Improving Search-Based Android Test Generation Using Surrogate Models

Auer, Michael; Adler, F.T.

doi:10.1007/978-3-031-21251-2_4

Cited by 7 publications

(1 citation statement)

References 21 publications

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“…If the event does not cause the AU T to exit, the algorithm retrieves the newly available events (line 21) and initializes their Q-values (lines [22][23][24][25][26]. It calculates the discount factor, gamma, based on the new events (line 27), retrieves the reward of the executed event (line 28), and selects a new event to execute using an epsilon-greedy policy (line 29).…”

Section: Construct Validitymentioning

confidence: 99%

Reinforcement Learning-Based Test Case Generation with Test Suite Prioritization for Android Application Testing

Khan

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This dissertation introduces a hybrid strategy for automated testing of Android applications that combines reinforcement learning and test suite prioritization. These approaches aim to improve the effectiveness of the testing process by employing reinforcement learning algorithms, namely Q-learning and SARSA (State-Action-Reward-State-Action), for automated test case generation. The studies provide compelling evidence that reinforcement learning techniques hold great potential in generating test cases that consistently achieve high code coverage; however, the generated test cases may not always be in the optimal order. In this study, novel test case prioritization methods are developed, leveraging pairwise event interactions coverage, application state coverage, and application activity coverage, so as to optimize the rates of code coverage specifically for SARSA-generated test cases. Additionally, test suite prioritization techniques are introduced based on UI element coverage, test case cost, and test case complexity to further enhance the ordering of SARSA-generated test cases. Empirical investigations demonstrate that applying the proposed test suite prioritization techniques to the test suites generated by the reinforcement learning algorithm SARSA improved the rates of code coverage over original orderings and random orderings of test cases.

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