Fastbot2: Reusable Automated Model-based GUI Testing for Android Enhanced by Reinforcement Learning

We introduce a reusable automated model-based GUI testing technique for Android apps to accelerate the testing cycle. Our key insight is that the knowledge of event-activity transitions from the previous testing runs, i.e., executing which events can reach which activities, is valuable for guiding the follow-up testing runs to quickly cover major app functionalities. To this end, we propose (1) a probabilistic model to memorize and leverage this knowledge during testing, and (2) design a model-based guided testing strategy (enhanced by a reinforcement learning algorithm). We implemented our technique as an automated testing tool named Fastbot2. The evaluation on two popular industrial apps (with billions of user installations), Douyin and Toutiao, shows that Fastbot2 outperforms the state-of-the-art testing tools (Monkey, Ape and Stoat) in both activity coverage and fault detection in the context of continuous testing. To date, Fastbot2 has been deployed in the CI pipeline at ByteDance for nearly two years, and 50.8% of the developer-fixed crash bugs were reported by Fastbot2, which significantly improves app quality. Fastbot2 has been made publicly available to benefit the community at: https://github.com/bytedance/Fastbot-Android.