App store mining and analysis

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.

[1]  Fan Wu,et al.  Genetic improvement for adaptive software engineering (keynote) , 2014, SEAMS 2014.

[2]  Ahmed E. Hassan,et al.  Studying the relationship between source code quality and mobile platform dependence , 2014, Software Quality Journal.

[3]  A. Hassan,et al.  What Do Mobile App Users Complain About ? A Study on Free iOS Apps , 2014 .

[4]  Rachel Harrison,et al.  Retrieving and analyzing mobile apps feature requests from online reviews , 2013, 2013 10th Working Conference on Mining Software Repositories (MSR).

[5]  Ying Zou,et al.  An Exploratory Study on the Relation between User Interface Complexity and the Perceived Quality , 2014, ICWE.

[6]  Walid Maalej,et al.  Bug report, feature request, or simply praise? On automatically classifying app reviews , 2015, 2015 IEEE 23rd International Requirements Engineering Conference (RE).

[7]  Rajesh Vasa,et al.  An Analysis of the Mobile App Review Landscape: Trends and Implications , 2013 .

[8]  John A. Clark,et al.  The GISMOE challenge: constructing the pareto program surface using genetic programming to find better programs (keynote paper) , 2012, 2012 Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering.

[9]  Yuanyuan Zhang,et al.  The App Sampling Problem for App Store Mining , 2015, 2015 IEEE/ACM 12th Working Conference on Mining Software Repositories.

[10]  Justyna Petke,et al.  Reducing Energy Consumption Using Genetic Improvement , 2015, GECCO.

[11]  Christos Faloutsos,et al.  Why people hate your app: making sense of user feedback in a mobile app store , 2013, KDD.

[12]  Mario Linares Vásquez,et al.  Supporting evolution and maintenance of Android apps , 2014, ICSE Companion.

[13]  Hammad Khalid On identifying user complaints of iOS apps , 2013, 2013 35th International Conference on Software Engineering (ICSE).

[14]  Walid Maalej,et al.  User feedback in the appstore: An empirical study , 2013, 2013 21st IEEE International Requirements Engineering Conference (RE).

[15]  Ahmed E. Hassan,et al.  What Do Mobile App Users Complain About? , 2015, IEEE Software.

[16]  Michele Lanza,et al.  Software Analytics for Mobile Applications--Insights & Lessons Learned , 2013, 2013 17th European Conference on Software Maintenance and Reengineering.

[17]  Mark Harman,et al.  Ieee Transactions on Evolutionary Computation 1 , 2022 .

[18]  Mario Linares Vásquez,et al.  Revisiting Android reuse studies in the context of code obfuscation and library usages , 2014, MSR 2014.

[19]  Walid Maalej,et al.  How Do Users Like This Feature? A Fine Grained Sentiment Analysis of App Reviews , 2014, 2014 IEEE 22nd International Requirements Engineering Conference (RE).

[20]  Tao Xie,et al.  WHYPER: Towards Automating Risk Assessment of Mobile Applications , 2013, USENIX Security Symposium.

[21]  Yuanyuan Zhang,et al.  Search based software engineering for software product line engineering: a survey and directions for future work , 2014, SPLC.

[22]  Yuanyuan Zhang,et al.  App store mining and analysis: MSR for app stores , 2012, 2012 9th IEEE Working Conference on Mining Software Repositories (MSR).

[23]  Tim Menzies Beyond data mining; towards "idea engineering" , 2013, PROMISE.

[24]  Peter J. Bentley,et al.  Investigating app store ranking algorithms using a simulation of mobile app ecosystems , 2013, 2013 IEEE Congress on Evolutionary Computation.

[25]  Alessandra Gorla,et al.  Checking app behavior against app descriptions , 2014, ICSE.

[26]  Yuanyuan Zhang,et al.  Feature lifecycles as they spread, migrate, remain, and die in App Stores , 2015, 2015 IEEE 23rd International Requirements Engineering Conference (RE).