App store mining and analysis (keynote)

© 2015 ACM. 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]  Justyna Petke,et al.  Reducing Energy Consumption Using Genetic Improvement , 2015, GECCO.

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

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

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

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

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

[7]  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).

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

[9]  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.

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

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

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

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

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

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

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

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

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

[19]  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).

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

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

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

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

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

[25]  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).