  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.
