Key points are not available for this paper at this time.
Several tools are marketed to the educational community for plagiarism detection and prevention. This article briefly contrasts the performance of two leading tools, TurnItIn and MyDropBox, in detecting submissions that were obviously plagiarized from articles published in IEEE journals. Both tools performed poorly because they do not compare submitted writings to publications in the IEEE database. Moreover, these tools do not cover the Association for Computing Machinery (ACM) database or several others important for scholarly work in software engineering. Reports from these tools suggesting that a submission has ldquopassedrdquo can encourage false confidence in the integrity of a submitted writing. Additionally, students can submit drafts to determine the extent to which these tools detect plagiarism in their work. Because the tool samples the engineering professional literature narrowly, the student who chooses to plagiarize can use this tool to determine what plagiarism will be invisible to the faculty member. An appearance of successful plagiarism prevention may in fact reflect better training of students to avoid plagiarism detection.
Kaner et al. (Thu,) studied this question.