Effective, Platform-Independent GUI Testing via Image Embedding and Reinforcement Learning

Abstract

Software applications (apps) have been playing an increasingly important role in various aspects of society. In particular, mobile apps and web apps are the most prevalent among all applications and are widely used in various industries as well as in people’s daily lives. To help ensure mobile and web app quality, many approaches have been introduced to improve app GUI testing via automated exploration, including random testing, model-based testing, learning-based testing, etc. Despite the extensive effort, existing approaches are still limited in reaching high code coverage, constructing high-quality models, and being generally applicable. Reinforcement learning-based approaches, as a group of representative and advanced approaches for automated GUI exploration testing, are faced with difficult challenges, including effective app state abstraction, reward function design, etc. Moreover, they heavily depend on the specific execution platforms (i.e., Android or Web), thus leading to poor generalizability and being unable to adapt to different platforms.

This work specifically tackles these challenges based on the high-level observation that apps from distinct platforms share commonalities in GUI design. Indeed, we propose PIRLT_EST, an effective platform-independent approach for app testing. Specifically, PIRLT_EST utilizes computer vision and reinforcement learning techniques in a novel, synergistic manner for automated testing. It extracts the GUI widgets from GUI pages and characterizes the corresponding GUI layouts, embedding the GUI pages as states. The app GUI state combines the macroscopic perspective (app GUI layout) and the microscopic perspective (app GUI widget), and attaches the critical semantic information from GUI images. This enables PIRLT_EST to be platform-independent and makes the testing approach generally applicable on different platforms. PIRLT_EST explores apps with the guidance of a curiosity-driven strategy, which uses a Q-network to estimate the values of specific state-action pairs to encourage more exploration in uncovered pages without platform dependency. The exploration will be assigned with rewards for all actions, which are designed considering both the app GUI states and the concrete widgets, to help the framework explore more uncovered pages. We conduct an empirical study on 20 mobile apps and 5 web apps, and the results show that PIRLT_EST is zero-cost when being adapted to different platforms, and can perform better than the baselines, covering 6.3–41.4% more code on mobile apps and 1.5–51.1% more code on web apps. PIRLT_EST is capable of detecting 128 unique bugs on mobile and web apps, including 100 bugs that cannot be detected by the baselines.