Code Plagiarism and Originality Detection using Machine Learning for Ethical Code Practices

Authors

  • Harshali Patil Head of Department, Computer Engineering, Thakur College of Engineering and Technology, Kandivali, Mumbai-400101, INDIA
  • Siddhi Ambre Assistant Professor, Department of Computer Engineering, Thakur of College of Engineering and Technology, Kandivali, Mumbai-400101, INDIA
  • Karuna Bhosale Assistant Professor School of Engineering & Technology, Pimpri Chinchwad University, Talegoan, Pune-410506, INDIA
  • Anamika Singh Assistant Professor, Department of CS&E (cyber security), Thakur College of Engineering and Technology, Kandivali, Mumbai-400101, INDIA
  • Harsh Jha Department of Computer Engineering, Thakur of College of Engineering and Technology, Kandivali, Mumbai-400101, INDIA
  • Vedika Mandre Department of Computer Engineering, Thakur of College of Engineering and Technology, Kandivali, Mumbai-400101, INDIA
  • Ankit Maurya Department of Computer Engineering, Thakur of College of Engineering and Technology, Kandivali, Mumbai-400101, INDIA

Keywords:

Code Analysis, Code Originality System, Code Similarity, Ethical Coding Practices, Plagiarism Detection

Abstract

The design and development of a Code Originality System—a sophisticated software solution aimed at preserving the intellectual property rights of developers, upholding code quality, and promoting ethical coding practices. The primary goal of this research is to create a Code Originality System that utilizes various algorithms to analyze code similarity and detect plagiarism. Emphasis is placed on safeguarding intellectual property, ensuring code quality, and encouraging ethical coding practices. Utilizing token-based approaches and advanced machine learning models, the Code Originality System addresses challenges of code diversity, scalability, privacy, and algorithmic precision. The research emphasizes a pivotal role in safeguarding code integrity, offering insights into architectural components, customizable features, and integration capabilities. The study presents a robust Code Originality System, revealing its effectiveness in tackling challenges and underscoring its role in fostering innovation. The findings, supported by conclusive statistical data, highlight the system's uniqueness and its contribution to responsible and ethical software development practices. This research pioneers a Code Originality System, providing a critical stride towards a future defined by responsible and ethical software development practices.

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Published

24.03.2024

How to Cite

Patil, H. ., Ambre, S. ., Bhosale, K. ., Singh, A. ., Jha, H. ., Mandre, V. ., & Maurya, A. . (2024). Code Plagiarism and Originality Detection using Machine Learning for Ethical Code Practices. International Journal of Intelligent Systems and Applications in Engineering, 12(3), 209–215. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5242

Issue

Vol. 12 No. 3 (2024)

Section

Research Article

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