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Preferences, the setting options provided by Android, are an essential part of Android apps. Preferences allow users to change app features and behaviors dynamically, and therefore their impacts need to be considered when testing the apps. Unfortunately, few test cases explicitly specify the assignments of valid values to the preferences, or configurations , under which they should be executed, and few existing mobile testing tools take the impact of preferences into account or provide help to testers in identifying and setting up the configurations for running the tests. This paper presents the Prefest approach to effective testing of Android apps with preferences. Given an Android app and a set of test cases for the app, Prefest amplifies the test cases with a small number of configurations to exercise more behaviors and detect more bugs that are related to preferences. In an experimental evaluation conducted on real-world Android apps, amplified test cases produced by Prefest from automatically generated test cases covered significantly more code of the apps and detected 7 real bugs, and the tool’s test amplification time was at the same order of magnitude as the running time of the input test cases. Prefest ’s effectiveness and efficiency in amplifying programmer-written test cases was comparable with that in amplifying automatically generated test cases.

The lifecycle models of Android components such as Activities and Fragments predefine the possible orders in which the components' callback methods will be invoked during app executions. Correspondingly, resource utilization operations performed by Android components must comply with all possible lifecycles to ensure safe utilization of the resources in all circumstances, which, however, can be challenging to achieve. In response to the challenge, various techniques have been developed to detect resource utilization bugs that manifest themselves when components go through common lifecycles, but the fact that Android components may execute their callback methods in uncommon orders, leading to variant component lifecycles, has largely been overlooked by the existing techniques.In this paper, we first identify three variant lifecycles for Android Activities and Fragments and then develop a technique called VALA to automatically detect bugs in Android apps that are induced by the variant lifecycles and may cause resource utilization errors like resource leaks and data losses. In an experimental evaluation conducted on 35 Android apps, a supporting tool for the VALA technique automatically detected 8 resource utilization bugs. All the 8 bugs were manually confirmed to be real defects and 7 of them were reported for the first time.