Hybrid Code Clone Detection System | AI-Powered Code Similarity Analysis

Developed a full-stack web application that identifies duplicated and logically similar code using a hybrid detection engine combining traditional algorithms with deep learning. The system helps developers and educators identify code duplication, assess similarity, and improve software maintainability.

Key Features

Type-1 Detection — Textual Similarity: Identifies exact or near-exact code matches using difflib, with comments and unnecessary whitespace removed.

Type-2 Detection — Token Similarity: Normalizes identifiers and variable names to detect copied code despite renaming and formatting changes.

Type-3 Detection — Structural Similarity: Extracts control-flow keywords and structural patterns to identify code with added, removed, or modified statements.

Type-4 Detection — Semantic Similarity: Uses CodeBERT embeddings and cosine similarity to identify logically equivalent code with different implementations.

Hybrid Similarity Scoring: Combines the four detection scores using weighted aggregation to produce an overall similarity percentage.

Interactive Visualizations: Displays similarity comparisons through bar charts and radar charts using Chart.js.

Performance Metrics: Reports individual algorithm execution times alongside similarity scores.

Robust Processing: Includes input validation, exception handling, and a fallback mechanism when the semantic model is unavailable.

Web-Based Interface: Provides a neon-themed interface for entering two code snippets and viewing detailed analysis results.

Tech Stack: Python, Flask, CodeBERT, Hugging Face Transformers, HTML, CSS, JavaScript, Chart.js, and REST APIs.

Applications: Code duplication analysis, academic plagiarism screening, software quality assurance, code review, and software maintenance.