Reliability Models for Smartphone Applications

TL;DR

This paper evaluates existing software reliability growth models on smartphone application failure data, finds they are inadequate, and explores alternative distributions like Weibull and Gamma to better model smartphone app failures.

Contribution

It demonstrates the limitations of traditional SRGMs for smartphones and proposes using Weibull and Gamma distributions for more accurate failure modeling.

Findings

SRGMs do not fit smartphone failure data well

Gamma distribution often outperforms Weibull in modeling failures

Both Weibull and Gamma can effectively fit mobile application failure data

Abstract

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.