Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (11A): 221200145-7.doi: 10.11896/jsjkx.221200145

• Computer Software & Architecture • Previous Articles     Next Articles

Automated Testing Method of Android Applications Based on SA-UCB Algorithm

WANG Xi, ZHAO Chunlei, BU Zhiliang, YANG Yi   

  1. School of Computer Science and Engineering,Tianjin University of Technology,Tianjin 300384,China
    Key Laboratory of Computer Vision and System of Ministry of Education,Tianjin University of Technology,Tianjin 300384,China
    Tianjin Key Laboratory of Intelligent Computing and New Software Technology,Tianjin University of Technology,Tianjin 300384,China
  • Published:2023-11-09
  • About author:WANG Xi,born in 1998,postgraduate.Her main research interests include Android automated testing and so on.
    ZHAO Chunlei,born in 1979,Ph.D,associate professor,is a member of China Computer Federation.Her main research interests include cybersecurity and so on.
  • Supported by:
    Key Special Project of “Science and Technology Helps Economy 2020” of the Ministry of Science and Technology(SQ2020YFF0413781).

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Abstract: Aiming at the problem that the traditional reinforcement learning algorithm needs to learn the code of conduct,which leads to low testing efficiency,a model-based automated testing method for Android applications,SA-UCB,is proposed.The Sarsa algorithm is used to guide the test process,and the Q table is used as the reference for action strategy selection.And for the randomness of ε-greedy integrated by the classical Sarsa algorithm is too strong,the upper confidence bound algorithm(UCB algorithm) is introduced to balance the “exploration-exploitation dilemma”,which makes action decisions more decentralized.And it is applied to the Android automated testing process,the testing efficiency is improved.The SA-UCB method is compared with other five test methods in terms of test coverage,test efficiency and fault detection.The results show that SA-UCB strategy has certain advantages in test coverage and test efficiency under the same experimental conditions.

Key words: Android, Automated testing, Reinforcement learning, Sarsa, UCB

CLC Number: 

  • TP391
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