Faculty of Computing

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End date: 2024Subject: search.filters.subject.Android

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    Test Case Generation from Android Mobile Applications Focusing on Context Events
    (Association for Computing Machinery, 2018-02-02) Ibrahim Anka Salihu
    Nowadays mobile apps are developed to address more critical areas of people’s daily computing needs, which bring concern on the applications’ quality. Today’s Mobile apps processed not only the traditional GUI events but also accept and react to constantly varying context events which may have an impact on the application’s behaviour. To build high quality and more reliable applications, there is a need for effective testing techniques to test apps before release. Most of recent testing technique focuses on GUI events only making it difficult to identify other defects in the changes that can be inclined by the context in which an application runs. This paper proposed an approach for testing mobile apps considering the two sets of events: GUI events which we identified through static analysis of bytecode and context events obtained from analysis of manifest.xml file. Results from the experimental evaluation indicated that our approach is effective in identifying and testing context events.
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    Reinforcement Learning for Testing Android Applications
    (International Conference on Multidisciplinary Engineering and Applied Science, 2023-02-02) Moussa Mahamat Boukar; Muhammed Aliyu Suleiman; Ibrahim Anka Salihu
    This paper offers a review of current research studies that use reinforcement learning (RL) to test Android applications. The primary purpose of this study is to simplify future research by collecting and investigating the current state of Android app testing approaches using the RL technique. We provide a well-defined criterion comprising of seven key points. The key points are: addressed problems, reasons for using the RL technique, RL algorithms, supported events, testing techniques, validation, and evaluation methods. In the literature, we have analyzed various techniques to evaluate their efficiency. This study showed that model-based testing is the most commonly used testing technique. Q-learning is the best algorithm in terms of predictive accuracy. We identified that code coverage is the most widely used evaluation metric and comparison with other tools and techniques is the preferred validation approach.