Dependability Assessment of Android OS

IEEE Transactions on Reliability

The reliability of mobile devices is a challenge for vendors, since the mobile software stack has significantly grown in complexity. In this paper, we study how to assess the impact of faults on the quality of user experience in the Android mobile OS through fault injection. We first address the problem of identifying a realistic fault model for the Android OS, by providing to developers a set of lightweight and systematic guidelines for fault modeling. Then, we present an extensible fault injection tool (AndroFIT) to apply such fault model on actual, commercial Android devices. Finally, we present a large fault injection experimentation on three Android products from major vendors, and point out several reliability issues and opportunities for improving the Android OS.

2010

As smart phones grow in popularity, manufacturers are in a race to pack an increasingly rich set of features into these tiny devices. This brings additional complexity in the system software that has to fit within the constraints of the devices (chiefly memory, stable storage, and power consumption) and hence, new bugs are revealed. How this evolution of smartphones impacts their reliability is a question that has been largely unexplored till now. With the release of open source OSes for hand-held devices, such as, Android (open sourced in October 2008) and Symbian (open sourced in February 2010), we are now in a position to explore the above question. In this paper, we analyze the reported cases of failures of Android and Symbian based on bug reports posted by thirdparty developers and end users and documentation of bug fixes from Android developers. First, based on 628 developer reports, our study looks into the manifestation of failures in different modules of Android and their characteristics, such as, their transience in time. Next, we analyze similar properties of Symbian bugs based on 153 failure reports. Our study indicates that Development tools, Web browsers, and Multimedia applications are most error-prone in both these systems. We further analyze 233 bug fixes for Android and categorized the different types of code modifications required for the fixes. The analysis shows that 78% of errors required minor code changes, with the largest share of these coming from modifications to attribute values and conditions. Our final analysis focuses on the relation between customizability, code complexity, and reliability in Android and Symbian. We find that despite high cyclomatic complexity, the bug densities in Android and Symbian are surprisingly low. However, the support for customizability does impact the reliability of mobile OSes and there are cautionary tales for their further development.

37th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN'07), 2007

While the new generation of hand-held devices, e.g., smart phones, support a rich set of applications, growing complexity of the hardware and runtime environment makes the devices susceptible to accidental errors and malicious attacks. Despite these concerns, very few studies have looked into the dependability of mobile phones. This paper presents measurement-based failure characterization of mobile phones. The analysis starts with a high level failure characterization of mobile phones based on data from publicly available web forums, where users post information on their experiences in using hand-held devices. This initial analysis is then used to guide the development of a failure data logger for collecting failure-related information on SymbianOS-based smart phones. Failure data is collected from 25 phones (in Italy and USA) over the period of 14 months. Key findings indicate that: (i) the majority of kernel exceptions are due to memory access violation errors (56%) and heap management problems (18%), and (ii) on average users experience a failure (freeze or self shutdown) every 11 days. While the study provide valuable insight into the failure sensitivity of smart-phones, more data and further analysis are needed before generalizing the results.

Information science, 2018

Assessment of software reliability is inevitable in modern software production process. Many works aimed at better models for measurement and prediction of reliability of software products. Tens of approaches have been developed and evaluated so far. However, very few works focus on approaches to compare existing systems with respect to reliability. Despite a tremendous importance to practice (and software management area), a complete and sound comparison methodology does not exist. In this paper, we propose a methodology for software reliability comparison. The methodology extensively applies the GQM approach and software reliability growth models. The methodology has been thoroughly evaluated on a case of assessment and comparison of three open source mobile operating systems: Sailfish, Tizen and CyanogenMod.

2019

The use of mobile phones is widespread, and more and more time is spent on mobile phones. With the widespread use of mobile phones, the mobile applications available in app stores also increase. There is a growing reliance on mobile applications. The availability of these applications is made at a fast pace and seeking to make development cost-effective. The consequence of this is often the disregard for the quality of the final product. Ensuring the quality of the applications allows to ensure the satisfaction of the end customer and their loyalty. It can avoid serious financial and human consequences. To promote the quality of the software, it is necessary to test the software ensuring that it does what is expected, working properly with a high level of quality. Mutation testing is a technique for injecting faults into code implementation. Each of the faults produced represents a mutant. The execution of these tests makes it possible, in a reliable way, to guarantee the quality an...

Smartphones have become the most used electronic devices. They carried out most of the functionalities of desktops, allowing various useful applications that suit the users' needs. Therefore, instead of the operator, the user has become the number one controller of the device and its applications and thus its reliability becomes an emergent need. We aim to investigate and evaluate the efficacy of Software Reliability Growth Models (SRGMs) when applied to Smartphone application failure data and check whether they achieve the same success as in the desktop/laptop area. We selected three of the most used SRGMs and applied them to three different Smartphone applications. None of the selected models were able to account for the data satisfactorily. Their failure is traced back to the specific features of mobile applications compared to desktop applications. Thus, a suitable model for Smartphone applications is still needed to improve their reliability.

Journal of Software Engineering and Applications, 2020

Software reliability is an important quality attribute, and software reliability models are frequently used to measure and predict software maturity. The nature of mobile environments differs from that of PC and server environments due to many factors, such as the network, energy, battery, and compatibility. Evaluating and predicting mobile application reliability are real challenges because of the diversity of the mobile environments in which the applications are used, and the lack of publicly available defect data. In addition, bug reports are optionally submitted by end-users. In this paper, we propose assessing and predicting the reliability of a mobile application using known software reliability growth models (SRGMs). Four software reliability models are used to evaluate the reliability of an open-source mobile application through analyzing bug reports. Our experiment proves it is possible to use SRGMs with defect data acquired from bug reports to assess and predict the software reliability in mobile applications. The results of our work enable software developers and testers to assess and predict the reliability of mobile software applications.

2009

Most of the current software reliability analysis approaches are geared to traditional desktop software systems, which are relatively stable and static throughout their execution. In this paper, we present a framework targeted at mobile computing domain that addresses the uncertainties associated with the reliability analysis in this setting. Moreover, the framework's architecture-centric reliability estimates are leveraged to improve the runtime reliability of the system through dynamic architectural reconfiguration.

Smartphones are becoming necessary tools in the daily lives of millions of users who rely on these devices and their applications. There are thousands 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.

2016

Mobile devices are significantly complex, featurerich, and heavily customized, thus they are prone to software reliability and performance issues. This paper considers the problem of software aging in Android mobile OS, which causes the device to gradually degrade in responsiveness, and to eventually fail. We present a methodology to identify factors (such as workloads and device configurations) and resource utilization metrics that are correlated with software aging. Moreover, we performed an empirical analysis of recent Android devices, finding that software aging actually affects them. The analysis pointed out processes and components of the Android OS affected by software aging, and metrics useful as indicators of software aging to schedule software rejuvenation actions.