Searching source code fragments using incremental clustering

We propose a methodology using abstract syntax trees for the detection of plagiarism in source code, within an academic environment. We show the architecture and decisions that came before we produce our own solution (Spector), after conducting a study of the methods and tools in existence. An example is then shown, which goes through and explains each of the algorithms steps. Finally, conclusions are drawn noting that such a system, while not the most efficient, produces accurate results.

In programming learning environments, the pressure of delivering many programming assignments makes plagiarism the easiest solution. This highly threatens the learning process; therefore, the need of an automatic, fast, and accurate detection of source code plagiarism becomes essential. To detect whether a pair of Java files is plagiarized, this paper proposes four classification feature sets: (i) structural histogram features, histogram-based features for summarizing similarity matrices; (ii) lexical per-class features, extracted from a lexical similarity matrix between the classes of the two compared files based on character 3-grams; (iii) structural counting features, twelve counting features representing the code structure; and (iv) modified original features: a set of modifications on the features of the used baseline. The results show that the best feature sets in F-measure are the structural histogram features and the lexical per-class features combined, which improve the F-measure by 4% compared to the baseline. The added features slow down the execution time. However, it is still efficient, given that it can classify 70k pairs in 23 min. In addition, we partially re-annotated the SOurce COde Re-use dataset. After the re-annotation, the F-measure of both the baseline and our work is improved, and our work achieves an F-measure of 93.6%, which is 7.5% higher than the new F-measure of the baseline. In addition, some remarks and recommendations are provided for using the SOurce COde Re-use dataset as a benchmark.

Source code plagiarism is wide spread in beginners' programming courses. Especially, if programming is a minor subject, as for instance in engineering degrees. It is very tempting for students during a programming assignment to use a working copy of a fellow student rather than struggling with the time-consuming coding by themselves. But as learning programming requires a significant personal commitment, we confirm the results of other studies, that cheating leads to higher failure rates and lower scores in the examination. Automatic plagiarism detection systems are therefore measures against cheating. We analyzed the students' achievements and opinions during the last 5 years of operating an automated assessment system with plagiarism detection. The paper discusses in detail the benefits of such a system, e.g. the equal treatment of all students compared to manual plagiarism checks, and shows also the disadvantages, e.g. code obfuscation, that students perform in order to circumvent the system.

Advancement in information technology has resulted in massive textual material that is open to appropriation. Due to researchers’ misconduct, a plethora of plagiarism detection (PD) systems have been developed. However, most PD systems on the market do not support the Arabic language. In this paper, we discuss the design and construction of an Arabic PD reference corpus that is dedicated to academic language. It consists of (2312) dissertations that were defended by postgraduate students at the University of Jordan (JU) between the years 2001–2016. This Academic Jordan University Plagiarism Detection corpus; henceforth, JUPlag, follows the Dewey decimal classification (DDC) in the way it is structured. The goal of the corpus is twofold: Firstly, it is a database for the detection of plagiarism in student assignments, reports, and dissertations. Secondly, the n-gram structure of the corpus provides a knowledgebase for linguistic analysis, language teaching, and the learning of plagiarism-free writing. The PD system is guided by JU Library’s metadata for retrieval and discovery of plagiarism. To test JUPlag, we injected an unseen dissertation with multiple instances of plagiarism-simulated paragraphs and sentences. Experimentation with the system using different verbatim n-gram segments is indeed promising. Preliminary results encourage that permission be sought to enrich this corpus with all the theses in the Thesis Repository of the Union of Arab Universities. The JUPlag corpus is intended to function as an indispensable source for testing and evaluating plagiarism detection techniques. Since the University of Jordan is seeking to become a center for plagiarism detection for Arabic content and being a non-profit organization, it will charge a nominal fee for the use of JUPlag to finance the maintenance and development of the corpus.

The integration of Artificial Intelligence (AI) in academic writing has revolutionized the way scholars approach writing, editing, and ensuring academic integrity. This chapter explores the multifaceted role of AI in enhancing the academic writing process, focusing on AI-powered writing assistants, plagiarism detection, and grammar enhancement systems. With the advent of machine learning algorithms, AI has evolved from basic grammar checkers to sophisticated tools that provide real-time, context-aware suggestions, ensuring the clarity, coherence, and logical consistency of academic texts. Furthermore, AI tools for plagiarism detection have become critical in maintaining academic integrity by identifying not only direct plagiarism but also subtle forms such as paraphrasing and translation-based content theft. The chapter delves into how AI-driven plagiarism detection systems are increasingly becoming multilingual, offering effective support for non-native English speakers and researchers across different linguistic backgrounds. Additionally, the role of AI in customizing writing assistance for specific academic genres, ensuring adherence to discipline-specific conventions, and maintaining logical consistency from thesis to conclusion is examined. The future of AI in academic writing lies in its ability to provide personalized, adaptive feedback that enhances writing quality while promoting ethical standards across global academic platforms. The potential of AI tools to transform academic writing is immense, with far-reaching implications for scholarly productivity, writing efficiency, and the preservation of academic integrity.

Although paraphrasing is the linguistic mechanism underlying many plagiarism cases, little attention has been paid to its analysis in the framework of automatic plagiarism detection. Therefore, state-of-the-art plagiarism detectors find it difficult to detect cases of paraphrase plagiarism. In this article, we analyze the relationship between paraphrasing and plagiarism, paying special attention to which paraphrase phenomena underlie acts of plagiarism and which of them are detected by plagiarism detection systems. With this aim in mind, we created the P4P corpus, a new resource that uses a paraphrase typology to annotate a subset of the PAN-PC-10 corpus for automatic plagiarism detection. The results of the Second International Competition on Plagiarism Detection were analyzed in the light of this annotation. The presented experiments show that (i) more complex paraphrase phenomena and a high density of paraphrase mechanisms make plagiarism detection more difficult, (ii) lexical substitutions are the paraphrase mechanisms used the most when plagiarizing, and (iii) paraphrase mechanisms tend to shorten the plagiarized text. For the first time, the paraphrase mechanisms behind plagiarism have been analyzed, providing critical insights for the improvement of automatic plagiarism detection systems.

One method to prevent plagiarism is to rely on plagiarism checker application. However, prevention of plagiarism to date in Indonesia still relies on the plagisarism checker held by institutions/organizations outside Indonesia for example Turnitin, Dupli Checker, Copyleaks, PaperRater, gramarly, etc. Plagiarism checker applications currently use methods that are applied for English. Development of plagiarism checker application for Bahasa requires support from multiple disciplines. Bahasa has a very unique pattern and different from English. String matching is an approach for plagiarism detection in computer sience. String matching approach is a “character by character” matching method. This approach could also use hashing block of the character and then use n-Gram to match the hashing block. But before n-Gram, file or ducument need a preprocesing such as tokenization, stopword removal, stemming, dan hashing with Rabin-Karp algorithm. The plagiarism detection system with a string matching approach generates a percentage of the similarity of the document by calculating the N-Gram result with Dice's Similarity Coefficient. For text extraction on a system built with tokenization, stopword removal, and stemming. The text then randomized using the Rabin-Karp method.

Plagiarism has always been a serious concern in programming courses. Most universities use online code evaluation platforms to assess students’ knowledge, making source code plagiarism a real concern. With the hope of reducing this problem, many source code plagiarism detection tools are proposed each year, either based on source code analysis or the changes in student's characteristics, such as behavior, history of submissions, source IP address, etc. The purpose of this research is to offer a detailed state-of-the-art overview on source code plagiarism detection in programming courses and suggest a direction for a robust approach.

Source code plagiarism is a common occurrence in undergraduate computer science education. Many source code plagiarism detection tools have been proposed to address this problem. However, most of these tools only measure the similarity between assignment submissions, and do not actually identify which are suspicious of plagiarism. This work presents a semi-automatic approach that enables the indication of suspicious assignment submissions by analysing source code similarity scores among the submissions. The proposed approach seeks the consensus of multiple source code plagiarism detection tools in order to identify program pairs that are consistently evaluated with high similarity. A case study is presented to demonstrate the use of the proposed approach. The results of this case study indicate that it can accurately identify assignment submissions that are suspicious of plagiarism.

The problem of academic plagiarism has been present for centuries. Yet, the widespread dissemination of information technology, including the internet, made plagiarising much easier. Consequently, methods and systems aiding in the detection of plagiarism have attracted much research within the last two decades. Researchers proposed a variety of solutions, which we will review comprehensively in this article. Available detection systems use sophisticated and highly efficient character-based text comparisons, which can reliably identify verbatim and moderately disguised copies. Automatically detecting more strongly disguised plagiarism, such as paraphrases, translations or idea plagiarism, is the focus of current research. Proposed approaches for this task include intrinsic, cross-lingual and citation-based plagiarism detection. Each method offers unique strengths and weaknesses; however, none is currently mature enough for practical use. In the future, plagiarism detection systems may benefit from combining traditional character-based detection methods with these emerging detection approaches.

Informatics and programming are currently one of the most popular areas of study. Evolution in this area happens so quickly that one of the main sources which students use during their studies is the Internet. The issue is that this variety of resources available on the Internet often encourage students to copy these resources to their work and to commit plagiarism. In this paper, we will focus on plagiarism problems in computer science teaching. We compare available tools to detect plagiarism in the source code with the algorithm proposed by us. As the test dataset we will use the semester works from the courses taught at our university. For this comparison, it will be necessary to customize the proposed algorithm, so it can handle Java source codes. We will focus on the accuracy and completeness of each algorithm. We will also attempt to remove the useless detecting of automatically generated code as a plagiarism.

Source code plagiarism identificatio requires a system capable of identifying semantic similarity rather than mere textual resemblance. This study utilized a dataset of 1,000 source code files, which after cleaning resulted in 996 individual code samples collected from GitHub repositories. The dataset included various programming languages (Python, Java, JavaScript, TypeScript, C++), divided into 697 training data, 149 validation data, and 149 testing data. The model employed was CodeBERT, configured with a hidden size of 768, 12 layers, and 12 attention heads. CodeBERT generated vector embeddings for each code sample, which were then projected by a Siamese Network to calculate cosine similarity between code pairs. Testing used a threshold of 0.80 to classify plagiarism. The identification results achieved an accuracy of 96.4%, precision of 95.2%, recall of 97.8%, F1-score of 96.4%, and an error rate of 4.6%. The system produced similarity scores and status labels of “plagiarism detected” or “not detected,” demonstrating the effectiveness of the CodeBERT-based approach for adaptive and intelligent code similarity identificatio.

The objective of the work is to explore the potency of integrating structural and citation information with effective syntax‐semantic text‐based analysis for scientific plagiarism detection. One of the major limitations in today's plagiarism checkers is their sole dependence on text‐based detection, where they ignore the citation and structural information. Further, the text‐based detection approaches that they employ usually fail to trace out intelligent manipulations. In the proposed work, a plagiarism detection system is presented that employs the effective coupling of various modules, namely, logical structure classifications and citation parsing, two‐stage candidate document selections, syntax‐semantic‐based exhaustive passage level analysis with plagiarism analysis using structural and citation information. Further, a new plagiarism score, namely, weighted overall similarity index is proposed, opposed to the general plagiarism scores. The proposed approach is evaluated on the data set created by Alzahrani et al. (), which contains scientific publications imposed with various plagiarism complexities. Comparison of the final system results is done against a potential baseline approach. The proposed approach exhibits considerable improvement over the comparative baseline, and hence reflects the potency of syntax‐semantic text‐based analysis with structural and citation information.

Identifying plagiarized content is a crucial task for educational and research institutions, funding agencies, and academic publishers. Plagiarism detection systems available for productive use reliably identify copied text, or near-copies of text, but often fail to detect disguised forms of academic plagiarism, such as paraphrases, translations, and idea plagiarism. To improve the detection capabilities for disguised forms of academic plagiarism, we analyze the images in academic documents as text-independent features. We propose an adaptive, scalable, and extensible image-based plagiarism detection approach suitable for analyzing a wide range of image similarities that we observed in academic documents. The proposed detection approach integrates established image analysis methods, such as perceptual hashing, with newly developed similarity assessments for images, such as ratio hashing and position-aware OCR text matching. We evaluate our approach using 15 image pairs that are representative of the spectrum of image similarity we observed in alleged and confirmed cases of academic plagiarism. We embed the test cases in a collection of 4,500 related images from academic texts. Our detection approach achieved a recall of 0.73 and a precision of 1. These results indicate that our image-based approach can complement other content-based feature analysis approaches to retrieve potential source documents for suspiciously similar content from large collections. We provide our code as open source to facilitate future research on image-based plagiarism detection.

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