ParaAim: Testing Android Applications Parallel at Activity Granularity

Widely used commercial Android applications (apps) turn to be of complex GUIs and hundreds of activities. Testing this kind of apps is challenging. Existing automated testing tools cannot complete the testing of these complex apps in a short time. However, scaling these tools for parallelism to accelerate the testing is not straightforward. In this paper, we borrow the basic concepts from parallel computing, and introduce the parallel testing platform ParaAim. ParaAim partitions an app testing job into a set of tasks at the activity granularity. Starting from a specific entrance activity, ParaAim explores the UI-states of the app and each newly discovered activity spawns a new task. ParaAim dispatches the new task to an idle device and sets it up to the entrance by replaying an event sequence. In this manner, the independent parts of the app are explored simultaneously. We focus on the efficiency of this parallel GUI exploration schema. ParaAim assigns the tasks that have more possibility to find new activities with high priority for better performance. Event sequence minimization is also studied to reduce the time cost of replay, and we design widget fuzzy match technique to handle the state inconsistency issue. We evaluated ParaAim with 20 popular commercial apps on different settings of Android device cluster. The results show that ParaAim scales well and evidently increases the average speed of exploration to nearly 2 times with two devices, and 3 times with four devices, than a single device.