Automated test generation for Andriod apps with reinforcement learning algorithms often produce test suites with redundant coverage. We looked at minimizing test suites that have already been generated based on state–action–reward–state–action (SARSA) algorithms. In this dissertation, we hypothesize that there is room for improvement by introducing novel hybrid approaches that combine SARSA-generated test suites with greedy reduction algorithms following the principle of Head-up Guidance System (HGS™) approach. In addition, we apply an empirical study on Android test suites that reveals the value of these new hybrid methods. Our novel approaches focus on post-processing test suites by applying greedy reduction algorithms. To reduce Android test suites, we utilize different coverage criteria including event-based criterion (EBC), element-based criterion (ELBC), and combinatorial-based sequences criteria (CBSC) that follow the principle of combinatorial testing to generate sequences of events and elements. The proposed criteria effectively decreased the test suites generated by SARSA and revealed a high performance in maintaining code coverage. These findings suggest that test suite reduction using these criteria is particularly well suited for SARSA-generated test suites of Android apps.
| Identifer | oai:union.ndltd.org:unt.edu/info:ark/67531/metadc2356236 |
| Date | 07 1900 |
| Creators | Alenzi, Abdullah Sawdi M. |
| Contributors | Bryce, Renée, Morozov, Kirill, Tunc, Cihan, Li, Yuan |
| Publisher | University of North Texas |
| Source Sets | University of North Texas |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
| Format | Text |
| Rights | Public, Alenzi, Abdullah Sawdi M., Copyright, Copyright is held by the author, unless otherwise noted. All rights Reserved. |
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