Electrical and Computer Engineering Publications

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Lecture Notes in Networks and Systems


LNNS 232


5th Computational Methods in Systems and Software (CoMeSySo 2021)

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Smartphones have become the most used electronic devices. They carry out most of the functionalities of desktops, offering various useful applications that suit the user’s 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.

Citation of this paper:

  1. Meskini S., Nassif A.B. and Capretz L.F., Reliability Models for Smartphone Applications, 5th Computational Methods in Systems and Software (CoMeSySo 2021), Lecture Notes in Networks and Systems (LNNS 232), pp. 1-25, DOI: https://doi.org/10.1007/978-3-030-90318-3_31, Springer Nature Switzerland AG, October 2021.

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