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Android applications provide benefits to mobile phone users by offering operative functionalities and interactive user interfaces. However, application crashes give users an unsatisfactory experience, and negatively impact the application’s overall rating. Android application crashes can be avoided through intensive and extensive testing. In the related literature, the graphical user interface (GUI) test generation tools focus on generating tests and exploring application functions using different approaches. Such tools must choose not only which user interface element to interact with, but also which type of action to be performed, in order to increase the percentage of code coverage and to detect faults with a limited time budget. However, a common limitation in the tools is the low code coverage because of their inability to find the right combination of actions that can drive the application into new and important states. A Q-Learning-based test coverage approach developed in DroidbotX was proposed to generate GUI test cases for Android applications to maximize instruction coverage, method coverage, and activity coverage. The overall performance of the proposed solution was compared to five state-of-the-art test generation tools on 30 Android applications. The DroidbotX test coverage approach achieved 51.5% accuracy for instruction coverage, 57% for method coverage, and 86.5% for activity coverage. It triggered 18 crashes within the time limit and shortest event sequence length compared to the other tools. The results demonstrated that the adaptation of Q-Learning with upper confidence bound (UCB) exploration outperforms other existing state-of-the-art solutions.

Purpose The purpose of this paper is to monitor in-class activities and the performance of the students. Design/methodology/approach A pilot study was conducted to evaluate the proposed system using a questionnaire with 132 participants (teachers and non-teachers) in a presentation style to record the participant’s perception about performance expectancy (PE), effort expectancy (EE), facilitating conditions (FC), usability expectancy (UE) and user’s satisfaction (S) based on unified theory of acceptance use of technology (UTAUT) model. Findings The results show that PE, EE, FC had positive and significant influence on the UE of the proposed system. The effect of EE and FC on UE was seen to be more in female compared to male participants. The teacher category considered the PE and EE as important factors in determining their decision to use the proposed system. Originality/value A real-time student(s) visualization system based on the concept of real-time student locating system using radio frequency identification technology is proposed. Concepts can be categorized within the Internet of Things in the education domain.

Graphical User Interface (GUI) testing of Android apps has gained considerable interest from the industries and research community due to its excellent capability to verify the operational requirements of GUI components. To date, most of the existing GUI testing tools for Android apps are capable of generating test inputs by using different approaches and improve the Android apps’ code coverage and fault detection performance. Many previous studies have evaluated the code coverage and crash detection performances of GUI testing tools in the literature. However, very few studies have investigated the effectiveness of the test input generation tools, especially in the events sequence length of the overall test coverage and crash detection. The event sequence length generally shows the number of steps required by the test input generation tools to detect a crash. It is critical to highlight its effectiveness due to its significant effects on time, testing effort, and computational cost. Thus, this study evaluated the effectiveness of six test input generation tools for Android apps that support the system events generation on 50 Android apps. The generation tools were evaluated and compared based on the activity coverage, method coverage, and capability in detecting crashes. Through a critical analysis of the results, this study identifies the diversity and similarity of test input generation tools for Android apps to provide a clear picture of the current state of the art. The results revealed that a long events sequence performed better than a shorter events sequence. However, a long events sequence led to a minor positive effect on the coverage and crash detection. Moreover, the study showed that the tools achieved less than 40% of the method coverage and 67% of the activity coverage.

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