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.

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.

Artificial Intelligence (AI) and Machine Learning (ML) have significantly impacted various industries, including software development. Software testing, a crucial part of the software development lifecycle (SDLC), ensures the quality and reliability of software products. Traditionally, software testing has been a labor-intensive process requiring significant manual effort. However, the advent of AI and ML has transformed this landscape by introducing automation and intelligent decision-making capabilities. AI and ML technologies enhance the efficiency and effectiveness of software testing by automating complex tasks such as test case generation, test execution, and result analysis. These technologies reduce the time required for testing and improve the accuracy of defect detection, ultimately leading to higher quality software. AI can predict potential areas of failure by analyzing historical data and identifying patterns, which allows for more targeted and efficient testing. This paper explores the role of AI and ML in software testing by reviewing existing literature, analyzing current tools and techniques, and presenting case studies that demonstrate the practical benefits of these technologies. The literature review provides a comprehensive overview of the advancements in AI and ML applications in software testing, highlighting key methodologies and findings from various studies. The analysis of current tools showcases the capabilities of popular AI-driven testing tools such as Eggplant AI, Test.ai, Selenium, Appvance, Applitools Eyes, Katalon Studio, and Tricentis Tosca, each offering unique features and advantages. Case studies included in this paper illustrate real-world applications of AI and ML in software testing, showing significant improvements in testing efficiency, accuracy, and overall software quality.

Gesture-based interaction in smart vehicles has been introduced to improve usability, minimize distraction, and enhance road safety. However, existing gestures vary across vehicle brands and are not always intuitive for users. This study examines current trends in gesture control technology and investigates user preferences for in-vehicle gesture interfaces. In particular, it explores the inclusion of American Sign Language (ASL) as a gesture set and analyzes potential gender differences in preferences. A mixed-methods approach was employed: (i) a quantitative closed card sorting study evaluating predefined gestures from commercial vehicle systems and ASL and (ii) a qualitative gesture elicitation study capturing user-defined proposals. Results show that simple one-handed gestures, such as swiping and pointing, were consistently preferred for their versatility and low cognitive demand. ASL gestures were generally perceived as unsuitable for vehicle control due to their complexity and frequent requirement of two-handed execution. Gender analysis revealed no significant differences in gesture preferences. While the relatively small sample size (n = 19 for the elicitation study) limits generalizability, the findings provide design implications for developing intuitive gesture control systems in smart vehicles. They highlight the importance of simplicity, consistency, and integration with other modalities (e.g., voice or touch) to support safer and more effective multimodal interaction.

This study investigates the configurational pathways enabling women in Oman to translate entrepreneurial intentions into technology venture creation. By integrating institutional theory and resource-based view, we develop a novel framework examining how formal institutional support (FIS), informal institutional support (IIS), and digital self-efficacy (DSE) interact in Oman’s conservative context. We emphasize the significant enabling role of work–life balance resources (WLBR) and the cultural legitimacy of spousal endorsement. Our mixed-methods design utilizes survey data from 418 female IT graduates and 20 semi-structured interviews, analyzed through fuzzy-set Qualitative Comparative Analysis (fsQCA). The findings indicate that FIS predicts entrepreneurial intention (β = 0.34, p < 0.001) but not venture creation (OR = 0.85, p = 0.298), revealing a visibility gap in policy implementation. IIS predicts venture creation (OR = 1.43, p = 0.033), with spousal endorsement acting as a cultural legitimacy signal. DSE alone fails to predict venture creation but is vital when combined with WLBR. FsQCA identifies a sufficient configuration pathway characterized by the combination of spousal endorsement, domestic support, DSE, and WLBR with solution consistency of 0.93 and coverage of 0.78. WLBR is a necessary condition with necessity consistency of 0.96, demonstrating that venture creation is improbable without it. Qualitative evidence shows founders reposition conservative norms as legitimacy signals, while non-founders emphasize funding barriers despite policy awareness. We recommend that policymakers subsidize care infrastructure, leverage women-led community networks for targeted outreach, and formalize state-backed legitimacy programs that reduce kinship dependency while building autonomy-focused alternatives.