Can we rely on smartphone applications?

TL;DR

This paper evaluates the applicability of traditional reliability models to smartphone applications and finds that while common models fall short, Weibull and Gamma distributions effectively fit failure data, improving reliability prediction.

Contribution

It demonstrates the inadequacy of traditional models for mobile apps and proposes Weibull and Gamma distributions as better alternatives for failure data modeling.

Findings

Traditional reliability models are insufficient for smartphone apps.

Weibull and Gamma models fit failure data well.

Better reliability prediction methods are identified.

Abstract

Smartphones are becoming necessary tools in the daily lives of mil-lions of users who rely on these devices and their applications. There are thou-sands of applications for smartphone devices such as the iPhone, Blackberry, and Android, thus their reliability has become paramount for their users. This work aims to answer two related questions: (1) Can we assess the reliability of mobile applications by using the traditional reliability models? (2) Can we model adequately the failure data collected from many users? Firstly, it has been proved that the three most used software reliability models have fallen short of the mark when applied to smartphone applications; their failures were traced back to specific features of mobile applications. Secondly, it has been demonstrated that the Weibull and Gamma distribution models can adequately fit the observed failure data, thus providing better means to predict the reliability of smartphone applications.