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Developed for MSRIT's Information Science Department, under the guidance of Dr. Mydhili K Nair. Developed for the course IS6EB1 (Machine Learning)

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hhk998402/MOSS-Plagiarism-Checker

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MOSS-Plagiarism-Checker

Developed for MSRIT's Information Science Department, under the guidance of Dr. Mydhili K Nair. Developed for the course IS6EB1 (Machine Learning)

About the Project

The MOSS-Plagiarism-Checker uses the Stanford MOSS Plagiarism Checker service to perform the following functions: -

  1. Download code from specified git repositories of students for a particular assignment
  2. Collate the necessary files from all of the students, and send to MOSS for code plagiarism check
  3. The report generated by MOSS is used to generate detailed reports
  4. Finally, the reports are depicted in the form of a graph to show students who have similar submissions

Installation / Setup

  1. Clone the repo to a folder of your choice

  2. Install the required python libararies using pip install -r requirements.txt

Configurable Parameters

Within config.yml, a few parameters have to be changed with every new assignment

  • name_of_assignment: "Assignment_MLP"
    The assignment name has to be of the format Assignment_(nameOfAssignment)
  • form_responses_csv: "ML GitHub Link (Responses) - Form Responses 1.csv"
    The CSV file path should be provided. In this example, it is considered that the CSV file is placed in the root directory of the repo
  • language: "python"
    Specifying the coding language of the submissions while submitting to MOSS. Please refer to documentation
  • threshold_percentage: 50 Specify threshold percentage when generating code similarity (plagiarism) reports from MOSS
  • consolidated_report_name: "ConsolidatedReport_Assignment_MLP.xls" Use this parameter to specify the name of the consolidated report generated for all students

About

Developed for MSRIT's Information Science Department, under the guidance of Dr. Mydhili K Nair. Developed for the course IS6EB1 (Machine Learning)

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