Code Plagiarism Detector

Note: not all files could be uploaded due to file size limmitations on GitHub.

Overview Academic dishonesty in programming courses is evolving. Students now disguise plagiarism with methods like variable renaming, loop/recursion swaps, or library substitution.
This project builds a multilayered AI system that catches plagiarism at different levels:

• Token similarity → compare tokens (words, numbers, characters)
• Semantic similarity → compare code logic
• Output similarity → Compare program/snippet outputs across test cases
• Ensemble layer → Combine all three above into one classifier

Final product: a program where a user can upload/paste two Python files and receive a plagiarism risk score + breakdown.

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How It Works

1. Token Check → TF-IDF vectorization of code, take cosine similarity.
2. Semantic Check → CodeBERT embeddings to capture deeper logic similarity, take cosine similarity.
3. Output Check → Run both snippets on sandboxed inputs and compare outputs, take (# of matching outputs) / (total test cases) as score.
4. Meta Classifier → Combine the above similarity scores into one feature vector and feed into MLP. MLP outputs final classification.

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Development Environment Setup

Requirements

• Python 3.9+
• 1 NVIDIA GPU

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Core Libraries installation

Paste in terminal: pip install pandas scikit-learn torch transformers scipy streamlit

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Dataset Access

The Stack (HuggingFace)

Synthetic plagiarized pairs will be created for training labels. 50/50 ratio of plaigiarized and non-plagiarized pairs.

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Git / Repo Workflow

Clear branch naming. eg. feature/token-sim, bugfix/output-check, experiment/codebert-v2

Commits: Use imperative mood (e.g., Add output checker).

Pull Requests: Must pass tests + review by 1 other dev.

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Behavioural

Show up prepared to weekly syncs.

Push reproducible code (scripts + requirements).

Document experiments so others can replicate.

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Naming Convention

Use snake case for file names.

Camel case for everything else (variables, classes, etc).

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Acceptance Criteria

≥ 85% accuracy on held-out test pairs (plagiarised and non-plagiarised pairs)

Detect ≥ 70% of difficult cases (renaming, reordering, recursion vs iteration, built in vs self made functions)

Runtime ≤ 5s per comparison on a laptop

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Repo Structure

code_plagiarism_detector/
├── data/              # datasets, generated pairs
├── models/            # saved model weights
├── src/
│   ├── token_sim.py        # token similarity (TF-IDF + cosine)
│   ├── semantic_sim.py     # semantic similarity (CodeBERT)
│   ├── output_check.py     # output similarity (sandboxed execution)
│   ├── ensemble.py         # combines token, semantic, output scores
│   └── utils/              # helper functions
├── ui/                # Front End
├── tests/             # unit + integration tests
├── docs/              # documentation, experiment logs
├── requirements.txt   # project dependencies
└── README.md