Reinforcement Learning Application Testing Method based on Multi-attribute Fusion

Reinforcement learning has been successfully applied to assess the reliability of applications, but the existing testing methods based on reinforcement learning have the problems of invalid interactive widgets and difficult training, resulting in low testing efficiency. In order to solve these problems, this paper proposes a lightweight application automation testing method of deep reinforcement learning based on multi-attribute fusion (MARTesting). First, the invalid widgets are removed by the difference operation of the attribute sets of the current and previous state, then the attributes of all widget elements on the page are abstracted into state as the input of the neural network, and a state is accurately determined by fusing the position and text information of page elements, finally combine the novelty of the state and the execution frequency of the action as a reward function. The experimental results on six open source applications show that the MARTesting method proposed in this paper has achieved significant improvements in code coverage and branch coverage compared with existing methods.