Theory & Background

Tokenize source code (remove whitespace/comments, convert identifiers to generic tokens: INT, ID, OP, etc.). Calculate Longest Common Subsequence (LCS) of token sequences. Similarity = 2 × LCS_length / (tokens_A + tokens_B). This is the core of JPlag and MOSS algorithms. Advantage: token normalization makes superficial changes (variable renaming, comment adding, whitespace changes) transparent — only structural changes matter.

Components & Requirements

10 components required for this project.

#	Component	Purpose	Qty
1	Python 3.10+	Implementation language	x1
2	ast module (stdlib)	Python AST parsing	x1
3	javalang (Java parser)	Java code parsing	x1
4	sentence-transformers	Semantic code embeddings	x1
5	difflib (stdlib)	Sequence similarity (SequenceMatcher)	x1
6	Flask	Web interface for submissions	x1
7	CodeBERT (HuggingFace)	Code-aware embeddings	x1
8	Matplotlib + NetworkX	Similarity visualization graph	x1
9	Celery + Redis	Async batch comparison	x1
10	MongoDB	Submission and result storage	x1

Step-by-Step Implementation

Follow these 4 steps carefully.

1

Token-Based Similarity (JPlag Method)

Tokenize source code (remove whitespace/comments, convert identifiers to generic tokens: INT, ID, OP, etc.). Calculate Longest Common Subsequence (LCS) of token sequences. Similarity = 2 × LCS_length / (tokens_A + tokens_B). This is the core of JPlag and MOSS algorithms. Advantage: token normalization makes superficial changes (variable renaming, comment adding, whitespace changes) transparent — only structural changes matter.

2

AST-Based Structural Analysis

Parse code to AST. Compare AST structures using tree edit distance: minimum operations (insert, delete, relabel node) to transform tree A into tree B. Lower edit distance = higher similarity. Hash AST subtrees using Merkle hashing — identical subtrees produce identical hashes. Count matching subtrees. Advantage over token comparison: captures code structure independent of textual representation.

3

Semantic Similarity with CodeBERT

CodeBERT is pre-trained on code-text pairs. Generate code embeddings: tokenize code, pass through CodeBERT, use [CLS] token embedding as code vector. Cosine similarity between code vectors measures semantic similarity. This captures algorithmic intent even when implementation differs significantly. Example: recursive and iterative factorial implementations are functionally equivalent — cosine similarity > 0.85 despite syntactic differences.

4

Multi-Technique Fusion

Combine all three techniques for robust detection: token similarity (40% weight), AST structural similarity (35% weight), semantic embedding similarity (25% weight). Final score = 0.4×token + 0.35×AST + 0.25×semantic. Flag pairs above threshold (0.7) for review. Present evidence: highlighted matching token sequences, highlighted matching AST subtrees, and a diff view showing structural changes between suspicious submissions.

Code & Implementation

Core code for plagiarism_detector.py:

plagiarism_detector.py
  Python

Testing & Troubleshooting

Test Code Plagiarism Detection System by verifying each subsystem individually before full integration.

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Troubleshooting Tips

Verify power voltages, check ground connections, use serial monitor for debug.

Real-World Applications

*Academic integrity enforcement in courses

*Open-source license violation detection

*Code review similarity alerts in enterprise

*Software patent prior art analysis

*CTF (Capture The Flag) plagiarism detection

*Homework grading automation

*Competitive programming submission fairness

*Code review plagiarism in competitive submissions

Extensions & Next Steps

Add cross-language plagiarism detection (Python vs Java)
Implement incremental detection for large repositories
Build a visualization showing exactly which code blocks match
Add API for integration with LMS (Moodle, Canvas)
Implement watermarking to track code lineage

Interactive Playground

Coming Soon

An interactive simulator will be available here — simulate circuits and run code in-browser without hardware.

Frequently Asked Questions

How do students commonly try to evade plagiarism detectors?

Common obfuscation techniques: variable renaming (token normalization defeats this), adding/removing blank lines and comments (defeated by tokenization), reordering independent code blocks (detected by AST analysis), converting between loops (for ↔ while — AST handles this partially), translating between languages (CodeBERT semantic embeddings partially detect this), inserting dead code (token density analysis detects padding), and using AI to paraphrase code (hardest to detect — semantic embeddings help).

Introduction