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There are many approaches to hypothesis testing. The well-known approach of p-values has been drawn into question and even controversy in more recent years, even though criticisms reportedly date
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Skip to main content Main Menu - Primary Home Projects Supervisors Expression of Interest Contact Testing AI/LLM systems Primary supervisor Yongqiang Tian Research area Software Engineering In
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Skip to main content Main Menu - Primary Home Projects Supervisors Expression of Interest Contact Automated software testing and debugging with/without LLMs Primary supervisor Yongqiang Tian Research
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testing approaches that can be used to verify that machine learning models are not biased. Required knowledge Software engineering, software testing, statistics, machine learning
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inefficient to achieve a full spectrum examination. A generic, scalable and effective vulnerability detection platform, taking advantage of both static and dynamic techniques, is desirable. To further overcome
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large datasets and developing experimental techniques, including the use of artificial intelligence. There are also opportunities to be involved in the development and testing of new hardware for the next
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action in a software project. Furthermore, the refined LLMs will prove valuable in assisting with writing documentation throughout the software development journey. Required knowledge Software engineering
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testing code, and learning from feedback. 🔍 Research Objectives Design an Agentic SWE Framework Model an AI system that combines reasoning, planning, and self-correction for software engineering tasks
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to support a project that considers one of the following issues: - Victimology of cyberscam victims - Innoculation to cyberscams - Examination of victims' cyberscam journeys - Effective methods to change
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. The project aims to examine how these credentials can be designed and governed to ensure authenticity and trust while safeguarding learners’ privacy. By combining technical analysis, formal modeling, and policy