• International Journal of Knowledge Content Development & Technology
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• v.14 no.3
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• pp.39-57
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• 2024

Plagiarism detection software is beneficial in detecting plagiarism in research works of postgraduate students. Despite the benefits of using plagiarism detection software, studies have revealed that most students, including postgraduates, do not use plagiarism detection software as expected. This could depend on the provision of facilitating conditions like internet connectivity, training opportunities and electricity. Thus, this study examined facilitating conditions and the use of plagiarism detection software among postgraduates of the University of Ibadan, Nigeria. A descriptive survey research design of the correlational type was used for this study, with a population of 2143 postgraduates. The multi-stage random sampling technique was used to determine the sample size of 242. The questionnaire was the research instrument, and data was analysed using descriptive statistics. Results showed that most postgraduates agreed that the university provided facilitating conditions like internet connectivity. The majority of the respondents noted that they used Turnitin monthly. Most of the respondents noted that they used plagiarism detection software to paraphrase their work and check the correctness of the grammar in their documents. The most prominent challenges confronting plagiarism detection software use by most respondents were their inability to afford subscription payment to use the plagiarism detection software and slow internet connectivity. There was a significant positive relationship between facilitating conditions and the use of plagiarism detection software by the postgraduates of the University of Ibadan, Nigeria. Some of the recommendations for the institution's management include leveraging the vast network of alumni willing to give back to the institution and intervening in the provision of internet connectivity and electricity.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2005.11a
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• pp.319-323
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• 2005

As the software industry develops at a rate speed, anyone can copy or plagiarize without difficulty contents that are becoming digitalized. To make it worse, the development of various contents that be illegally copied and plagiarized are resulting in the increasing infringement on and the plagiarism of the intellectual property. This dissertation tries to put forth the method and the theory to effectively detect any plagiarism of the source code of programs realized in various languages. This dissertation analyzes the advantage and disadvantage of the plagiarism test software, and especially, presents a method to detect possible plagiarism by using the Pattern Matching to overcome its disadvantage. And it also intends to introduce more developed automatic detection system by overcoming the problems with the method of Pattern Matching.

• Journal of Information Technology Applications and Management
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• v.24 no.1
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• pp.1-10
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• 2017

Recently, the problem of plagiarism has emerged as a big social issue because not only literature but also thesis become the target of plagiarism. Even the government requires conformation for plagiarism of high-ranking official's thesis as a standard of their ethical morality. Plagiarism is not just direct copy but also paraphrasing, rewording, adapting parts, missing references or wrong citations. This makes the problem more difficult to handle adequately. We propose a plagiarism detection scheme called a bit signature in which each unique word of document is represented by 0 or 1. The bit signature scheme can find the similar documents by comparing their absolute and relative bit signatures. Experiments show that a bit signature scheme produces better performance for document copy detection than existing similar schemes.

• 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.

• 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.

• Journal of KIISE:Software and Applications
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• v.37 no.5
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• pp.394-402
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• 2010

JavaScript is one of the most popular languages to develope web sites and web applications. Since applicationss written in JavaScript are sent to clients as the original source code, they are easily exposed to plagiarists. Therefore, a method to detect plagiarized JavaScript programs is necessary. The conventional program dependency graph(PDG) based approaches are not suitable to analyze JavaScript programs because they do not reflect dynamic features of JavaScript. They also generate false positives in some cases and show inefficiency with large scale search space. We devise a JavaScript specific PDG(JS PDG) that captures dynamic features of JavaScript and propose a JavaScript plagiarism detection method for precise and fast detection. We evaluate the proposed plagiarism detection method with experiment. Our experiments show that our approach can detect false-positives generated by conventional PDG and can prune the plagiarism search space.

• 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.

• Journal of Distribution Science
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• v.13 no.4
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• pp.5-8
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• 2015

Purpose - The purpose of this paper is to review, analyze, and learn from the most recent cases of plagiarism and to identify and promote ethical practices in research and publication. Research design, data, and methodology - This is a case study, an analytical approach, which focuses on analyzing the most recent cases of plagiarism to identify ethical issues and concerns in journal publication practices. Results - Despite the availability of many software and web-based applications and programs to detect plagiarism, there is no universal or perfect plagiarism detection application available to ease the editorial responsibility. Lack of understanding the concept and ignorance of plagiarism were the main reasons for the cases of plagiarism. Conclusions - Some of the plagiarism cases reveal a lack of knowledge in proper application of in-text citations and references, including quoting, requiting, paraphrasing, and citing sources, etc. Furthermore, the need for recognizing and considering the distorted and falsified primary and secondary research data as plagiarism is essential to enhance ethical practices in journal publication.

• 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.

• Journal of Software Assessment and Valuation
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• v.17 no.1
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• pp.31-40
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• 2021

Plagiarism refers to the act of using the original data as if it were one's own without revealing the source. The plagiarism of source code causes a variety of problems, including legal disputes. Plagiarism in software projects is usually determined by measuring similarity by comparing every pair of source code within two projects. However, blindly comparing every pair has been a huge computational burden, causing a major factor of not using tools of better accuracy. If we can only compare pairs that are probable to be clones, eliminating pairs that are impossible to be clones, we can concentrate more on improving the accuracy of detection. In this paper, we propose a method of selecting highly probable candidates of clone pairs by pre-classifying suspected source-codes using a machine-learning model called code2vec.