Masters of Art / Globally there is growing concern around increased levels of plagiarism. Gullifer and Tyson (2010, p. 463) claim that plagiarism is growing at a rapid rate, and universities are now required to devote enough time and resources to combating it. South African universities have also found themselves to be victims of plagiarism. To prevent increased levels of plagiarism in South Africa, Stellenbosch University (SU) promotes the use of Turnitin. The study described in this thesis investigated postgraduate students’ and lecturer’s perceptions and attitudes regarding plagiarism and interacting with Turnitin at the University of Stellenbosch Business School to fill a research gap, as up to this point, this topic has not been studied. The study adopted a mixed methods approach, which combined both quantitative and qualitative methods. Moreover, the theory of planned behaviour informed the study.

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The investigation concerns a 1982 suit brought by Texas novelist Benjamin Capps and his publishers against the author and publisher of an historical novel, Sacajawea, alleging that the book contained approximately 145 instances of copyright infringement. Parallel-column exhibits of passages from the novel by Anna Lee Waldo and from Capps's writings illustrate the evidence submitted in court. The publishing history of the novel, brought out by Avon Books, is related, as well as the story of readers' discoveries of suspicious material and the ultimate litigation. A comparison is made of the original novel and a revised edition published in 1984. Using the Sacajawea case as a reference point, the study considers the state of ethics in the contemporary literary world.

This semestral thesis presents definition of plagiarism and focuses primarily on solving this problem in academic world. The main topic is the detection of plagiarism. It is discussed the various steps of the detection process and special attention is given to plagiarism detection of program codes. The work mentions question of the reliability of detection tools and divides the plagiarism detection methods into basic groups. One chapter is devoted metrics for comparing files. Mentioned are two tools available to detect plagiarism. In the last chapter is analyzed own draft program for plagiarism detection of program codes. The detector was applied to a database of student’s works, and the results were plotted.

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This empirical exploratory study quantitatively analyzed content similarity indices (potential plagiarism) from a corpus consisting of 360 dissertations and 360 published articles. The population was defined using the filtering search criteria human resource development, training and development, organizational development, career development, or HRD. This study described in detail the process of collecting content similarity analysis (CSA) metadata using Turnitin software (www.turnitin.com). This researcher conducted robust descriptive statistics, a Wilcoxon signed-rank statistic between the similarity indices before and after false positives were excluded, and a multinomial logistic regression analysis to predict levels of plagiarism for the dissertations and the published articles. The corpus of dissertations had an adjusted rate of document similarity (potential plagiarism) of M = 9%, (SD = 6%) with 88.1% of the dissertations in the low level of plagiarism, 9.7% in the high and 2.2% in the excessive group. The corpus of published articles had an adjusted rate of document similarity (potential plagiarism) of M = 11%, (SD = 10%) with 79.2% of the published articles in the low level of plagiarism, 12.8% in the high and 8.1% in the excessive group. Most of the difference between the dissertations and published articles were attributed to plagiarism-of-self issues which were absent in the dissertations. Statistics were also conducted which returned a statistically significant justification for employing the investigative process of removing false positives, thereby adjusting the Turnitin results. This study also found two independent variables (reference and word counts) that predicted dissertation membership in the high (.15-.24) and excessive level (.25-1.00) of plagiarism and published article membership in the excessive level (.25-1.00) of plagiarism. I used multinomial logistic regression to establish the optimal prediction model. The multinomial logistic regression results for the dissertations returned a Nagelkerke pseudo R2 of .169 and for the published articles a Nagelkerke pseudo R2 .095.

Authorship attribution of source code is the task of deciding who wrote a program, given its source code. Applications include software forensics, plagiarism detection, and determining software ownership. A number of methods for the authorship attribution of source code have been presented in the past. A review of those existing methods is presented, while focusing on the two state-of-the-art methods: SCAP and Burrows. The primary goal was to develop a new method for authorship attribution of source code that is even more effective than the current state-of-the-art methods. Toward that end, a comparative study of the methods was performed in order to determine their relative effectiveness and establish a baseline. A suitable set of test data was also established in a manner intended to support the vision of a universal data set suitable for standard use in authorship attribution experiments. A data set was chosen consisting of 7,231 open-source and textbook programs written in C++ and Java by thirty unique authors. The baseline study showed both the Burrows and SCAP methods were indeed state-of-the-art. The Burrows method correctly attributed 89% of all documents, while the SCAP method correctly attributed 95%. The Burrows method inherently anonymizes the data by stripping all comments and string literals, while the SCAP method does not. So the methods were also compared using anonymized data. The SCAP method correctly attributed 91% of the anonymized documents, compared to 89% by Burrows. The Burrows method was improved in two ways: the set of features used to represent programs was updated and the similarity metric was updated. As a result, the improved method successfully attributed nearly 94% of all documents, compared to 89% attributed in the baseline. The SCAP method was also improved in two ways: the technique used to anonymize documents was changed and the amount of information retained in the source code author profiles was determined differently. As a result, the improved method successfully attributed 97% of anonymized documents and 98% of non-anonymized documents, compared to 91% and 95% that were attributed in the baseline, respectively. The two improved methods were used to create an ensemble method based on the Bayes optimal classifier. The ensemble method successfully attributed nearly 99% of all documents in the data set.