Automated Test Input Generation for Android: Are We There Yet?

Mobile applications, often simply called "apps", are increasingly widespread, and we use them daily to perform a number of activities. Like all software, apps must be adequately tested to gain confidence that they behave correctly. Therefore, in recent years, researchers and practitioners alike have begun to investigate ways to automate apps testing. In particular, because of Android's open source nature and its large share of the market, a great deal of research has been performed on input generation techniques for apps that run on the Android operating systems. At this point in time, there are in fact a number of such techniques in the literature, which differ in the way they generate inputs, the strategy they use to explore the behavior of the app under test, and the specific heuristics they use. To better understand the strengths and weaknesses of these existing approaches, and get general insight on ways they could be made more effective, in this paper we perform a thorough comparison of the main existing test input generation tools for Android. In our comparison, we evaluate the effectiveness of these tools, and their corresponding techniques, according to four metrics: code coverage, ability to detect faults, ability to work on multiple platforms, and ease of use. Our results provide a clear picture of the state of the art in input generation for Android apps and identify future research directions that, if suitably investigated, could lead to more effective and efficient testing tools for Android

Dependability where the mobile world meets the enterprise world

As we move toward increasingly larger scales of computing, complexity of systems and networks has increased manifold leading to massive failures of cloud providers (Amazon Cloudfront, November 2014) and geographically localized outages of cellular services (T-Mobile, June 2014). In this dissertation, we investigate the dependability aspects of two of the most prevalent computing platforms today, namely, smartphones and cloud computing. These two seemingly disparate platforms are part of a cohesive story—they interact to provide end-to-end services which are increasingly being delivered over mobile platforms, examples being iCloud, Google Drive and their smartphone counterparts iPhone and Android. ^ In one of the early work on characterizing failures in dominant mobile OSes, we analyzed bug repositories of Android and Symbian and found similarities in their failure modes [ISSRE2010]. We also presented a classification of root causes and quantified the impact of ease of customizing the smartphones on system reliability. Our evaluation of Inter-Component Communication in Android [DSN2012] show an alarming number of exception handling errors where a phone may be crashed by passing it malformed component invocation messages, even from unprivileged applications. In this work, we also suggest language extensions that can mitigate these problems. ^ Mobile applications today are increasingly being used to interact with enterprise-class web services commonly hosted in virtualized environments. Virutalization suffers from the problem of imperfect performance isolation where contention for low-level hardware resources can impact application performance. Through a set of rigorous experiments in a private cloud testbed and in EC2, we show that interference induced performance degradation is a reality. Our experiments have also shown that optimal configuration settings for web servers change during such phases of interference. Based on this observation, we design and implement the IC 2engine which can mitigate effects of interference by reconfiguring web server parameters [MW2014]. We further improve IC 2 by incorporating it into a two-level configuration engine, named ICE, for managing web server clusters [ICAC2015]. Our evaluations show that, compared to an interference agnostic configuration, IC 2 can improve response time of web servers by upto 40%, while ICE can improve response time by up to 94% during phases of interference

Towards a threat assessment framework for apps collusion

App collusion refers to two or more apps working together to achieve a malicious goal that they otherwise would not be able to achieve individually. The permissions based security model of Android does not address this threat as it is rather limited to mitigating risks of individual apps. This paper presents a technique for quantifying the collusion threat, essentially the first step towards assessing the collusion risk. The proposed method is useful in finding the collusion candidate of interest which is critical given the high volume of Android apps available. We present our empirical analysis using a classified corpus of over 29,000 Android apps provided by Intel SecurityTM

AppGuard — fine-grained policy enforcement for untrusted android applications

Android’s success makes it a prominent target for malicious software. However, the user has very limited control over security-relevant operations. This work presents AppGuard, a powerful and flexible security system that overcomes these deficiencies. It enforces user-defined security policies on untrusted Android applications without requiring any changes to a smartphone’s firmware, root access, or the like. Finegrained and stateful security policies are expressed in a formal specification language, which also supports secrecy requirements. Our system offers complete mediation of security-relevant methods based on calleesite inline reference monitoring and supports widespread deployment. In the experimental analysis we demonstrate the removal of permissions for overly curious apps as well as how to defend against several recent real-world attacks on Android phones. Our technique exhibits very little space and runtime overhead. The utility of AppGuard has already been demonstrated by more than 1,000,000 downloads

Towards a threat assessment framework for apps collusion

App collusion refers to two or more apps working together to achieve a malicious goal that they otherwise would not be able to achieve individually. The permissions based security model of Android does not address this threat as it is rather limited to mitigating risks of individual apps. This paper presents a technique for quantifying the collusion threat, essentially the first step towards assessing the collusion risk. The proposed method is useful in finding the collusion candidate of interest which is critical given the high volume of Android apps available. We present our empirical analysis using a classified corpus of over 29,000 Android apps provided by Intel SecurityTM