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Shared information and program plagiarism detection
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
An Experience on Ada Programming Using On-Line Judging
Ada-Europe '09 Proceedings of the 14th Ada-Europe International Conference on Reliable Software Technologies
An evaluation framework for plagiarism detection
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
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Science of Computer Programming
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Evolutionary Computation
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Student plagiarism is a major problem in universities worldwide. In this paper, we focus on plagiarism in answers to computer programming assignments, where students mix and/or modify one or more original solutions to obtain counterfeits. Although several software tools have been developed to help the tedious and time consuming task of detecting plagiarism, little has been done to assess their quality, because determining the real authorship of the whole submission corpus is practically impossible for markers. In this paper, we present a Grammar Evolution technique which generates benchmarks for testing plagiarism detection tools. Given a programming language, our technique generates a set of original solutions to an assignment, together with a set of plagiarisms of the former set which mimic the basic plagiarism techniques performed by students. The authorship of the submission corpus is predefined by the user, providing a base for the assessment and further comparison of copy-catching tools. We give empirical evidence of the suitability of our approach by studying the behavior of one advanced plagiarism detection tool (AC) on four benchmarks coded in APL2, generated with our technique.