Proliferation of Android devices and apps has created a demand for applicable automated software testing techniques. Prior research has primarily focused on either unit or GUI testing of Android apps, but not their end-to-end system testing in a systematic manner. We present EvoDroid, an evolutionary approach for system testing of Android apps. EvoDroid overcomes a key shortcoming of using evolutionary techniques for system testing, i.e., the inability to pass on genetic makeup of good individuals in the search. To that end, EvoDroid combines two novel techniques: (1) an Android-specific program analysis technique that identifies the segments of the code amenable to be searched independently, and (2) an evolutionary algorithm that given information of such segments performs a stepwise search for test cases reaching deep into the code. Our experiments have corroborated EvoDroid's ability to achieve significantly higher code coverage than existing Android testing tools.

There is a growing need for automated testing techniques aimed at Android apps. A critical challenge is the systematic generation of test cases. One method of systematically generating test cases for Java programs is symbolic execution. But applying symbolic execution tools, such as Symbolic Pathfinder (SPF), to generate test cases for Android apps is challenged by the fact that Android apps run on the Dalvik Virtual Machine (DVM) instead of JVM. In addition, Android apps are event driven and susceptible to path-divergence due to their reliance on an application development framework. This paper provides an overview of a two-pronged approach to alleviate these issues. First, we have developed a model of Android libraries in Java Pathfinder (JPF) to enable execution of Android apps in a way that addresses the issues of incompatibility with JVM and path-divergence. Second, we have leveraged program analysis techniques to correlate events with their handlers for automatically generating Android-specific drivers that simulate all valid events.

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