Applying Reinforcement Learning for Automated Testing of Mobile Application Focusing on State Definition, Reward, and Learning Method
Keita Murase, Shingo Takada · Proceedings/Proceedings of the ... International Conference on Software Engineering and Knowledge Engineering · 2023
There have been various studies on the automation of mobile app testing.Typical methods for automated testing of mobile apps are based on random search and on building state transition models.But there are problems in terms of the efficiency of search and accuracy of model building.This paper focuses on applying reinforcement learning to testing of mobile apps, especially issues such as explosion of the number of states, fixed rewards for transitions, and difficulty in convergence of learning.We focus on state definition, reward function, and a learning method to solve these problems.Specifically, we define states using discrete values of UI (User Interface) information on the screen, define a dynamic reward function, and perform periodic learning by using the transition history.The proposed method is implemented and evaluated.Evaluation results show that our proposed approach shows 1.21 times higher coverage than an existing tool using reinforcement learning.