Design Pattern Detection Guide

Design Pattern Detection Guide

Feature: C3.1 - Detect common design patterns in codebases Version: 2.6.0+ Status: Production Ready ✅

Table of Contents

• Overview
• Supported Patterns
• Detection Levels
• Usage
  • CLI Usage
  • Codebase Scraper Integration
  • MCP Tool
  • Python API
• Language Support
• Output Format
• Examples
• Accuracy

Overview

The pattern detection feature automatically identifies common design patterns in your codebase across 9 programming languages. It uses a three-tier detection system (surface/deep/full) to balance speed and accuracy, with language-specific adaptations for better precision.

Key Benefits:

• 🔍 Understand unfamiliar code - Instantly identify architectural patterns
• 📚 Learn from good code - See how patterns are implemented
• 🛠️ Guide refactoring - Detect opportunities for pattern application
• 📊 Generate better documentation - Add pattern badges to API docs

Supported Patterns

Creational Patterns (3)

1. Singleton - Ensures a class has only one instance
2. Factory - Creates objects without specifying exact classes
3. Builder - Constructs complex objects step by step

Structural Patterns (2)

4. Decorator - Adds responsibilities to objects dynamically
5. Adapter - Converts one interface to another

Behavioral Patterns (5)

6. Observer - Notifies dependents of state changes
7. Strategy - Encapsulates algorithms for interchangeability
8. Command - Encapsulates requests as objects
9. Template Method - Defines skeleton of algorithm in base class
10. Chain of Responsibility - Passes requests along a chain of handlers

Detection Levels

Surface Detection (Fast, ~60-70% Confidence)

• How: Analyzes naming conventions
• Speed: <5ms per class
• Accuracy: Good for obvious patterns
• Example: Class named "DatabaseSingleton" → Singleton pattern

skill-seekers-patterns --file db.py --depth surface

Deep Detection (Balanced, ~80-90% Confidence) ⭐ Default

• How: Structural analysis (methods, parameters, relationships)
• Speed: ~10ms per class
• Accuracy: Best balance for most use cases
• Example: Class with getInstance() + private constructor → Singleton

skill-seekers-patterns --file db.py --depth deep

Full Detection (Thorough, ~90-95% Confidence)

• How: Behavioral analysis (code patterns, implementation details)
• Speed: ~20ms per class
• Accuracy: Highest precision
• Example: Checks for instance caching, thread safety → Singleton

skill-seekers-patterns --file db.py --depth full

Usage

CLI Usage

# Single file analysis
skill-seekers-patterns --file src/database.py

# Directory analysis
skill-seekers-patterns --directory src/

# Full analysis with JSON output
skill-seekers-patterns --directory src/ --depth full --json --output patterns/

# Multiple files
skill-seekers-patterns --file src/db.py --file src/api.py

CLI Options:

• --file - Single file to analyze (can be specified multiple times)
• --directory - Directory to analyze (all source files)
• --output - Output directory for JSON results
• --depth - Detection depth: surface, deep (default), full
• --json - Output JSON format
• --verbose - Enable verbose output

Codebase Scraper Integration

The --detect-patterns flag integrates with codebase analysis:

# Analyze codebase + detect patterns
skill-seekers analyze --directory src/ --detect-patterns

# With other features
skill-seekers analyze \
  --directory src/ \
  --detect-patterns \
  --build-api-reference \
  --build-dependency-graph

Output: output/codebase/patterns/detected_patterns.json

MCP Tool

For Claude Code and other MCP clients:

# Via MCP
await use_mcp_tool('detect_patterns', {
    'file': 'src/database.py',
    'depth': 'deep'
})

# Directory analysis
await use_mcp_tool('detect_patterns', {
    'directory': 'src/',
    'output': 'patterns/',
    'json': true
})

Python API

from skill_seekers.cli.pattern_recognizer import PatternRecognizer

# Create recognizer
recognizer = PatternRecognizer(depth='deep')

# Analyze file
with open('database.py', 'r') as f:
    content = f.read()

report = recognizer.analyze_file('database.py', content, 'Python')

# Print results
for pattern in report.patterns:
    print(f"{pattern.pattern_type}: {pattern.class_name} (confidence: {pattern.confidence:.2f})")
    print(f"  Evidence: {pattern.evidence}")

Language Support

Language-Specific Adaptations:

• Python: Detects @decorator syntax, __new__ singletons
• JavaScript: Recognizes module pattern, EventEmitter
• Java/C#: Identifies interface-based patterns
• Go: Detects sync.Once singleton idiom
• Rust: Recognizes lazy_static, trait adapters

Output Format

Human-Readable Output

============================================================
PATTERN DETECTION RESULTS
============================================================
Files analyzed: 15
Files with patterns: 8
Total patterns detected: 12
============================================================

Pattern Summary:
  Singleton: 3
  Factory: 4
  Observer: 2
  Strategy: 2
  Decorator: 1

Detected Patterns:

src/database.py:
  • Singleton - Database
    Confidence: 0.85
    Category: Creational
    Evidence: Has getInstance() method

  • Factory - ConnectionFactory
    Confidence: 0.70
    Category: Creational
    Evidence: Has create() method

JSON Output (--json)

{
  "total_files_analyzed": 15,
  "files_with_patterns": 8,
  "total_patterns_detected": 12,
  "reports": [
    {
      "file_path": "src/database.py",
      "language": "Python",
      "patterns": [
        {
          "pattern_type": "Singleton",
          "category": "Creational",
          "confidence": 0.85,
          "location": "src/database.py",
          "class_name": "Database",
          "method_name": null,
          "line_number": 10,
          "evidence": [
            "Has getInstance() method",
            "Private constructor detected"
          ],
          "related_classes": []
        }
      ],
      "total_classes": 3,
      "total_functions": 15,
      "analysis_depth": "deep",
      "pattern_summary": {
        "Singleton": 1,
        "Factory": 1
      }
    }
  ]
}

Examples

Example 1: Singleton Detection

# database.py
class Database:
    _instance = None

    def __new__(cls):
        if cls._instance is None:
            cls._instance = super().__new__(cls)
        return cls._instance

    def connect(self):
        pass

Command:

skill-seekers-patterns --file database.py

Output:

Detected Patterns:

database.py:
  • Singleton - Database
    Confidence: 0.90
    Category: Creational
    Evidence: Python __new__ idiom, Instance caching pattern

Example 2: Factory Pattern

# vehicle_factory.py
class VehicleFactory:
    def create_vehicle(self, vehicle_type):
        if vehicle_type == 'car':
            return Car()
        elif vehicle_type == 'truck':
            return Truck()
        return None

    def create_bike(self):
        return Bike()

Output:

  • Factory - VehicleFactory
    Confidence: 0.80
    Category: Creational
    Evidence: Has create_vehicle() method, Multiple factory methods

Example 3: Observer Pattern

# event_system.py
class EventManager:
    def __init__(self):
        self.listeners = []

    def attach(self, listener):
        self.listeners.append(listener)

    def detach(self, listener):
        self.listeners.remove(listener)

    def notify(self, event):
        for listener in self.listeners:
            listener.update(event)

Output:

  • Observer - EventManager
    Confidence: 0.95
    Category: Behavioral
    Evidence: Has attach/detach/notify triplet, Observer collection detected

Accuracy

Benchmark Results

Tested on 100 real-world Python projects with manually labeled patterns:

Key Insights:

• Observer pattern has highest accuracy (event-driven code has clear signatures)
• Chain of Responsibility has lowest (similar to middleware/filters)
• Python AST-based analysis provides +10-15% accuracy over regex-based
• Language adaptations improve confidence by +5-10%

Known Limitations

1. False Positives (~13%):

   • Classes named "Handler" may be flagged as Chain of Responsibility
   • Utility classes with create* methods flagged as Factories
   • Mitigation: Use --depth full for stricter checks

2. False Negatives (~20%):

   • Unconventional pattern implementations
   • Heavily obfuscated or generated code
   • Mitigation: Provide clear naming conventions

3. Language Limitations:

   • Regex-based languages have lower accuracy than Python
   • Dynamic languages harder to analyze statically
   • Mitigation: Combine with runtime analysis tools

Integration with Other Features

API Reference Builder (Future)

Pattern detection results will enhance API documentation:

## Database Class

**Design Pattern**: 🏛️ Singleton (Confidence: 0.90)

The Database class implements the Singleton pattern to ensure...

Dependency Analyzer (Future)

Combine pattern detection with dependency analysis:

• Detect circular dependencies in Observer patterns
• Validate Factory pattern dependencies
• Check Strategy pattern composition

Troubleshooting

No Patterns Detected

Problem: Analysis completes but finds no patterns

Solutions:

1. Check file language is supported: skill-seekers-patterns --file test.py --verbose
2. Try lower depth: --depth surface
3. Verify code contains actual patterns (not all code uses patterns!)

Low Confidence Scores

Problem: Patterns detected with confidence <0.5

Solutions:

1. Use stricter detection: --depth full
2. Check if code follows conventional pattern structure
3. Review evidence field to understand what was detected

Performance Issues

Problem: Analysis takes too long on large codebases

Solutions:

1. Use faster detection: --depth surface
2. Analyze specific directories: --directory src/models/
3. Filter by language: Configure codebase scraper with --languages Python

Future Enhancements (Roadmap)

• C3.6: Cross-file pattern detection (detect patterns spanning multiple files)
• C3.7: Custom pattern definitions (define your own patterns)
• C3.8: Anti-pattern detection (detect code smells and anti-patterns)
• C3.9: Pattern usage statistics and trends
• C3.10: Interactive pattern refactoring suggestions

Technical Details

Architecture

PatternRecognizer
├── CodeAnalyzer (reuses existing infrastructure)
├── 10 Pattern Detectors
│   ├── BasePatternDetector (abstract class)
│   ├── detect_surface() → naming analysis
│   ├── detect_deep() → structural analysis
│   └── detect_full() → behavioral analysis
└── LanguageAdapter (language-specific adjustments)

Performance

• Memory: ~50MB baseline + ~5MB per 1000 classes
• Speed:
  • Surface: ~200 classes/sec
  • Deep: ~100 classes/sec
  • Full: ~50 classes/sec

Testing

• Test Suite: 24 comprehensive tests
• Coverage: All 10 patterns + multi-language support
• CI: Runs on every commit

References

• Gang of Four (GoF): Design Patterns book
• Pattern Categories: Creational, Structural, Behavioral
• Supported Languages: 9 (Python, JavaScript, TypeScript, C++, C, C#, Go, Rust, Java)
• Implementation: src/skill_seekers/cli/pattern_recognizer.py (~1,900 lines)
• Tests: tests/test_pattern_recognizer.py (24 tests, 100% passing)

Status: ✅ Production Ready (v2.6.0+) Next: Start using pattern detection to understand and improve your codebase!