How to Build a Python Plagiarism Checker: A Step-by-Step Guide

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Plagiarism detection in Python code is an essential topic, especially in academia, software development, and open-source communities. Whether you’re an educator checking student assignments or a developer ensuring code originality, having a reliable plagiarism checker is crucial.

This guide will cover everything from understanding code plagiarism to building your own plagiarism checker using Python. We’ll also explore existing tools that can help automate the process.

What Is Code Plagiarism?

Before diving into detection methods, let’s first define what constitutes plagiarism in coding.

Code plagiarism occurs when someone like student, developer etc copies or closely replicates another person’s programming code without proper attribution

Types of Code Plagiarism

  1. Exact Copying – When the code is copied verbatim without any modifications.
  2. Variable Renaming – The logic remains the same, but variable and function names are changed.
  3. Structural Modifications – Minor changes like altering indentation or changing loop structures.
  4. Logic Paraphrasing – The implementation is rewritten differently but performs the same function.

Why Is Code Plagiarism a Problem?

Plagiarism is a serious issue in various settings:

  • Academia: Students submitting identical code violates academic integrity policies.
  • Software Development: Companies must prevent unauthorized code reuse.
  • Open Source: Detecting plagiarism protects intellectual property and licensing rights.

Now that we understand plagiarism in programming, let’s explore different ways to detect it.

Methods for Detecting Code Plagiarism in Python

There are multiple approaches to identifying plagiarism in Python code. Each method has its strengths and weaknesses.

1. Text-Based Comparison

One of the simplest ways to detect plagiarism is by comparing raw text in code files. This method works well for exact matches and minor modifications.

Example: Using difflib to Compare Two Python Files

import difflib

def compare_files(file1, file2):
    with open(file1, 'r') as f1, open(file2, 'r') as f2:
        content1 = f1.readlines()
        content2 = f2.readlines()
    
    diff = difflib.unified_diff(content1, content2)
    return ''.join(diff)

# Example usage
file1 = 'script1.py'
file2 = 'script2.py'
print(compare_files(file1, file2))

✅ Pros: Fast and simple to implement.
❌ Cons: Fails if variable names or structure are altered.

2. Using Abstract Syntax Trees (ASTs)

Instead of comparing raw text, ASTs analyze the structure of code, making them more effective when variable names or comments are modified.

Example: Detecting Similarity with Python’s ast Module

import ast

def get_ast_structure(file):
    with open(file, 'r') as f:
        return ast.dump(ast.parse(f.read()))

file1_ast = get_ast_structure('script1.py')
file2_ast = get_ast_structure('script2.py')

print("Files are similar!" if file1_ast == file2_ast else "Files are different.")

✅ Pros: More robust than text-based comparison.
❌ Cons: Cannot detect logic-level plagiarism.

3. Hashing & Fingerprinting for Faster Comparison

Instead of comparing every line, fingerprinting techniques like Rabin-Karp or SimHash break code into chunks and generate unique fingerprints.

Example: Using Python’s hashlib for Simple Hashing

import hashlib

def hash_code(file):
    with open(file, 'r') as f:
        return hashlib.md5(f.read().encode()).hexdigest()

hash1 = hash_code('script1.py')
hash2 = hash_code('script2.py')

print("Plagiarism detected!" if hash1 == hash2 else "No plagiarism.")

✅ Pros: Works efficiently on large datasets.
❌ Cons: Limited to detecting identical code.

Building a Python Plagiarism Checker from Scratch

Now, let’s combine these methods to build a basic plagiarism detection tool that compares multiple files in a directory.

Full Python Script for a Basic Plagiarism Checker

import os
import difflib
import ast
import hashlib

def get_files(directory):
    return [os.path.join(directory, f) for f in os.listdir(directory) if f.endswith('.py')]

def compare_text(file1, file2):
    with open(file1, 'r') as f1, open(file2, 'r') as f2:
        return difflib.SequenceMatcher(None, f1.read(), f2.read()).ratio()

def compare_ast(file1, file2):
    with open(file1, 'r') as f1, open(file2, 'r') as f2:
        return ast.dump(ast.parse(f1.read())) == ast.dump(ast.parse(f2.read()))

def hash_code(file):
    with open(file, 'r') as f:
        return hashlib.md5(f.read().encode()).hexdigest()

def detect_plagiarism(directory):
    files = get_files(directory)
    results = {}

    for i in range(len(files)):
        for j in range(i + 1, len(files)):
            text_similarity = compare_text(files[i], files[j])
            ast_similarity = compare_ast(files[i], files[j])
            hash_similarity = hash_code(files[i]) == hash_code(files[j])

            results[(files[i], files[j])] = (text_similarity, ast_similarity, hash_similarity)

    return results

# Run the checker on a directory of Python scripts
directory = "path/to/python/files"
plagiarism_results = detect_plagiarism(directory)

for (file1, file2), (text_sim, ast_sim, hash_sim) in plagiarism_results.items():
    print(f"Comparing {file1} and {file2}:")
    print(f"  Text Similarity: {text_sim:.2f}")
    print(f"  AST Match: {ast_sim}")
    print(f"  Hash Match: {hash_sim}\n")

✅ Pros: Combines multiple detection techniques for accuracy.
❌ Cons: Limited by structural variations and code obfuscation.

Existing Tools for Code Plagiarism Detection

If building your own tool is not feasible, here are some ready-to-use solutions:

ToolFeaturesUsage
MOSSFree academic plagiarism checkerOnline submission
CopyleaksAI-powered code similarity detectionPython API
JPlagDetects structural similaritiesOpen-source

Example: Using Copyleaks API for Plagiarism Checking

from copyleaks.sdk import Copyleaks

client = Copyleaks()
client.login(API_KEY)
client.scan_file('script1.py', 'python')

✅ Pros: Powerful detection with minimal setup.
❌ Cons: May require a subscription.

Challenges & Limitations of Plagiarism Detection

Even with advanced techniques, plagiarism detection isn’t perfect. Here are some common challenges:

  • Code Obfuscation: Plagiarists can modify code structure to evade detection.
  • Logic Rewriting: Some methods fail when logic is re-implemented differently.
  • Performance Issues: Large-scale plagiarism detection can be computationally expensive.

To improve detection accuracy, combining multiple methods (text-based, ASTs, and machine learning) is recommended.

Conclusion

Thus, Detecting plagiarism in Python code is essential for maintaining academic integrity and protecting intellectual property. In this guide, we:
✅ Explored different plagiarism detection methods.
✅ Built a simple Python plagiarism checker.
✅ Discussed existing tools and their applications.

For more advanced detection, integrating machine learning techniques and commercial tools like Copyleaks can enhance accuracy.

Therefore Ready to implement a plagiarism checker in your project? Start coding today and ensure code originality!

FAQs

❓ Can I use Python to detect plagiarism in other programming languages?
Yes! Many techniques, like text comparison and ASTs, can be adapted for languages like Java or JavaScript.

❓ What is the best free plagiarism detection tool?
MOSS is widely used in academia for detecting code similarity.

❓ How accurate are Python-based plagiarism checkers?
It depends on the method. ASTs and fingerprinting improve accuracy, but they’re not foolproof.

epub in python

References

Below are the sources used in this article:

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