Detection of Source Code Plagiarism Using Machine Learning Approach

Bandara, Upul; Wijayrathna, Gamini

doi:10.7763/ijcte.2012.v4.555

Cited by 11 publications

(6 citation statements)

References 10 publications

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“…We prepared the baseline comparisons with well‐known classification methods such as RF, K‐nearest neighbor (KNN), Naïve Bayes (NB), and J48 35–38 . These models are already suggested by some authors for source code plagiarism detection.…”

Section: Resultsmentioning

confidence: 99%

An intelligent decision support system for software plagiarism detection in academia

2021

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The act of source code plagiarism is an academic offense that discourages the learning habits of students. Online support is available through which students can hire professional developers to code their regular programming tasks. These facilities make it easier for students to practice plagiarism. First, raw source codes are cleaned from noisy data to extract meaningful codes as the actual logic is more important to the programmers. Second, pre‐processing techniques based on tokenization are used to convert filtered codes into meaningful tokens. It breaks the codes into small instances with the number of occurrences known as the frequency. Thirdly, the local and global weighting scheme method is applied to estimate the significance of each feature in an individual or a group of documents. It helps us greatly to zoom in on the importance of each feature of how effective it is for the next phase. Fourth, the single value decomposition method is used to reduce the dimensions of these features by maintaining the actual semantics of the source codes. This technique is used to remove overloaded noise information and collect only those features that are more effective for plagiarism detection. Fifth, the latent semantic analysis (LSA) technique is used to mine the actual semantics of the source codes in the form of latent variables. After that, the LSA features are used as input to cosine similarity to compute the plagiarism among different source codes. To validate the proposed approach, we used the topic modeling approach to group the relevant features into different topics.

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Section: Resultsmentioning

confidence: 99%

An intelligent decision support system for software plagiarism detection in academia

2021

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“…[39] This algorithm aims to analyze and extract the major elements of two photos to determine their similarity. [40] However, the SIFT algorithm is known to have limitations, including sensitivity to image noise, variation, and distortion. [41]…”

Section: Literature Surveymentioning

confidence: 99%

“…For the text extracting mechanism tokenization system brought into existence as well as streaming was designed.Subsequently, the text is randomized using the Rabin-Karp technique. [40] 2021 A method was developed for identifying text and cross-language plagiarism in both English and Albanian. By implementing this approach to monitor student work, it was anticipated that this paper would enhance standards and accountability in educational settings and universities.…”

Section: Solutions Brought To Overcome the Plagiarism Year Approachmentioning

confidence: 99%

A Comprehensive Survey of Advanced Image Processing and OCR Techniques for Enhanced Image Plagiarism Detection

Palvadi Srinivas Kumar

2024

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In today's digital world, sharing files, documents, and presentations online has become routine for both work and study purposes. However, this surge in online sharing has brought forth a significant challenge: plagiarism. Plagiarism will happen whenever the copied content in various sources by not giving proper credit. This paper delves into the realm of plagiarism detection, particularly focusing on images. We discuss various tools and techniques designed to detect plagiarism and compare their effectiveness based on factors like accuracy. Finally, we analyze the work of different authors and share common findings. Our research employs a thorough review process to ensure the accuracy of our conclusions. By emphasizing the use of AI-powered tools, our agenda is to promote sharing of original dataover the online domain.

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“…The fingerprinting method is designed to extract the resemblance between source code files mainly based on the digest algorithm. In the work of Bandara and Wijayrathna, 23 the structure‐based along with attributed counting methods are used for structures mining. The resultant structures are used to extract resemblance in source codes.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The proposed research is compared with other works done before as shown in Table 6. In the work of Bandara and Wijayrathna, 23 the authors used the KNN algorithm to retrieve similarity in Java source codes. The used technique retrieved count of lines, expressions, tokens, comments, spaces, underscore, indent spaces.…”

Section: Comparison With Other Approachesmentioning

confidence: 99%

Software plagiarism detection in multiprogramming languages using machine learning approach

Ullah

Wang

Farhan

et al. 2018

Concurrency and Computation

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Summary The Software plagiarism, which arises the problem of software piracy is a growing major concern nowadays. It is a serious risk to the software industry that gives huge economic damages every year. The customers may develop a modified version of the original software in other types of programming languages. Furthermore, the plagiarism detection in different types of source codes is a challenging task because each source code may have specific syntax rules. In this paper, we proposed a methodology for software plagiarism detection in multiprogramming languages based on machine learning approaches. The Principal Component Analysis (PCA) is applied for features extraction from source codes without losing the actual information. It extracts features by factor analysis and converts the dataset into normalized linear principal components which are further useful for predictions analysis. Then, the multinomial logistic regression model (MLR) is applied to these components to classify the source codes documents based on predictions. It gives the generalization of logistic regression to handle multiclass problems. Further, the predictors' performance in MLR is evaluated by 2 tailed z test. To apply the experiment, the dataset is collected in five different and popular languages, ie, C, C++, Java, C#, and Python. Each programming language taken in two different case studies, ie, binary search and Stack.

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Detection of Source Code Plagiarism Using Machine Learning Approach

Cited by 11 publications

References 10 publications

An intelligent decision support system for software plagiarism detection in academia

An intelligent decision support system for software plagiarism detection in academia

A Comprehensive Survey of Advanced Image Processing and OCR Techniques for Enhanced Image Plagiarism Detection

Software plagiarism detection in multiprogramming languages using machine learning approach

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