International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 06 | Jun 2025
p-ISSN: 2395-0072
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Real-Time Code Plagiarism Detection Using NLP and Machine Learning for Academic and Industry Applications Nargis Siddiqui1, Deepshikha2 1Master of Technology, Computer Science and Engineering, Lucknow Institute of Technology, Lucknow, India 2Assistant Professor, Department of Computer Science and Engineering, Lucknow Institute of Technology,
Lucknow, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Code plagiarism has become a complex
with exponential growth of software development in the academic environment, as well as on the industry. Although the concepts of code sharing and the collaboration are the core elements of contemporary development approaches, they introduce the multifaceted problem of the intellectual property preservation and academic integrity. Intentional or unintended plagiarism of a source code subsequently jeopardizes the validity of academic testing and intellectual property of business software systems. As more and more institutions and organizations become dependent on coding assignments, shared repositories and ongoing development processes there occurs an urgent necessity in the need of highfidelity, completely automated plagiarism detection systems that could handle the emerging environment of code reuse.
problem to research and development in the current academic and industrial environment. The old systems of detection like MOSS and JPlag focus more on the syntactic similarity and this limits their effectiveness when it comes to some advanced techniques of plagiarism like Paraphrasing, renaming variable and code generated by AI. The given research implements a language-agnostic and real-time code plagiarism detection framework that is based on the combination of Natural Language Processing (NLP) and Machine Learning (ML) under the specific names of CodeBERT, Graph Neural Networks (GNNs) and Abstract Syntax Trees (ASTs). The system adopts a hybrid structure by studying the syntactical as well as semantical characteristics of the source code in different languages, among which include Python, Java, and C++ to mention a few. With its integration with technologies, such as Apache Kafka, FastAPI, Redis, and Kubernetes, the suggested solution enables real-time submissions of the code, with a response time of less than 150 milliseconds and high throughput. More than 15,000 code samples were used to train and validate the model, comprising of both industrial and academic code and synthetically obfuscated code as well as code generated by AI. The performance of evaluation has improved to a large extent in comparison to the traditional tools recording an F1- score of 91 percent in academic and 88 percent in industrial purposes. Case studies indicate that the academic dishonesty reduced by 30 percent, and violations of licenses were detected in enterprise codebases. The contribution of this work is not only in the next step towards state of code similarity detection but also in ability to conduct ethical and scalable content delivery of originality in education as well as in professional software development.
Figure-1: Evaluation of Plagiarism Detection
1.1 Motivation This research is motivated by the rising rate of incidence and level of sophistication of code plagiarism in the academia and industry. Students in the educational system have a reputation of reproducing programming projects taken by their fellow students through forums or even using artificial intelligence supported sites such as GitHub Copilot without any credits. This negates outcomes in learning and invalidates validity of assessment. On the industrial-side, unlicensed reuse of the ownership code, reverse chicanery and non-compliance with open-source permissions can raise severe risks, such as law suits and tainting an image. Consequently, open-source ecosystem, being a model of innovation, is frequently abused by developers, who do not follow the guidelines of the
Key Words: Code Plagiarism Detection, Natural Language Processing (NLP), Machine Learning (ML), CodeBERT, Abstract Syntax Tree (AST), Graph Neural Networks (GNN), Real-Time Detection, Cross-Language Analysis.
1. INTRODUCTION During recent years, reuse, adoption, and reproduction of the source code has experienced a significant increase
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