Towards the Validation of Plagiarism Detection Tools by Means of Grammar Evolution

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 t...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation Vol. 13; no. 3; pp. 477 - 485
Main Authors: Cebrian, M., Alfonseca, M., Ortega, A.
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.06.2009
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
AC generators
Applied sciences
Artificial intelligence
Automatic programming
Benchmark testing
Benchmarks
Computer languages
Computer programs
Computer science
computer science education
Computer science; control theory; systems
Counterfeiting
Educational institutions
educational technology
Evolution
Exact sciences and technology
General aspects
genetic algorithms
Grammars
Learning and adaptive systems
Markers
Mathematical models
Occupational training. Personnel. Work management
Plagiarism
Programming
Software
Software engineering
Software tools
Students
Tasks
Whales
Validation
computer science education
Automatic programming
Software development
Evolutionary algorithm
Empirical method
Grammar
Marker
genetic algorithms
Experimental study
educational technology
Teaching
Copyright
Software tool
Cheating
Programming language
University
Genetic algorithm
Education
Intellectual property
ISSN:1089-778X, 1941-0026
Online Access:Get full text
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Description
Summary: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.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2008.2008797