Layout and Image Recognition Driving Cross-Platform Automated Mobile Testing

The fragmentation problem has extended from Android to different platforms, such as iOS, mobile web, and even mini-programs within some applications (app), like WeChat 1 . In such a situation, recording and replaying test scripts is one of the most popular automated mobile app testing approaches. However, such approach encounters severe problems when crossing platforms. Different versions of the same app need to be developed to support different platforms relying on different platform supports. Therefore, mobile app developers need to develop and maintain test scripts for multiple platforms aimed at completely the same test requirements, greatly increasing testing costs. However, we discover that developers adopt highly similar user interface layouts for versions of the same app on different platforms. Such a phenomenon inspires us to replay test scripts from the perspective of similar UI layouts. In this paper, we propose an image-driven mobile app testing framework, utilizing Widget Feature Matching and Layout Characterization Matching to analyze app UIs. We use computer vision (CV) technologies to perform UI feature comparison and layout hierarchy extraction on mobile app screenshots to obtain UI structures containing rich contextual information of app widgets, including coordinates, relative relationship, etc. Based on acquired UI structures, we can form a platform-independent test script, and then locate the target widgets under test. Thus, the proposed framework non-intrusively replays test scripts according to a novel platform-independent test script model. We also design and implement a tool named LIRAT to devote the proposed framework into practice, based on which, we conduct an empirical study to evaluate the effectiveness and usability of the proposed testing framework. The results show that the overall replay accuracy reaches around 65.85% on Android (8.74% improvement over state-of-the-art approaches) and 35.26% on iOS (35% improvement over state-of-the-art approaches).