In spite of years of effort, plagiarism in student assignment submissions still causes considerable difficulties for course designers; if students’ work is not their own, how can anyone be certain they have learnt anything. Running-Karp-Rabin Greedy-String-Tiling (or RKS-GST), whose development arose from the observation that students shuffle independent code segments. It is able to detect transposed subsequences, and is less perturbed by spurious additional statements. Additionally , n-grams model for n=2 and n=3 is also being used for this purpose.A bigram or digram is a sequence of two adjacent elements from a string of tokens, which are typically letters, syllables, or words similarly a trigram is sequnce of three adjacent letters. A bigram is an n-gram for n=2 and a trigram is for n=3.
As of now this project is compatible with python 2.x . Required python libraries are specified bellow.You can also install this libraries using requirements.txt file. To install type in command prompt or terminal (make sure your current working directory is the location of your requiements.txt file)
pip install -r requirements.txt
Or install it individually, following libraries using python pip command.
nltk==3.2.2
pandas==0.20.2
Note : We have used a starting dataset to test out our project . It can be referenced here:
ir.shef.ac.uk/cloughie/resources/plagiarism_corpus.html
for rkr-gst
- Clone this repository
- write
from textpreforrkr import main_methodin your python file or in python terminal. - Call
main_methodin your python file with arguments in the following formatmain_method([List of original file names],name of suspected copied file)
NOTE :
- list of files in same directory otherwise specify directory.
- input file in same directory otherwise specify directory.
You can view documentation of this project here.
IEEE MSIT Summer Project
Members
- Deepanshu Kapoor , Sahil Chopra , Akanksha Rai Sharma
Mentors
- Yatin Gupta , Abhijeet Chauhan