An automated black-box model discovery with systematic sampling on android mobile applications

Official URL: https://risc01.sabanciuniv.edu/record=b2486361 _(Table of contents)

Clients progressively depend on mobile applications for computational needs. With the popularity of Google Android and the rise of interest in Android devices, Android applications have been valuable and millions of mobile applications have increased the importance and demand of test processes in the complex systems. Since the applications had well-developed strong conditions that need to be tested, automation in the testing has played a significant role. Many types of researches have primarily focused on different model discovery strategies to be used for different purposes (e.g., test generation, bug detection). However, they were not used systematically for testing of mobile applications. We present a tool that provides an automated black-box model discovery by applying systematic sampling to build a model of an application dynamically for different uses. The approach includes two purposes: (1) discovering the model of an application by providing systematic sampling, and (2) predicting guard conditions of the discovered model. The results of our experiments have confirmed the ability of the approach to acquire higher code coverage and the accuracy of predicted guard conditions than existing approaches