  Smartphones have become the most used electronic devices. They carry out most
of the functionalities of desktops, offering various useful applications that
suit the users needs. Therefore, instead of the operator, the user has been the
main controller of the device and its applications, therefore its reliability
has become an emergent requirement. As a first step, based on collected
smartphone applications failure data, we investigated and evaluated the
efficacy of Software Reliability Growth Models (SRGMs) when applied to these
smartphone data in order to check whether they achieve the same accuracy as in
the desktop/laptop area. None of the selected models were able to account for
the smartphone data satisfactorily. Their failure is traced back to: (i) the
hardware and software differences between desktops and smartphones, (ii) the
specific features of mobile applications compared to desktop applications, and
(iii) the different operational conditions and usage profiles. Thus, a
reliability model suited to smartphone applications is still needed. In the
second step, we applied the Weibull and Gamma distributions, and their two
particular cases, Rayleigh and S-Shaped, to model the smartphone failure data
sorted by application version number and grouped into different time periods.
An estimation of the expected number of defects in each application version was
obtained. The performances of the distributions were then compared amongst each
other. We found that both Weibull and Gamma distributions can fit the failure
data of mobile applications, although the Gamma distribution is frequently more
suited.
