• Journal of KIISE:Software and Applications
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• v.30 no.7_8
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• pp.792-802
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• 2003

Earlier program plagiarism check systems are performed by using simple text, attribute or token string base on match techniques. They have difficulties in checking program styles which have nothing to do with program syntax such as indentation, spacing and comments. This paper introduces a plagiarism check model which compares syntax-trees for the given programs. By using syntax-trees, this system can overcome the weekness of filtering program styles and have advantage of comparing the structure of programs by syntax and semantic analysis. Our study introduces syntactic tree creation, unparsing and similarity check algorithms about C/C++ program plagiarism checking for internet cyber education and estimate plagiarism pattern.

• Journal of KIISE:Software and Applications
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• v.35 no.7
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• pp.442-451
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• 2008

Most plagiarism detection systems evaluate the similarity of source codes and detect plagiarized program pairs. If we use the source codes in plagiarism detection, the source code security can be a significant problem. Plagiarism detection based on target code can be used for protecting the security of source codes. In this paper, we propose a new plagiarism detection technique for Java programs using bytecodes without referring their source codes. The plagiarism detection procedure using bytecode consists of two major steps. First, we generate the token sequences from the Java class file by analyzing the code area of methods. Then, we evaluate the similarity between token sequences using the adaptive local alignment. According to the experimental results, we can find the distributions of similarities of the source codes and that of bytecodes are very similar. Also, the correlation between the similarities of source code pairs and those of bytecode pairs is high enough for typical test data. The plagiarism detection system using bytecode can be used as a preliminary verifying tool before detecting the plagiarism by source code comparison.

• The KIPS Transactions:PartD
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• v.13D no.6 s.109
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• pp.833-838
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• 2006

Program Plagiarism is an infringement of software copyright. In detecting program plagiarism, many different source program comparison methods has been studied. But, it is not easy to detect plagiarized program that made a few cosmetic changes in program structures and variable names In this paper, we propose a new ground-breaking technique in detecting plagiarism by Memory Access Log Analysis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.6
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• pp.1627-1648
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• 2012

We propose an adaptive method for detecting plagiarized pairs from a large set of source code. This method is adaptive in that it uses an adaptive algorithm and it provides an adaptive threshold for determining plagiarism. Conventional algorithms are based on greedy string tiling or on local alignments of two code strings. However, most of them are not adaptive; they do not consider the characteristics of the program set, thereby causing a problem for a program set in which all the programs are inherently similar. We propose adaptive local alignment-a variant of local alignment that uses an adaptive similarity matrix. Each entry of this matrix is the logarithm of the probabilities of the keywords based on their frequency in a given program set. We also propose an adaptive threshold based on the local outlier factor (LOF), which represents the likelihood of an entity being an outlier. Experimental results indicate that our method is more sensitive than JPlag, which uses greedy string tiling for detecting plagiarism-suspected code pairs. Further, the adaptive threshold based on the LOF is shown to be effective, and the detection performance shows high sensitivity with negligible loss of specificity, compared with that using a fixed threshold.

• English Language & Literature Teaching
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• v.12 no.1
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• pp.221-238
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• 2006

This study investigates the reliability of plagiarism measurement in the ESL essay writing context. The current study aims to address the answers to the following research questions: 1) How does plagiarism measurement affect test reliability in a psychometric view? and 2) how do raters conceive the plagiarism in their analytic scoring? This study uses the mixed-methodology that crosses quantitative-qualitative techniques. Thirty eight international students took an ESL placement writing test offered by the University of Illinois. Two native expert raters rated students' essays in terms of 5 analytic features (organization, content, language use, source use, plagiarism) and made a holistic score using a scoring benchmark. For research question 1, the current study, using G-theory and Multi-facet Rasch model, found that plagiarism measurement threatened test reliability. For research question 2, two native raters and one non-native rater in their email correspondences responded that plagiarism was not a valid analytic area to be measured in a large-scale writing test. They viewed the plagiarism as a difficult measurement are. In conclusion, this study proposes that a systematic training program for avoiding plagiarism should be given to students. In addition, this study suggested that plagiarism is measured reliably in the small-scale classroom test.

• Proceedings of the Korea Contents Association Conference
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• 2003.11a
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• pp.72-78
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• 2003

In this paper, survey of technical methods for protecting intellectual properties and proposal of art works plagiarism detection are accomplished. For this, in this paper, a survey of technical methods for inspecting of program source code plagiarism, analysis of natural languages plagiarism types and existing inspection methods are accomplished Also, author verification system and plagiarism detection about ancient literatures or art works is proposed because of ancient literatures or art work are important in the aspect of cultural properties control, protecting of author's intellectual property and owner's property estimation.

• East Asian Journal of Business Economics (EAJBE)
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• v.5 no.3
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• pp.41-47
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• 2017

Purpose - Adult learners are easily exposed to the risk of plagiarizing others' writings when writing papers or reports. Therefore, this study investigates the research ethics of adult learners and suggests alternatives for research ethics standards in online lifelong education system. The purpose of this study is to investigate the causes of violation of research ethics and preventive measures (education program) for adult learners. Research design, data, methodology - Before establishing a research ethics education plan, it is confirmed through the empirical investigation of adult learners in undergraduate. In this study, 30 questionnaires of 5-point Likert scale were used to examine the level of consciousness of adult learners related to research ethics. Results - First, the experience of participating in ethics education and the presence of prior knowledge did not affect the prevention of research ethics violation. Second, adult learners did not learn about research ethics in the pre- college education environment and they frequently engaged in research ethics violation without guilt or consciousness. Third, the students who experienced the research ethics violation (plagiarism) were more willing to know and use the plagiarism search program. Fourth, Opinions differed according to age as to the need to enforce research ethics compliance. Conclusions - Universities should clearly and easily announce and monitor plagiarism criteria or guidelines. When adult learners are relieved of the anxiety about the possibility of violation of research ethics, a developmental and original research atmosphere will be created.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.6
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• pp.189-196
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• 2014

Though the plagiarism is illegal and should be avoided, it still occurs frequently. Particularly, the plagiarism of source codes is more frequently committed than others since it is much easier to copy them because of their digital nature. To prevent code plagiarism, there have been reported a variety of studies. However, previous studies for plagiarism detection techniques on source codes do not consider the data structures although a source code consists both of data structures and algorithms. In this paper, a plagiarism detection technique for source codes considering data structures is proposed. Specifically, the data structures of two source codes are represented as sets of trees and compared with each other using Hungarian Method. To show the usefulness of this technique, an experiment has been performed on 126 source codes submitted as homework results in an object-oriented programming course. When both the data structures and the algorithms of the source codes are considered, the precision and the F-measure score are improved 22.6% and 19.3%, respectively, than those of the case where only the algorithms are considered.

• The KIPS Transactions:PartA
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• v.16A no.6
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• pp.453-462
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• 2009

Studies on software plagiarism detection, prevention and judgement have become widespread due to the growing of interest and importance for the protection and authentication of software intellectual property. Many previous studies focused on comparing all pairs of submitted codes by using attribute counting, token pattern, program parse tree, and similarity measuring algorithm. It is important to provide a clear-cut model for distinguishing plagiarism and collaboration. This paper proposes a source code clustering algorithm using a probability model on extreme value distribution. First, we propose an asymmetric distance measure pdist($P_a$, $P_b$) to measure the similarity of $P_a$ and $P_b$ Then, we construct the Plagiarism Direction Graph (PDG) for a given program set using pdist($P_a$, $P_b$) as edge weights. And, we transform the PDG into a Gumbel Distance Graph (GDG) model, since we found that the pdist($P_a$, $P_b$) score distribution is similar to a well-known Gumbel distribution. Second, we newly define pseudo-plagiarism which is a sort of virtual plagiarism forced by a very strong functional requirement in the specification. We conducted experiments with 18 groups of programs (more than 700 source codes) collected from the ICPC (International Collegiate Programming Contest) and KOI (Korean Olympiad for Informatics) programming contests. The experiments showed that most plagiarized codes could be detected with high sensitivity and that our algorithm successfully separated real plagiarism from pseudo plagiarism.

• International Journal of Control, Automation, and Systems
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• v.6 no.6
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• pp.809-817
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• 2008

Program plagiarism is widespread due to intelligent software and the global Internet environment. Consequently the detection of plagiarized source code and software is becoming important especially in academic field. Though numerous studies have been reported for detecting plagiarized pairs of codes, we cannot find any profound work on understanding the underlying mechanisms of plagiarism. In this paper, we study the evolutionary process of source codes regarding that the plagiarism procedure can be considered as evolutionary steps of source codes. The final goal of our paper is to reconstruct a tree depicting the evolution process in the source code. To this end, we extend the well-known bioinformatics approach, a local alignment approach, to detect a region of similar code with an adaptive scoring matrix. The asymmetric code similarity based on the local alignment can be considered as one of the main contribution of this paper. The phylogenetic tree or evolution tree of source codes can be reconstructed using this asymmetric measure. To show the effectiveness and efficiency of the phylogeny construction algorithm, we conducted experiments with more than 100 real source codes which were obtained from East-Asia ICPC(International Collegiate Programming Contest). Our experiments showed that the proposed algorithm is quite successful in reconstructing the evolutionary direction, which enables us to identify plagiarized codes more accurately and reliably. Also, the phylogeny construction algorithm is successfully implemented on top of the plagiarism detection system of an automatic program evaluation system.