  Source code plagiarism is a long-standing issue in tertiary computer science
education. Many source code plagiarism detection tools have been proposed to
aid in the detection of source code plagiarism. However, existing detection
tools are not robust to pervasive plagiarism-hiding transformations, and as a
result can be inaccurate in the detection of plagiarised source code. This
article presents BPlag, a behavioural approach to source code plagiarism
detection. BPlag is designed to be both robust to pervasive plagiarism-hiding
transformations, and accurate in the detection of plagiarised source code.
Greater robustness and accuracy is afforded by analysing the behaviour of a
program, as behaviour is perceived to be the least susceptible aspect of a
program impacted upon by plagiarism-hiding transformations. BPlag applies
symbolic execution to analyse execution behaviour and represent a program in a
novel graph-based format. Plagiarism is then detected by comparing these graphs
and evaluating similarity scores. BPlag is evaluated for robustness, accuracy
and efficiency against 5 commonly used source code plagiarism detection tools.
It is then shown that BPlag is more robust to plagiarism-hiding transformations
and more accurate in the detection of plagiarised source code, but is less
efficient than compared tools.
