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A Survey on Functionality-oriented GUI Testing Empowered by Deep Learning
PhD Qualifying Examination Title: "A Survey on Functionality-oriented GUI Testing Empowered by Deep Learning" by Mr. Xiaolei LI Abstract: Graphical User Interface (GUI) Testing has become indispensable in software testing as an effective method for functionality verification. Through interactive GUI testing, software developers can identify discrepancies between the expected and actual behavior of the software, allowing them to pinpoint and resolve potential defects. However, manually writting test cases for GUIs is both time-consuming and prone to human error. To address this, automated GUI testing tools have been developed to reduce manual effort. Most traditional GUI testing tools rely on coverage-oriented metrics, aiming to explore the GUI state space as extensively as possible, sometimes overlooking the real business logic of the applications. This lack of functionality awareness may lead to a low fault detection rate, compared to functionality-oriented testing, which centers around specific target functionalities when generating test cases. In recent years, advanced deep learning (DL) techniques have been integrated into automated GUI testing tools to empower functionality-oriented testing. In this survey, we conduct a comprehensive review of DL-boosted functionality-oriented GUI testing tools, examining how DL techniques are integrated and the challenges they address. Specifically, we discuss two main phases of typical functionality-oriented GUI testing tools: target functionality identification and guided GUI exploration and a key component: text input generation. For each, we identify the critical challenges mitigated by the integration of DL techniques and offer insights into opportunities for future research in this field. Date: Tuesday, 14 January 2025 Time: 2:00pm - 4:00pm Venue: Room 3494 Lifts 25/26 Committee Members: Prof. Shing-Chi Cheung (Supervisor) Dr. Yepang Liu (Co-supervisor, SUSTech) Prof. Raymond Wong (Chairperson) Dr. Shuai Wang Dr. Jiasi Shen