in Real-Time10.4018/978-1-5225-2449-6.ch005(104-140)Online publication date: 2017
https://doi.org/10.4018/978-1-5225-2449-6.ch005
Martin WSarro FJia YZhang YHarman M(2017)A Survey of App Store Analysis for Software EngineeringIEEE Transactions on Software Engineering10.1109/TSE.2016.263068943:9(817-847)Online publication date: 1-Sep-2017
https://doi.org/10.1109/TSE.2016.2630689
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