Update Behavior in App Markets and Security Implications:
A Case Study in Google Play
Stefan Diewald, Luis Roalter
a University of Technology
Department of Computer Science,
Electrical and Space Engineering
Digital market places (e.g. Apple App Store, Google Play)
have become the dominant platforms for the distribution of
software for mobile phones. Thereby, developers can reach
millions of users. However, neither of these market places to-
day has mechanisms in place to enforce security critical up-
dates of distributed apps. This paper investigates this problem
by gaining insights on the correlation between published up-
dates and actual installations of those. Our ﬁndings show that
almost half of all users would use a vulnerable app version
even 7 days after the ﬁx has been published. We discuss our
results and give initial recommendations to app developers.
Mobile applications; digital market places; update behavior;
ACM Classiﬁcation Keywords
D.4.6. Operating Systems: Security and Protection
INTRODUCTION AND MOTIVATION
Platform-speciﬁc marketplaces, such as the Apple App Store
or Google Play (formerly Android Market), are nowadays an
important source for mobile app distribution . In March
2012, Apple reached in total 25 billion iOS app downloads1.
Until 2011, 10 billion Android apps have been downloaded in
total over Google Play2. Smartphone users ﬁnd their applica-
tions bundled at one place and are informed about available
updates (via a badge symbol on the App Store icon on iOS,
or a message in the notiﬁcation bar on Android). However,
neither on iOS or Android, application updates are installed
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MobileHCI’12, September 21–24, 2012, San Francisco, CA, USA.
automatically. Android has a setting for installing updates
without conﬁrmation, but it is disabled by default.
This update mechanism implementation can be seen as a po-
tential risk for security. Unﬁxed security holes increase the
vulnerability of a device. As users need to take charge of
keeping their system up to date themselves, important up-
dates might not be installed timely or at all. Especially for
research apps (e.g. [11, 10]) or at the beginning of an app’s
market lifetime, regular installation of updates is important.
Being in state of development, such apps often are less stable
and require more frequent ﬁxes. Until the end of 2011, more
than 20,000 new apps per month were published in Google
Play3, so that potentially a large number of apps is affected
by this phenomenon. Security ﬂaws become even more se-
vere for the novel and upcoming category of apps that inte-
grate with the home or automobile (so-called in-car apps, see
e.g. ), since in that case not only the app itself, but also the
connected property becomes insecure.
In a case study, we observed users’ update behavior of an An-
droid app we have placed in Google Play. We gained insights
on the correlation between published updates and their actual
installation and discuss the consequences and recommended
actions on the part of the developers.
While inclusion in the Apple App Store requires a review pro-
cess , Google Play is free of constraints for uploading apps.
However, apps are scanned for viruses and malware  and
in case of malicious content deleted. This is, however, just a
method to uncover software that obviously tries to do ‘evil’
things, but not to detect programming bugs or security holes.
Automatic analysis of security problems during the submis-
sion process to digital market places has been proposed us-
ing several approaches [6, 14]. Di Cerbo et al.  present
a methodology for mobile forensics analysis to detect ‘ma-
licious’ (or ‘malware’) applications. The methodology relies
on the comparison of the Android security permission of each
application with a set of reference models, for applications
that manage sensitive data. Thus, this research is focusing
more on protecting the user from malicious apps whereas our
paper focuses on capturing the (non-)compliances of users to
install ﬁxes of a trusted developer.
It has also been found that Android apps often require per-
missions that are actually unneeded. Extensions to Android’s
permission model have consequently been proposed which
focus particularly on improving the (initially quite coarse)
granularity of permissions [12, 15] or remove them in hind-
sight by inline reference monitoring4. Fewer rights inherently
also decrease the probability for security-relevant bugs.
Miluzzo et al.  looked at implications and challenges of
large-scale distribution of research apps through the Apple
App Store. They pointed out that insufﬁcient software ro-
bustness and poor usability may lead to a loss of conﬁdence
on the part of the users, but did not quantitatively examine this
phenomenon (such as the number of uninstalls due to dissat-
isfaction). AppTicker  is a project that allows monitoring
mobile app usage, (un)installation and more to gain informa-
tion about usage patterns on smartphones. To our knowledge,
the particular phenomenon of update behavior in app stores
has not been examined yet. Despite the security approaches
and measures we presented in this section, keeping the soft-
ware up to date remains the central requirement for a stable
and secure system.
For our case study, we are looking at VMI Mensa5, an An-
droid application developed by the research group of the au-
thors of this paper. VMI Mensa shows meals and prices of
cafeterias and canteens of university campuses in our city.
The application, targeted at students and university employ-
ees, has been available in Google Play since July 21, 2011
and meanwhile (as of July 2012) reached 2,294 downloads.
It has received 123 ratings (averagely rated with 4.8 out of
5 stars) and 40 user comments. Since its launch, the app
has continuously been extended in its functionality, e.g. by
a location-aware canteen ﬁnder, details on ingredients, acces-
sibility information (e.g. on elevators), and much more.
Update Installation Analysis
Since VMI Mensa was ﬁrst available in Google Play, we have
shipped 21 updates. For our analysis, we used the built-in
statistics tools of the Android Developer Console in Google
Play. They allow keeping track of the number of installa-
tions over time, monitor installed app versions and a lot more.
All data is anonymous and cannot be related with individual
users. As stated before, updates may install automatically or
manually by user conﬁrmation. We cannot track whether au-
tomatic update installation was enabled on users’ devices.
For our analysis, we looked at the latest ﬁve updates, pub-
lished at December 22, 2011, January 17, January 26, Febru-
ary 24 and April 02 (all 2012). The average time between
updates was 26 days, which we consider not as an unreason-
able effort for users to regularly install them. All updates
added new functionality to the app and/or ﬁxed small prob-
lems, but none were critical for security. For each update,
we observed how many users downloaded the update on the
initial day of publishing and in the 6 consecutive days. We
calculated the update installation ratio by relating the down-
load count to the total count of active device installations on
the respective days.
User Communication Analysis
In addition to the anonymous update installation statistics, we
considered available user communication in form of feedback
emails, comments and ratings in Google Play for our analysis.
We will bring in these ﬁndings in the discussion section.
In the following, we describe and visualize the quantitative
results of our case study.
Table 1 shows the installation percentages on the update pub-
lishing day (day 0) and the six consecutive days (day 1 to day
6), averaged over all ﬁve updates that were considered in this
study. The exact ratios are very similar for all updates, which
is implied by the low standard deviations (see last column of
the table). In average, 17.0% installed the update on day 0.
On the following days, the numbers continuously and expo-
nentially decrease: 14.6% installed the update on day 1, only
7.8% on day 2, and 5.1% on day 3. On day 6, only another
2.3% downloaded the update.
Day after Update Update Installed Standard Deviation
Publishing Day 17.0% 2.7%
Day 1 14.6% 2.0%
Day 2 7.8% 1.3%
Day 3 5.1% 0.9%
Day 4 3.5% 0.7%
Day 5 2.8% 0.5%
Day 6 2.3% 0.4%
Total in 7 days 53.2% 2.7%
Table 1. Percentage of all users who installed an update within 7 days
after it was published. Only slightly more than half of all users installed
a recent update within one week. Data was averaged based on ﬁve subse-
quent updates published within 102 days. Standard deviation is related
to the ﬁve individual updates we observed in our use case.
This trend is visualized in Fig. 1 and can be summarized as
follows: Most of those users who actually do install updates
install them quickly. We hypothesize that the relatively high
ratios of the ﬁrst two days might partly be due to the auto-
matic update option. Users that did not install the update early
are also not likely to do so in the subsequent days. In total,
just 53.2%, slightly more than a half, had the most recent up-
date installed one week after publication.
We also looked at the distribution of the latest ﬁve versions
of the app on users’ devices, illustrated by different colors
in Fig. 2. The seven-day periods after an update has been
published are slightly shaded for illustration. The visualiza-
tion shows the spread of new versions due to cumulative in-
stalls (visualized with a steep graph that ﬂattens out more and
more), and the decrease of older versions. It also becomes
Figure 1. Visualization representing the number of ﬁve subsequent update downloads (vertical axis) over time. The graph shows maxima on the update
publishing day (possibly also due to activated auto-updates) and exponentially decreases thereafter. Modiﬁed diagram based on Android Developer
Figure 2. Visualization representing the number of installations by version (vertical axis); the colored lines indicate the ﬁve latest versions. The diagram
reveals how long old versions are active on user’s devices. The 7-day periods after an update has been published are highlighted. Modiﬁed diagram
based on Android Developer Console statistics.
evident how long outdated versions (up to four versions older
than the latest one) are still circulating. As an example, we
look at April 28, 2012, which is two weeks after the latest
update has been published: Only 56.4% of all users have in-
stalled the latest version (v.27) at this time. The previous four
versions were still in use by 8.5% (v.26), 6.0% (v.25), 5.5%
(v.24) and 2.1% (v.23). Most severely, 21.5% had even older
versions installed on their devices at that time.
Results from our case study reveal a problematic update be-
havior: Even one week after their publication, updates were
installed only by about 50% of users. The rest used differ-
ent outdated versions; one ﬁfth even did not install even one
of the last ﬁve updates. This implies two potential groups of
users: those who update in an exemplary manner, and those
who barely update at all. Hence, developers must not make
the mistake to rely on the belief that at least the penultimate
version of their app would run on most devices.
If we project this result to general update behavior, our ﬁnd-
ings imply a critical security situation. The harmless feature
updates in our case study could be important security-related
ﬁxes in another app. On average, almost half of all users
would use a vulnerable app version even 7 days after the ﬁx
has been published. The time from detection of a security
hole to the ﬁnal update shipment is not even considered here.
Further reasons indicate that the ‘real’ update situation could
even be worse than in our exemplary case analysis. A high
number of installed apps could further decrease the amount
of up-to-date apps, since more time would be required for in-
dividual updates. Furthermore, the fact that users are presum-
ably highly engaged with our examined canteen app could
have an impact on update frequency as well. We see an
even more critical situation with apps that are not regularly
used, but for which security is crucial just then (e.g. for on-
line banking apps). In-depth usage monitoring  is required
for better understanding the relation between usage frequency
and update behavior.
We also looked at users’ behavior in case of problems. Our
app contained a ‘Give feedback’ item in the preferences menu
that allowed sending an email to the developers. In the app
description in Google Play, we asked users to give us feed-
back using this function. We also linked to a Q&A page from
which users could contact the developers as well. Our ex-
perience revealed that few users actually used these oppor-
tunities. They rather made use of the rating functionality in
Google Play. For example, the download of the daily menu
was not working for one day due to a server migration. Sev-
eral users immediately left a bad rating in Google Play, com-
plaining about the app not working any more. Apparently,
they had not read the requests to provide feedback per mail
or not found the feedback link in the app. A similar case il-
lustrates as well that not all users read the description texts in
Google Play: One user commented that it would be good to
have an English translation. In fact, the app is fully localized
to 6 languages (amongst them English), and localizations au-
tomatically adapt to the device’s system language. Similarly,
this user rated the app worse because of this complaint.
For developers, our observations have three consequences.
First, they show how quick users are with bad ratings, which
may be problematic especially for commercial apps – other
work already stated that user reviews can be brutal .
Hence, it is important to keep the application bug-free and
provide timely updates in case of problems.
Second, developers cannot rely on users reading instructions
and employing the built-in feedback functions. We gained the
insight that ways to further improve such functions should be
found, and we also learned that keeping track of ratings and
comments in Google Play is important. Otherwise, in some
cases, we would not have been aware of potential problems.
In our case, they were related to usability and minor issues,
but they could have been security bugs as well. This is espe-
cially important since security holes not necessarily go along
with unresponsive or crashing apps and thus are not covered
by the built-in error reporting function of Google Play.
Third, as a ﬁrst step towards an improved security on mobile
phone platforms and in light of sometimes difﬁcult download
mechanisms , we encourage developers to support users in
updating, e.g. by built-in update checks within their applica-
tion and/or forwarding users to the platform market place, as
we use it in our research apps .
In this paper, we have analyzed update behavior and secu-
rity implications in application markets at the example of an
Android application we developed and offer for download in
Google Play. We found that, in average, half of all users did
not install an update even seven days after it has been pub-
lished and thus would use a potentially vulnerable applica-
tion. Although generalizations of our initial ﬁndings must be
carried out carefully and further studies will be necessary, we
raised the awareness for a potential slow update propagation
on Android and other mobile platforms.
Further automatic quality assessments for uploaded apps in
digital market places and more automated update mecha-
nisms could be ways to increase the level of security on mo-
We thank all of our students involved in the development of
VMI Mensa and our other research apps.
1. Apple Inc. App store review guidelines. https:
ohmer, M., Hecht, B., Sch¨
oning, J., Kr¨
uger, A., and
Bauer, G. Falling asleep with angry birds, facebook and
kindle: a large scale study on mobile application usage.
In Proceedings of the 13th International Conference on
Human Computer Interaction with Mobile Devices and
Services, ACM (2011), 47–56.
3. Cramer, H., Rost, M., Belloni, N., Bentley, F., and
Chincholle, D. Research in the large. using app stores,
markets, and other wide distribution channels in
ubicomp research. In Proceedings of the 12th ACM
international conference adjunct papers on Ubiquitous
computing - Adjunct, Ubicomp ’10 Adjunct, ACM (New
York, NY, USA, 2010), 511–514.
4. Di Cerbo, F., Girardello, A., Michahelles, F., and
Voronkova, S. Detection of malicious applications on
android OS. In Proceedings of the 4th international
conference on Computational forensics (IWCF’10),
H. Sako, K. Y. Franke, and S. Saitoh, Eds., Springer
(Berlin, Heidelberg, 2010), 138–149.
5. Diewald, S., M¨
oller, A., Roalter, L., and Kranz, M.
Mobile Device Integration and Interaction in the
Automotive Domain. In AutoNUI: Automotive Natural
User Interfaces Workshop at the 3rd International
Conference on Automotive User Interfaces and
Interactive Vehicular Applications (AutomotiveUI 2011)
6. Gilbert, P., Chun, B.-G., Cox, L. P., and Jung, J. Vision:
automated security validation of mobile apps at app
markets. In Proceedings of the second international
workshop on Mobile cloud computing and services,
MCS ’11, ACM (New York, NY, USA, 2011), 21–26.
7. Henze, N., and Sahami, A. Appticker.
8. Lockheimer, H. Google Mobile Blog. Android and
02/android-and- security.html, February 2012.
9. Miluzzo, E., Lane, N., Lu, H., and Campbell, A.
Research in the app store era: Experiences from the
CenceMe app deployment on the iPhone. In Proc.
oller, A., Roalter, L., Diewald, S., Scherr, J., Kranz,
M., Hammerla, N., Olivier, P., and Pl¨
otz, T. Gymskill: A
personal trainer for physical exercises. In Pervasive
Computing and Communications (PerCom), 2012 IEEE
International Conference on (march 2012), 213 –220.
oller, A., Thielsch, A., Dallmeier, B., Roalter, L.,
Diewald, S., Hendrich, A., Meyer, B. E., and Kranz, M.
Mobidics – improving university education with a
mobile didactics toolbox. In Ninth International
Conference on Pervasive Computing (Pervasive 2011),
Video Proceedings (San Francisco, CA, USA, June
12. Nauman, M., Khan, S., and Zhang, X. Apex: Extending
android permission model and enforcement with
user-deﬁned runtime constraints. In Proceedings of the
5th ACM Symposium on Information, Computer and
Communications Security, ACM (2010), 328–332.
13. Research, and Markets. Application Distribution
Channels 2011. Evans Data Corp., Sep. 2011.
14. Shabtai, A., Kanonov, U., Elovici, Y., Glezer, C., and
Weiss, Y. “Andromaly”: a behavioral malware detection
framework for android devices. Journal of Intelligent
Information Systems 38 (2012), 161–190.
15. Vidas, T., Christin, N., and Cranor, L. Curbing android
permission creep. In Proceedings of the Web, vol. 2