EP-Detector: Automatic Detection of Error-Prone Operation Anomalies in Android Applications

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.