As Android apps become increasingly dependent on network services, Network-Related Faults (NRFs) are gradually more prevalent and severely degrade user experience. These faults are typically scattered across apps and require complex, often non-trivial network patterns to trigger, which makes their detection challenging. To date, we still lack a general and in-depth understanding of NRFs in real-world Android apps. To fill this gap, we conduct the first empirical study on 154 real-world network-related bugs collected from 42 diverse, representative Android apps, investigating their characteristics, influences, triggering patterns, and origins. Our study reveals several notable findings and practical implications to guide future research on detecting and mitigating NRFs. Motivated by the empirical results and the limitations of existing Android testing approaches—namely, the lack of targeted network events and efficient injection mechanisms—we propose NATE, a novel Network-Aware Testing Enhancer that augments existing general Android testing approaches for NRF detection. NATE leverages curiosity-driven reinforcement learning to provide network-aware guidance and to inject effective network events, enabling testing approaches to explore network-related extra app functionalities and detect NRFs. When integrated with two state-of-the-art general Android testing approaches, experiments conducted on 12 large, active apps demonstrate the effectiveness and efficiency of NATE, with 1.7-5.7× as many faults detected, as well as 8.8% and 12.5% more code covered. Among the network-related faults detected by NATE, 21 have been explicitly confirmed as real-world bugs by the developers (six of which have already been fixed), where 16 of them were first reported by NATE. Notably, none of the 21 bugs were detected by the original general testing approaches, demonstrating the unique contributions of NATE.

Graphical User Interface (GUI) testing is one of the primary approaches for testing mobile apps. Test scripts serve as the main carrier of GUI testing, yet they are prone to obsolescence when the GUIs change with the apps' evolution. Existing repair approaches based on GUI layouts or images prove effective when the GUI changes between the base and updated versions are minor, however, they may struggle with substantial changes. In this paper, a novel approach named COSER is introduced as a solution to repairing broken scripts, which is capable of addressing larger GUI changes compared to existing methods. COSER incorporates both external semantic information from the GUI elements and internal semantic information from the source code to provide a unique and comprehensive solution. The efficacy of COSER was demonstrated through experiments conducted on 20 Android apps, resulting in superior performance when compared to the state-of-the-art tools METER and GUIDER. In addition, a tool that implements the COSER approach is available for practical use and future research.

GUI models encapsulate the desired visual appearance and interactive behaviors of applications, facilitating various downstream tasks like model-based testing (MBT). Manually constructing high-quality GUI models is not only labor-intensive and costly but also prone to errors, particularly as applications evolve and require frequent model updates. Existing automated approaches for GUI model generation heavily rely on reverse engineering, where the models are abstractions of the code. As a result, they are not suitable for MBT to test functional issues because they are consistent with the code. Meanwhile, valuable development artifacts such as UI/UX design documents, which reflect design intentions, are often overlooked. In this paper, a novel approach named DemGen is proposed to seek a unique pathway for GUI model generation. Leveraging design documents, DemGen employs computer vision pre-trained models in conjunction with a rule-based correction mechanism to identify GUI elements and their intended behaviors as defined in those documents. Subsequently, the identified content is transformed into a formal GUI model adhering to the IFML modeling language. Our evaluation, conducted in collaboration with an industry partner on commercial applications, demonstrates the effectiveness and efficiency of DemGen in GUI element recognition and GUI model generation. Moreover, we conducted a comparative analysis of manual, automated, and hybrid modeling techniques, assessing the usefulness of generated models on MBT tasks.