Human-Machine Collaborative Testing for Android Applications

Android applications bring many challenges for testing due to the notorious fragmentation issues and their diverse usage environments. Even though classic crowdsourced testing can improve the usability and reliability of Android applications, it often requires many efforts and resources. Meanwhile, on the other hand, automated analysis techniques provide highly efficient testing solutions for Android applications, which can use rich test resources and computing power to save manual test costs. Therefore, to leverage the advantages of both manual and automated testing, in this paper, we propose an approach that combines classic static program analysis and crowdsourced testing to implement human-machine collaborative testing for Android applications. We first employ the static analysis technique to model the possible GUI window sequences into window transition graphs (WTG). Then, we use a depth-first search algorithm to traverse the WTG and generate the testing task lists. In the testing process, we recommend these tasks for testers and adjust the task priority based on user feedback to optimize collaborative testing efficiency and effectiveness. To validate our approach, we experiment it with 9 open-source Android applications. The experimental results show that the path coverage rate of human-machine collaborative testing is 18.2% higher than that of classic crowdsourced testing and can reduce duplicate bug reports by 17.0 percent and improve testing efficiency.