ELEMENT AND EVENT-BASED TEST SUITE REDUCTION FOR ANDROID TEST SUITES GENERATED BY REINFORCEMENT LEARNING

Abstract

Android stands as one of the most popular operating systems on a global scale. Given the popularity and the tremendous use of Android apps and the necessity of developing robust and reliable apps, it is crucial to create efficient and effective testing tools while addressing real-world time and budget constraints. Recently, automated test generation with Reinforcement Learning algorithms have shown promise, but there is room for improvement as these algorithms often produce test suites with redundant coverage. Fine tuning parameters of RL algorithms may assist, however, this comes with trade-offs and requires time consuming and careful consideration of the characteristics of the application under test and its environment. Therefore, devising cost-effective tools and techniques is imperative to mitigate this redundancy. Instead of exploring parameters of RL algorithms, we looked at minimizing test suites that have already been generated based on 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 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.