Abstract: Recently,the research of Android graphical interface test method has attracted wide attention.At present,most test methods are developed based on reinforcement learning.However,the existing methods can explore the application by selecting parameters according to experience,and can not to change the parameter settings according to the interface changes adaptively.This paper proposes an adaptive Android testing method based on Thompson sampling,which combines Thompson sampling with the Q-learning algorithm,enabling the agent to adaptively determine the next exploration action based on the current state of the interface controls being examined so that to balance exploitation and exploration more effectively for superior testing outcomes.Firstly,events of the jump at the interface during exploration are modeled for the Beta probability distribution,then a probability distribution matrix is obtained,which is weighted-averaged with the Q-table.It can take into account both the exploration value and the exploition value of the events.At the same time,the probability distribution of operational events under the current interface is sampled,and the maximum sampling value is the exploration probability value,combined with the weighted matrix,it can guide the test more comprehensively,so as to realize the adaptive exploration of the Android application interface.Experiments executed on 13 Android applications confirm the efficacy of the proposed method through experimentalcomparison and analysis with conventional reinforcement learning testing tools.

Key words: Android GUI testing, Reinforcement learning, Q-learning, Thompson sampling