Empirical Software Engineering

, Volume 22, Issue 1, pp 505–546

An empirical study of emergency updates for top android mobile apps


DOI: 10.1007/s10664-016-9435-7

Cite this article as:
Hassan, S., Shang, W. & Hassan, A.E. Empir Software Eng (2017) 22: 505. doi:10.1007/s10664-016-9435-7


The mobile app market continues to grow at a tremendous rate. The market provides a convenient and efficient distribution mechanism for updating apps. App developers continuously leverage such mechanism to update their apps at a rapid pace. The mechanism is ideal for publishing emergency updates (i.e., updates that are published soon after the previous update). In this paper, we study such emergency updates in the Google Play Store. Examining more than 44,000 updates of over 10,000 mobile apps in the Google Play Store, we identify 1,000 emergency updates. By studying the characteristics of such emergency updates, we find that the emergency updates often have a long lifetime (i.e., they are rarely followed by another emergency update). Updates preceding emergency updates often receive a higher ratio of negative reviews than the emergency updates. However, the release notes of emergency updates rarely indicate the rationale for such updates. Hence, we manually investigate the binary changes of several of these emergency updates. We find eight patterns of emergency updates. We categorize these eight patterns along two categories “Updates due to deployment issues” and “Updates due to source code changes”. We find that these identified patterns of emergency updates are often associated with simple mistakes, such as using a wrong resource folder (e.g., images or sounds) for an app. We manually examine each pattern and document its causes and impact on the user experience. App developers should carefully avoid these patterns in order to improve the user experience.


Android mobile apps Emergency updates Patterns SDK version Permissions Empirical study Software engineering 

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Software Analysis and Intelligence Lab (SAIL), School of ComputingQueen’s UniversityKingstonCanada
  2. 2.Department of Computer Science and Software EngineeringConcordia UniversityMontrealCanada

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