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.
翻译:智能手机已成为最常用的电子设备。 它们运行了台式计算机的大多数功能, 提供了适合用户需要的各种有用的应用程序。 因此, 用户不是操作员, 经常成为该设备及其应用程序的主要控制器, 因此其可靠性已成为一项紧急要求。 作为第一步, 根据所收集的智能手机应用程序故障数据, 我们调查并评估了软件可靠性增长模型(SRGMs)在应用到这些智能手机数据时的功效。 第二步, 我们应用了Weibull和Gamma的分布是否达到与桌面/笔记本区域相同的精确度。 所选模型没有一个能够令人满意地解算出智能手机数据。 它们的失败可追溯到:(i) 台式和智能手机应用程序之间的硬件和软件差异, 因此, 与桌面应用程序故障数据故障相比, 移动应用程序的具体特点和使用情况。 因此, 仍然需要一种适合智能手机应用程序的可靠性模型。 第二步, 我们应用Webull和Gamma的分布, 两个特定案例, Raylele和SShapedt, 它们的智能应用失败数据被追溯到:(i) 每一个应用版本的功能分配周期, 我们的预期版本和数据分布的每个版本的错误都被找到。