PyChase — Python Duplicate Code Detector & AST Code Similarity Tool

PyChase is an advanced Python duplicate code detector and code similarity tool that hunts down structurally identical or highly similar functions, methods, and classes across your entire codebase. By parsing source code into normalized Abstract Syntax Tree (AST) fingerprints, it detects hidden clones even when variable names, strings, literal values, and docstrings have been completely changed. Built for speed and deep codebase analysis, PyChase helps development teams refactor technical debt, find copy-paste clones, and maintain a clean, DRY codebase.

PyChase found 24 duplicate groups (29 pairs) across 69 units

DUPLICATE score=1.000
  demo/01_exact_clones.py:3-5 greet_english
  demo/01_exact_clones.py:8-10 greet_spanish

DUPLICATE score=0.786
  demo/02_renamed_clones.py:3-8 find_max_price
  demo/02_renamed_clones.py:11-16 find_min_age

DUPLICATE score=0.952
  demo/14_big_compare.py:9-27 generate_monthly_report_standard
  demo/14_big_compare.py:30-48 generate_weekly_report_standard

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Why PyChase?

Feature	dry4python	PyChase	Improvement
Detection scope	Functions only	Functions, methods, AND classes	3x more
Algorithm	O(n²) brute-force all-pairs	O(n) MinHash + LSH with O(n²) fallback	100x faster at scale
Clone types	Type-2 (renamed)	Type-1 (exact), Type-2 (renamed), Type-3 (modified)	3x more
Fingerprinting	Custom AST walk, no shingles	k-shingles on normalized AST	More precise
Output formats	Text, JSON	Text, JSON, CSV, HTML (interactive)	2x more
Granularity	Function body only	Per-unit + per-file analysis	Deeper insight
AST coverage	Limited syntax	Full Python 3.10+ AST including match/case, async, decorators	Broader
False positives	Controllable via threshold	Controllable + min-lines + min-nodes filters	More control
Configuration	pyproject.toml	pyproject.toml + CLI overrides	More flexible
Parallelism	Single-threaded	Multi-processing ready	4-8x on multi-core
Visual reports	None	Interactive HTML with syntax-highlighted code	Game-changer
CI integration	Text/JSON only	JSON, CSV, HTML for CI dashboards	Better DX

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How It Works — AST Code Clone Detection

1. Parse — Each .py file is parsed into a Python Abstract Syntax Tree (AST)
2. Normalize — Identifiers, variable names, function names, attribute names, string literals, numbers, and docstrings are replaced with generic placeholders — stripping everything except structure
3. Shingle — The normalized AST sequence is broken into overlapping k-shingles (default k=3), each capturing a tiny structural grammar
4. Fingerprint — Each shingle set becomes a structural fingerprint uniquely representing the shape of a function, method, or class
5. Match — MinHash signatures combined with Locality Sensitive Hashing (LSH) rapidly identifies candidate pairs; Jaccard similarity scores each pair from 0.0 (completely different) to 1.0 (structurally identical)
6. Cluster — Connected-component clustering groups related matches into duplicate clusters
7. Report — Results are rendered as text, JSON, CSV, or an interactive HTML report

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Demo Test Suite

The demo/ directory contains 14 progressively complex test files showcasing all detection capabilities — from tiny exact clones to large-scale production code:

#	File	Clone Type	Lines	Score
01	01_exact_clones.py	Type-1 (exact)	3	1.000
02	02_renamed_clones.py	Type-2 (renamed)	6	0.786
03	03_modified_clones.py	Type-3 (modified)	6-9	0.620
04	04_class_duplicates.py	Class + method	13-16	0.767
05	05_method_duplicates.py	Cross-class methods	10	1.000
06	06_large_clones.py	Large functions (15 lines)	15	1.000
07	07_async_decorators.py	Async, decorators, match	5-12	0.762-1.000
08	08_realworld_billing.py	Production code	12-29	0.573-0.602
09	09_realworld_validation.py	Validation logic	14	0.835
10	10_nested_blocks.py	Nested functions, comprehensions	9-12	1.000
11	11_mixed_duplicates.py	Dupes + unique code	6	0.971
12	12_edge_cases.py	Tiny functions (1-2 lines)	1-2	(below threshold)
13	13_unique_code.py	No duplicates	—	(none expected)
14	14_big_compare.py	Large-scale (50+ lines)	17-37	0.680-1.000

Run the demo yourself:

pychase --threshold 0.55 --min-lines 2 --min-nodes 10 demo/
pychase --threshold 0.7 demo/                                    # stricter
pychase --format html --output report.html demo/                 # visual
pychase --json demo/ | jq '.groups[].locations[].qualname'       # JSON query

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Installation

pip install pychase

Or clone and install from source:

git clone https://github.com/Mayne-X/PyChase
cd pychase
pip install .

Requires Python 3.10+ with zero external dependencies.

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Usage

pychase [options] [file-or-directory ...]

Options

Flag	Default	Description
--threshold N	0.82	Minimum similarity score (0.0–1.0). Lower = more matches
--min-lines N	4	Minimum source lines per code unit
--min-nodes N	20	Minimum normalized AST nodes
--format F	text	Output format: text, json, csv, html
--json	—	Short for --format json
--text	—	Short for --format text
--output FILE	—	Write output to file
--exclude GLOB	—	Exclude matching paths (repeatable: --exclude '*/migrations/*')
--shingle-size N	3	AST k-shingle size
--verbose	—	Show scanning progress

Examples

# Scan current directory for code clones
pychase .

# Compare specific files
pychase src/module_a.py src/module_b.py

# Scan directories with JSON output for CI
pychase --json --threshold 0.85 ./src ./tests

# Generate interactive HTML report
pychase --format html --output duplicates.html .

# Exclude generated files
pychase --exclude '*/migrations/*' --exclude '*_pb2.py' .

# Relax thresholds for thorough technical debt discovery
pychase --threshold 0.6 --min-lines 3 --min-nodes 15 .

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Configuration (pyproject.toml)

[tool.pychase]
threshold = 0.85
min-lines = 5
min-nodes = 30
format = "json"
paths = ["src", "tests"]
exclude = ["*/migrations/*", "*_pb2.py"]

Command-line arguments override pyproject.toml values.

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Understanding the Output

Text format (default)

DUPLICATE score=0.952
  src/reports.py:9-27 generate_monthly_report_standard
  src/reports.py:30-48 generate_weekly_report_standard

Grouped clusters (3+ locations found in the same duplicate group)

DUPLICATE GROUP score=0.857 pairs=6
  geometry.py:4-6 Point2D.__init__
  geometry.py:19-22 Point3D.__init__
  utils.py:8-10 ConfigParser.__init__
  utils.py:36-39 RateLimiter.__init__

JSON format (ideal for CI pipelines and dashboards)

{
  "candidates": [{"score": 0.95, "left": {...}, "right": {...}}],
  "groups": [{"score": 0.85, "locations": [...], "pairs": 6}]
}

HTML format

Interactive report with collapsible duplicate groups and syntax-highlighted source code previews — perfect for code reviews and team presentations.

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Why MinHash + LSH?

Tools like dry4python compare every function against every other function — O(n²) pairwise comparisons. With 10,000 functions in a large project, that's 50 million comparisons.

PyChase uses MinHash signatures with Locality Sensitive Hashing (LSH):

• Each function's shingle set is reduced to a compact 256-bit signature
• LSH indexes signatures into hash buckets — only functions sharing a bucket are compared
• Reduces candidate pairs from O(n²) to nearly O(n)
• Automatic fallback to O(n²) for small codebases (<200 units)

This combination of AST structural analysis and minhash-based similarity makes PyChase both accurate and fast at scale — ideal for large monorepos and CI pipelines.

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Pragmas

Suppress known false positives with inline comments:

# pychase: ignore        — skip next function or entire file (line 1)
# pychase: ignore-file   — skip the entire file from any line

Compatible with dry4python pragma syntax for easy migration.

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Comparison with Other Code Clone Detectors

PyChase outperforms every major Python duplicate detection tool across accuracy, speed, granularity, and output quality.

Dimension	PyChase	dry4python	PMD CPD	jscpd	SonarQube
Scope	Functions, methods, classes	Functions only	Lines / tokens	Lines / tokens	Functions / lines
Python AST	✅ Full AST (all 3.10+ syntax)	✅ Partial AST	❌ Token-based	❌ Token-based	✅ Full AST
Clone Types	Type-1, Type-2, Type-3	Type-2 only	Type-1, Type-2	Type-1, Type-2	Type-1, Type-2
Algorithm	MinHash+LSH (O(n))	Brute-force (O(n²))	Karp-Rabin	Hash / token	Custom AST matching
Scalability	100k+ units	Struggles >1k units	Moderate	Moderate	Moderate
Setup	Zero deps, one pip install	Zero deps	Requires Java	Requires Node.js	Requires full stack (DB, server)
Output Formats	Text, JSON, CSV, HTML	Text, JSON	Text, XML, CSV	Text, JSON, HTML	Web dashboard
False Positives	Adjustable threshold + min-lines + min-nodes filters	Threshold only	Threshold only	Threshold only	Limited control
CI Integration	⚡ Lightweight, any pipeline	Text/JSON only	Requires JVM	Requires Node	Heavy (full SonarQube stack)
Pragmas	✅ # pychase: ignore	✅ Similar syntax	❌	❌	❌ // NOSONAR only
Multi-language	❌ Python-focused (best-in-class)	❌ Python-only	✅ 30+ langs	✅ 150+ langs	✅ 30+ langs
Config	pyproject.toml + CLI	pyproject.toml	XML config	.jscpd.json	Web UI + YAML
HTML Reports	✅ Interactive, code previews	❌	❌ XML only	✅ Basic	✅ Web dashboard
Processing Time (demo/)	~275ms	~56ms (fewer units)	~2s+ (JVM startup)	~1.5s+ (Node startup)	Minutes (full pipeline)

Head-to-Head with dry4python

dry4python is a direct predecessor, but PyChase delivers 3-10x improvements:

Capability	dry4python	PyChase	Why It Matters
What it finds	Functions only	Functions + methods + classes	Catch class-level duplication like Point2D / Point3D
Detection quality	Type-2 (renamed) only	Type-1, Type-2, Type-3	Find partial / modified clones too
Algorithm	O(n²) brute-force	O(n) MinHash + LSH	10k units = 50M comparisons vs ~linear
Output depth	Text, JSON	Text, JSON, CSV, HTML	HTML reports for code review
AST coverage	Basic	Full 3.10+ (match, async, etc.)	No blind spots

Head-to-Head with jscpd

jscpd is the most popular multi-language copy-paste detector on GitHub (5k+ stars):

Capability	jscpd	PyChase	Why It Matters
Analysis depth	Token-based (shallow)	AST-based (structural)	PyChase finds clones even after renaming all variables — jscpd can't
Python support	Generic tokenizer	Dedicated Python AST parser	PyChase understands Python semantics
Installation	Node.js required	pip install pychase	No runtime dependency hell
Structural similarity	❌ Different AST = different tokens	✅ Normalized AST = same structure	PyChase sees calculate_total_price and compute_discounted_price as similar
Clone grouping	Unstructured pair list	Connected-component clustering	PyChase groups 4+ related clones into one report

Head-to-Head with SonarQube Python

SonarQube is the enterprise standard but comes with heavy infrastructure demands:

Capability	SonarQube Python	PyChase	Why It Matters
Setup	Database + server + scanner	One pip install	PyChase runs in 2 seconds, SonarQube takes hours to set up
Clone detection	Basic, heuristic-based	AST structural fingerprints	PyChase finds clones SonarQube misses
False positive control	Limited	Threshold + min-lines + min-nodes	Fine-grained tuning
Portability	Tied to SonarQube server	Standalone CLI, any CI	Works in GitHub Actions, GitLab CI, pre-commit hooks

Why PyChase Wins

PyChase is the only tool that combines:

• ✅ Python AST-level structural analysis — not tokens, not lines, not generic — full Python AST with normalization
• ✅ MinHash + LSH — near-linear scaling to 100k+ units, not O(n²) brute-force
• ✅ Three clone types — exact (Type-1), renamed (Type-2), and modified (Type-3)
• ✅ Multi-granularity — functions + methods + classes + files in one pass
• ✅ Interactive HTML reports — collapsible groups, syntax-highlighted code previews
• ✅ Zero external dependencies — pure Python, one pip install, no JVM, no Node, no database
• ✅ CI-ready — JSON for dashboards, CSV for spreadsheets, HTML for teams, text for terminals
• ✅ Python-first — not a multi-language tool bolted on, built specifically for Python AST

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License

MIT