Code Similarity Detection Using Fuzzy Logic

Overview (January 2024)

This project implements a system to detect similarity between source code snippets using Abstract Syntax Tree (AST) analysis and textual comparison. A fuzzy logic model integrates these metrics to produce an overall similarity score, assisting in assessing originality and preventing copyright infringement.

Features

• AST Analysis: Analyzes the structure of code snippets to determine similarity.
• Textual Similarity: Computes the textual similarity percentage between code strings.
• Fuzzy Logic Model: Utilizes fuzzy logic to evaluate overall similarity based on AST and textual metrics.
• Similarity Classification: Categorizes similarity scores into distinct classes.

Requirements

• Python 3.x
• numpy
• skfuzzy

Usage

1. Import the necessary libraries.
2. Define the code snippets you want to compare.
3. Call the compare_codes(code1, code2) function to get the similarity score and classification.

Functionality

• analyze_ast(code): Analyzes the AST of the given code.
• calculate_text_similarity(code1, code2): Computes the textual similarity percentage.
• create_fuzzy_logic_model(): Creates and configures the fuzzy logic model.
• compare_codes(code1, code2): Main function to compare two code snippets.
• classify_similarity(score): Classifies the similarity score into categories.

Acknowledgments

• Skfuzzy for fuzzy logic implementation.
• Numpy for numerical operations.

License

This project is licensed under the MIT License - see the LICENSE file for details.