Applying Reinforcement Learning for Automated Testing of Mobile Application Focusing on State Definition, Reward, and Learning Method

Keita Murase, Shingo Takada

Research output: Contribution to journalConference articlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)64-69
Number of pages6
JournalProceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE
Volume2023-July
DOIs
Publication statusPublished - 2023
Event35th International Conference on Software Engineering and Knowledge Engineering, SEKE 2023 - Hybrid, San Francisco, United States
Duration: 2023 Jul 12023 Jul 10

ASJC Scopus subject areas

  • Software

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