Fresh apps: an empirical study of frequently-updated mobile apps in the Google play store

Mobile app stores provide a unique platform for developers to rapidly deploy new updates of their apps. We studied the frequency of updates of 10,713 mobile apps (the top free 400 apps at the start of 2014 in each of the 30 categories in the Google Play store). We find that a small subset of these apps (98 apps representing ˜1 % of the studied apps) are updated at a very frequent rate — more than one update per week and 14 % of the studied apps are updated on a bi-weekly basis (or more frequently). We observed that 45 % of the frequently-updated apps do not provide the users with any information about the rationale for the new updates and updates exhibit a median growth in size of 6 %. This paper provides information regarding the update strategies employed by the top mobile apps. The results of our study show that 1) developers should not shy away from updating their apps very frequently, however the frequency varies across store categories. 2) Developers do not need to be too concerned about detailing the content of new updates. It appears that users are not too concerned about such information. 3) Users highly rank frequently-updated apps instead of being annoyed about the high update frequency.

[1]  Ahmed E. Hassan,et al.  Understanding reuse in the Android Market , 2012, 2012 20th IEEE International Conference on Program Comprehension (ICPC).

[2]  Ying Zou,et al.  Exploring the Development of Micro-apps: A Case Study on the BlackBerry and Android Platforms , 2011, 2011 IEEE 11th International Working Conference on Source Code Analysis and Manipulation.

[3]  Jerri L. Ledford,et al.  Google Analytics , 2006 .

[4]  David Schuff,et al.  What Makes a Helpful Review? A Study of Customer Reviews on Amazon.com , 2010 .

[5]  Des Greer,et al.  Quantitative studies in software release planning under risk and resource constraints , 2003, 2003 International Symposium on Empirical Software Engineering, 2003. ISESE 2003. Proceedings..

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

[8]  Kristina Winbladh,et al.  Analysis of user comments: An approach for software requirements evolution , 2013, 2013 35th International Conference on Software Engineering (ICSE).

[9]  Bernd Brügge,et al.  User involvement in software evolution practice: A case study , 2013, 2013 35th International Conference on Software Engineering (ICSE).

[10]  Prakash Ramaswamy,et al.  The effects of individual XP practices on software development effort , 2003, SOEN.

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

[12]  Tijs van der Storm,et al.  Continuous release and upgrade of component-based software , 2005, SCM '05.

[13]  Ahmed E. Hassan,et al.  Impact of Ad Libraries on Ratings of Android Mobile Apps , 2014, IEEE Software.

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

[15]  Gabriele Bavota,et al.  API change and fault proneness: a threat to the success of Android apps , 2013, ESEC/FSE 2013.

[16]  Steffen Dienst,et al.  On the Relationship between the Number of Ad Libraries in an Android App and its Rating Israel , 2014 .

[17]  Aniruddha S. Gokhale,et al.  Techniques and processes for improving the quality and performance of open-source software , 2006, Softw. Process. Improv. Pract..

[18]  Merijn de Jonge,et al.  Nix: A Safe and Policy-Free System for Software Deployment , 2004, LISA.

[19]  SchuffDavid,et al.  What makes a helpful online review? a study of customer reviews on amazon.com , 2010 .

[20]  Denise Phillips,et al.  Extreme Adoption Experiences of a B2B Start-up , 2002 .

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

[22]  Günther Ruhe,et al.  The art and science of software release planning , 2005, IEEE Software.

[23]  Hee-Woong Kim,et al.  AN EXPLORATORY STUDY ON THE DETERMINANTS OF SMARTPHONE APP PURCHASE , 2011 .

[24]  Christopher Potts,et al.  Learning Word Vectors for Sentiment Analysis , 2011, ACL.

[25]  Jez Humble,et al.  Continuous Delivery: Reliable Software Releases Through Build, Test, and Deployment Automation , 2010 .

[26]  Matthias Marschall,et al.  Transforming a Six Month Release Cycle to Continuous Flow , 2007, Agile 2007 (AGILE 2007).

[27]  Bo Pang,et al.  A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts , 2004, ACL.

[28]  Ahmed E. Hassan,et al.  Revisiting prior empirical findings for mobile apps: an empirical case study on the 15 most popular open-source Android apps , 2013, CASCON.

[29]  Bogdan Dit,et al.  An exploratory analysis of mobile development issues using stack overflow , 2013, 2013 10th Working Conference on Mining Software Repositories (MSR).

[30]  Tore Dybå,et al.  A systematic review of effect size in software engineering experiments , 2007, Inf. Softw. Technol..

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

[32]  Heinz D. Knoell,et al.  Applied Quality Assurance Methods under the Open Source Development Model , 2008, 2008 32nd Annual IEEE International Computer Software and Applications Conference.

[33]  Foutse Khomh,et al.  Do faster releases improve software quality? An empirical case study of Mozilla Firefox , 2012, 2012 9th IEEE Working Conference on Mining Software Repositories (MSR).