Uncertainty-Based Metamorphic Testing for Validating Plagiarism Detection Systems

Abstract

Plagiarism is a severe issue in academia, and uncertainty in plagiarism detection systems might lead to inconsistent detections. Thus, evaluating the system is essential; however, it is also a test oracle problem as it is challenging to distinguish correct behaviour from potentially incorrect behaviour of the system. To alleviate this challenge, we develop a feasible approach by applying an uncertainty matrix to identify the uncertainty of the plagiarism detection systems and derive metamorphic relations of metamorphic testing from the identified uncertainty for validation. We experimented with three plagiarism detection systems in a classroom scenario where students were hypothesized to use tools to generate answers for assignments. These answers were fed into the systems for validation by comparing the systems’ similarity scores of the tool-generated answers. Results showed that the proposed approach can effectively validate plagiarism detection systems. Future studies can apply this approach to locate uncertainties to enhance systems’ robustness.