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The primary objective of this project is to enhance Large Language Models (LLMs) by incorporating software knowledge documentation. Our approach involves utilizing existing LLMs and refining them
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knowledge program analysis, fuzzing, software testing, natural language processing
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Automating code generation, SQL query formulation, and data preprocessing pipelines is a crucial step toward intelligent and efficient software development. This project aims to leverage large
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🎯 Research Vision The next generation of software engineering tools will move beyond autocomplete and static code generation toward autonomous, agentic systems — AI developers capable of planning
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Candidates should hold a previous degree (Bachelor’s and/or Master’s) in Computer Science, Data Science, Robotics, Mechatronics, or Software Engineering, with demonstrated knowledge in machine
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Generative AI NLP skills System security Software testing To be eligible you must have: A first-class honours (H1) Bachelor’s degree or equivalent in the relevant research area (completed or near
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nutritional data into a user-friendly platform, enabling consumers, restaurants, and policymakers to make informed food choices and reduce diet-related emissions. Required knowledge Data analytics and software
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Nowadays more and more intelligence software solutions emerge in our daily life, for example the face recognition, smart voice assitants, and autonomous vehicle. As a type of data-driven solutions
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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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software is not on the blacklist" (without revealing the exact software). There are multiple aspects of this project. For all aspects, some cryptography background is required. Design and analysis of new