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AuDeNTES: Automatic Detection of teNtative plagiarism according to a rEference Solution

AuDeNTES: Automatic Detection of teNtative plagiarism according to a rEference Solution AuDeNTES: Automatic Detection of teNtative plagiarism according to a rEference Solution LEONARDO MARIANI and DANIELA MICUCCI, University of Milano Bicocca In academic courses, students frequently take advantage of someone else ™s work to improve their own evaluations or grades. This unethical behavior seriously threatens the integrity of the academic system, and teachers invest substantial effort in preventing and recognizing plagiarism. When students take examinations requiring the production of computer programs, plagiarism detection can be semiautomated using analysis techniques such as JPlag and Moss. These techniques are useful but lose effectiveness when the text of the exam suggests some of the elements that should be structurally part of the solution. A loss of effectiveness is caused by the many common parts that are shared between programs due to the suggestions in the text of the exam rather than plagiarism. In this article, we present the AuDeNTES anti-plagiarism technique. AuDeNTES detects plagiarism via the code fragments that better represent the individual students ™ contributions by ltering from students ™ submissions the parts that might be common to many students due to the suggestions in the text of the exam. The ltered parts are identi ed by comparing students ™ submissions http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Computing Education (TOCE) Association for Computing Machinery

AuDeNTES: Automatic Detection of teNtative plagiarism according to a rEference Solution

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2012 by ACM Inc.
ISSN
1946-6226
DOI
10.1145/2133797.2133799
Publisher site
See Article on Publisher Site

Abstract

AuDeNTES: Automatic Detection of teNtative plagiarism according to a rEference Solution LEONARDO MARIANI and DANIELA MICUCCI, University of Milano Bicocca In academic courses, students frequently take advantage of someone else ™s work to improve their own evaluations or grades. This unethical behavior seriously threatens the integrity of the academic system, and teachers invest substantial effort in preventing and recognizing plagiarism. When students take examinations requiring the production of computer programs, plagiarism detection can be semiautomated using analysis techniques such as JPlag and Moss. These techniques are useful but lose effectiveness when the text of the exam suggests some of the elements that should be structurally part of the solution. A loss of effectiveness is caused by the many common parts that are shared between programs due to the suggestions in the text of the exam rather than plagiarism. In this article, we present the AuDeNTES anti-plagiarism technique. AuDeNTES detects plagiarism via the code fragments that better represent the individual students ™ contributions by ltering from students ™ submissions the parts that might be common to many students due to the suggestions in the text of the exam. The ltered parts are identi ed by comparing students ™ submissions

Journal

ACM Transactions on Computing Education (TOCE)Association for Computing Machinery

Published: Mar 1, 2012

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