To make an app stand out in an increasingly competitive market, developers must ensure its quality to deliver a better user experience. UI testing is a popular technique for quality assurance, which can thoroughly test the app from the users’ perspective. However, while considerable research has already studied UI testing on the Android platform, there is no research on iOS. This paper introduces CydiOS, a novel approach to performing model-based testing for iOS apps. CydiOS enhances the existing static analysis to build a more complete static model for the app under test. We propose an approach to retrieve runtime information to obtain real-time app context that can be mapped in the model. To improve the effectiveness of UI testing, we also introduce a potential-aware search algorithm to guide testing execution. We compare CydiOS with four representative algorithms(i.e., random, depth-first, stoat, and ape). We have evaluated CydiOS on 50 popular apps from App Store, and the results show that CydiOS outperforms other tools, achieving both higher code coverage and screen coverage. We open source CydiOS at https://github.com/SoftWare2022Testing/CydiOS, and a demo video can be found there.