The School of Computer Science is pleased to present…
Detecting Plagiarism in Source Code Using LSTM-Based Deep Learning
MSc Thesis Proposal by:
Sumisha Surendran
Date: Monday, June 26th, 2023
Time: 08:30 am – 9:30 am
Location: Essex Hall, Room 122
Reminders: 1. Two-part attendance mandatory (sign-in sheet, QR Code) 2. Arrive 5-10 minutes prior to the event starting - LATECOMERS WILL NOT BE ADMITTED. Note that due to demand, if the room has reached capacity, even if you are "early," admission is not guaranteed. 3. Please be respectful of the presenter by NOT knocking on the door for admittance once the door has been closed whether the presentation has begun or not (If the room is at capacity, overflow is not permitted (i.e. sitting on floors) as this is a violation of the Fire Safety code). 4. Be respectful of the decision of the advisor/host of the event if you are not given admittance. The School of Computer Science has numerous events occurring soon
Abstract:
Plagiarism detection is a crucial aspect of ensuring academic integrity, particularly in the field of computer science, where source code plays a vital role. With the increasing reliance on digital platforms for assignment submissions at universities, developing advanced techniques for detecting code plagiarism effectively and efficiently has become imperative.
This thesis proposes an approach to tackle this problem by leveraging the power of Long Short-Term Memory (LSTM)-based deep learning models. Our research aims to design and implement an intelligent plagiarism detection system capable of identifying instances of code plagiarism within student submissions. By employing LSTM-based deep learning algorithms, the system aims to capture the inherent sequential dependencies and patterns present in source code, thereby enhancing detection accuracy and reducing the need for manual inspection. The proposed system also aims to overcome challenges such as obfuscation, code modifications, and paraphrasing techniques employed by plagiarists. Our research aims to contribute to the academic community by providing a reliable and efficient solution for detecting plagiarism in source code submissions. The outcomes of this study can significantly benefit universities and educational institutions in maintaining academic integrity and fostering a culture of originality and innovation in computer science education.
Keywords: Deep learning, LSTM, source code, plagiarism detection
Thesis Committee:
Internal Reader: Dr. Hossein Fani
External Reader: Dr. Guoqing Zhang
Advisor: Dr. Dan Wu
