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Automated GUI Tests Generation for Android Apps Using Q-learning

Mobile applications are growing in popularity and pose new problems in the area of software testing. In particular, mobile applications heavily depend upon user interactions and a dynamically changing environment of system events. In this thesis, we focus on user-driven events and use Q-learning, a reinforcement machine learning algorithm, to generate tests for Android applications under test (AUT). We implement a framework that automates the generation of GUI test cases by using our Q-learning approach and compare it to a uniform random (UR) implementation. A novel feature of our approach is that we generate user-driven event sequences through the GUI, without the source code or the model of the AUT. Hence, considerable amount of cost and time are saved by avoiding the need for model generation for generating the tests. Our results show that the systematic path exploration used by Q-learning results in higher average code coverage in comparison to the uniform random approach.

Identiferoai:union.ndltd.org:unt.edu/info:ark/67531/metadc984181
Date05 1900
CreatorsKoppula, Sreedevi
ContributorsBryce, Renee, Ludi, Stephanie, Sweany, Philip H.
PublisherUniversity of North Texas
Source SetsUniversity of North Texas
LanguageEnglish
Detected LanguageEnglish
TypeThesis or Dissertation
Formatvii, 47 pages, Text
RightsPublic, Koppula, Sreedevi, Copyright, Copyright is held by the author, unless otherwise noted. All rights Reserved.

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