The Effectiveness of Low-Level Structure-based Approach Toward Source Code Plagiarism Level Taxonomy

TL;DR

This paper evaluates a low-level structure-based method for detecting source code plagiarism, demonstrating its effectiveness across various plagiarism levels and outperforming traditional token-based methods in real-world cases.

Contribution

It introduces an evaluation of the state-of-the-art low-level approach using real plagiarism data and confirms its superiority over baseline token-based methods across multiple plagiarism levels.

Findings

Effective in handling most plagiarism attacks

Outperforms baseline approach in most levels

Validated on real plagiarism cases

Abstract

Low-level approach is a novel way to detect source code plagiarism. Such approach is proven to be effective when compared to baseline approach (i.e., an approach which relies on source code token subsequence matching) in controlled environment. We evaluate the effectiveness of state of the art in low-level approach based on Faidhi \& Robinson's plagiarism level taxonomy; real plagiarism cases are employed as dataset in this work. Our evaluation shows that state of the art in low-level approach is effective to handle most plagiarism attacks. Further, it also outperforms its predecessor and baseline approach in most plagiarism levels.