Abstract: Android applications have become deeply integrated into various aspects of people’s daily lives.However,ensuring the correctness of these applications remains a highly challenging task.Traditional testing methods primarily rely on manual operations,while automated testing technologies,despite advancements,still require improvements.The continuous iteration of Android applications to enhance performance and meet functional requirements has led to increased application complexity and an explosive growth in state combinations.Testing the core aspects of Android applications involves exploring deep-seated failures under complex user interactions,but the search space for these failures is vast,necessitating substantial time investment for thorough testing.In recent years,researchers have begun employing reinforcement learning to test Android applications by adjusting exploration strategies based on rewards obtained during the interaction between agents and the Android applications.However,existing work has been limited to testing with a single device,significantly constraining testing efficiency.To address these challenges,this paper proposes a distributed automated testing framework for Android applications based on reinforcement learning,named DistributedAndroidExplore(DAE).DAE utilizes multiple agents to concurrently conduct reinforcement learning-based testing on applications and periodically iterates to aggregate the cumulative learning experiences of each agent,thereby enhancing testing efficiency.DAE is evaluated on 10 real-world Android applications.The results indicate that,in most cases,DAE surpasses the compared benchmark algorithms in terms of fault detection rate and code coverage.Furthermore,DAE demonstrates notably higher testing efficiency,with performance improvements ranging from 16.5% to 34.3%.

Key words: Distributed system, Android applications, Reinforcement learning, Q-learning, Automated testing