DupHunter

AST-driven near-duplicate code detector for Python. Finds copy-paste clones even when variables are renamed. Built for AI-assisted refactoring workflows.

$ duphunter-near-dup src/ --output text --threshold 0.76

src/api/users.py:45  get_user_by_id
src/api/orders.py:89 get_order_by_id
  similarity: 0.91 | 12 lines each | 3 AST diffs

Why?

Linters catch style issues. Type checkers catch type errors. DupHunter catches structural duplication — the kind that leads to bugs when you fix something in one copy but forget the other.

• AST normalization: Renames local variables to positional placeholders, so get_user_by_id and get_order_by_id match even with different parameter names
• MinHash + LSH indexing: Scales to large codebases without O(N^2) pairwise comparison
• Literal normalization: Finds "same logic, different data" clones (strings, numbers normalized)
• Cluster output: Groups transitive duplicates — if AB and BC, reports {A, B, C}
• MCP server: AI assistants can search for duplicates directly

Install

# Global install (recommended)
pipx install duphunter

# Or with pip
pip install duphunter

Quick Start

CLI

# Scan entire project
duphunter-near-dup .

# Focused search: "what's similar to the function at line 120?"
duphunter-near-dup . --query src/app.py:120 --top 20

# Human-readable output
duphunter-near-dup . --output text

# Cluster transitive duplicates
duphunter-near-dup . --cluster --output text

# Adjust sensitivity
duphunter-near-dup . --threshold 0.72          # more results, looser matching
duphunter-near-dup . --sensitivity high        # catch small 4-5 line helpers

# Ignore known patterns
duphunter-near-dup . --exclude-functions "test_*,setup_*"
duphunter-near-dup . --exclude-functions "re:^get_.*_by_.*$"

MCP Server (for AI assistants)

Add to ~/.claude.json or .mcp.json:

{
  "mcpServers": {
    "duphunter": {
      "command": "duphunter-mcp"
    }
  }
}

MCP tool: search_near_duplicates — accepts the same options as CLI flags (as JSON fields), returns structured match payload. Default sensitivity is high in MCP mode.

How It Works

1. Parse: Python AST extracts all function and class bodies
2. Normalize: Local identifiers → positional placeholders (arg0, local1), literals → type tokens
3. Fingerprint: Normalized AST tokens → MinHash signatures (128 permutations)
4. Index: LSH bands for fast candidate retrieval
5. Compare: Candidate pairs scored by Jaccard similarity on AST token shingles
6. Report: Pairs above threshold, optionally clustered by transitivity

Ignore Rules

Filter out known wrappers, test helpers, or generated code:

Format	Example	Matches
Exact name	approve_suggestion	Only that function
Glob pattern	get_*_by_*	get_user_by_id, get_order_by_name, ...
Regex	re:^test_.*_integration$	test_api_integration, test_db_integration, ...

Multiple rules: comma-separated or repeated flags.

Recommended Defaults

Use case	Command
General scan	duphunter-near-dup . --threshold 0.76
Refactoring prep	duphunter-near-dup . --query FILE:LINE --top 20
Catch small helpers	duphunter-near-dup . --sensitivity high
CI integration	duphunter-near-dup . --threshold 0.85 --output json

Requirements

• Python 3.10+
• No external dependencies (stdlib only — uses ast, hashlib, collections)

License

MIT