View Proposal

Proposer
Zi Hau Chin
Title
Detection of AI-Generated Programming Solutions Using Code-Structure Features
Goal
Investigate whether structural source-code characteristics can distinguish AI-generated solutions from human-written solutions.
Description
The system should be presented as a probabilistic research or screening tool, not proof of academic misconduct. -Which structural features are most useful for detecting AI-generated code? -Does the detector generalise to unseen programming tasks? -Does it generalise to unseen AI models? -How robust is it to human editing, reformatting, identifier renaming, and refactoring? -Are its explanations stable and meaningful?
Resources
HumanEval, HumanEval on Hugging Face, Zero-Shot Detection of Machine-Generated Code, Project CodeNet
Background
Python, supervised machine learning, feature engineering and model evaluation, generative AI and code-generation models
Url
Difficulty Level
High
Ethical Approval
Full
Number Of Students
1
Supervisor
Zi Hau Chin
Keywords
ai-generated code detection, code stylometry, code authorship, abstract syntax tree, machine-generated code, explainable classification
Degrees
Bachelor of Science in Computing Science
Back to List