Time-travel testing of Android apps

Android testing tools generate sequences of input events to exercise the state space of the app-under-test. Existing search-based techniques systematically evolve a population of event sequences so as to achieve certain objectives such as maximal code coverage. The hope is that the mutation of fit event sequences leads to the generation of even fitter sequences. However, the evolution of event sequences may be ineffective. Our key insight is that pertinent app states which contributed to the original sequence's fitness may not be reached by a mutated event sequence. The original path through the state space is truncated at the point of mutation. In this paper, we propose instead to evolve a population of states which can be captured upon discovery and resumed when needed. The hope is that generating events on a fit program state leads to the transition to even fitter states. For instance, we can quickly deprioritize testing the main screen state which is visited by most event sequences, and instead focus our limited resources on testing more interesting states that are otherwise difficult to reach. We call our approach time-travel testing because of this ability to travel back to any state that has been observed in the past. We implemented time-travel testing into TimeMachine, a time-travel enabled version of the successful, automated Android testing tool Monkey. In our experiments on a large number of open-and closed source Android apps, TimeMachine outperforms the state-of-theart search-based/model-based Android testing tools Sapienz and Stoat, both in terms of coverage achieved and crashes found.