Android applications are pervasively adopted and heavily relied on in our daily life, leading to the growing demand for enhanced user experiences, such as ease for operation and robustness. Nevertheless, developers continue to prioritize traditional functionality and performance, overlooking the pivotal role of user experience in real-world scenarios. For example, poorly designed page elements can lead to user confusion, resulting in unexpected outcomes, termed as the error-prone operation anomalies (EPAs). In this work, we undertake the first effort to uncover the underlying essence of the EPA problem. To achieve this objective, we investigated the root causes of EPAs from three dimensions, i.e., subject, object and environment. These causes were identified by multi-stage attribute capturing and precise similarity computation. In this process, the causes are categorized into fine-grained classes, namely confusing behaviours, unsuitable layout, and resource overload. Building upon these insights, we propose a dynamic GUI-based testing tool EP-Detector to facilitate detecting the EPAs in real-world apps. The EP-Detector is equipped with widget-exploration based target navigation and automatic test oracle, enabling it to detect error-prone page elements and simulate events with both comprehensiveness and precision. To systematically study the prevalence and severity of real-world EPAs, we conducted experiments on 53 popular Android apps with EP-Detector. The confirmed results not only validate the high precision and completeness of EP-Detector but also highlight that EPAs are prevalent in current apps, with at least one EPA existing in every two page widgets on average, and 28.3% of them may lead to security and functionality issues or risks. The EP-Detector is available at https://github.com/WordDealer/EP-Detector.

Android's multi-window solutions allow several apps to coexist on one screen, providing powerful functionalities and appealing visuals for modern mobile devices. However, this opens the door for potential malware hijacking attacks. In our study, we unveiled Android's insufficient security measures against malware activity in multi-window modes, and identified strategies on how to bypass these measures. We first introduced long-term monitoring methods through background services or malicious sub-window activities, and then designed and implemented 7 multi-window hijacking strategies to achieve multi-window hijacking in the three mainstream multi-window modes. To evaluate the effectiveness and impact of the attacks, we ran the hijacking in different system versions supporting multi-window solutions, and simulated the hijacking on 262 popular apps. All of the apps suffer from at least two types of multi-window hijacking attacks, and more than 65% of them can be attacked by all the 7 multi-window hijacking attacks. Furthermore, we designed and distributed a questionnaire to gather user opinions towards these attacks. 85% of respondents believe that the multi-window hijacking is highly covert and difficult to defend against. Of significance, we received acknowledgement and award fund from development teams of impactful apps.