A Machine Learning Based Plagiarism Detection in Source Code

Abstract

Converting source codes to feature vectors can be useful in programming-related tasks, such as plagiarism detection on ACM contests. We present a brand-new method for feature extraction from C++ files, which includes both features describing syntactic and lexical properties of an AST tree and features characterizing disassembly of source code. We propose a method for solving the plagiarism detection task as a classification problem. We prove the effectiveness of our feature set by testing on a dataset that contains 50 ACM problems and ∼90k solutions for them. Trained xgboost model gets a relative binary f1-score=0.745 on the test set.