solid-checker 1.0.0

Smart SOLID principles analyzer with context-aware scoring and adaptive recommendations

🔍 SOLID Checker - Smart SOLID Analysis Tool

A comprehensive, intelligent Python tool for analyzing code compliance with SOLID principles, featuring adaptive contextual analysis that adjusts evaluation criteria based on project type and complexity.

🌟 Key Features

🧠 Smart Contextual Analysis

• Automatic Project Type Detection: Identifies whether your code is a simple script, utility module, library, or large application
• Adaptive Scoring: Adjusts SOLID principle weights based on project characteristics
• Context-Aware Thresholds: Different quality expectations for different project types
• Intelligent Recommendations: Tailored advice that prevents over-engineering simple code while demanding excellence from libraries

📊 Comprehensive SOLID Analysis

• SRP (Single Responsibility Principle): Analyzes class responsibilities and method focus
• OCP (Open/Closed Principle): Detects extensibility violations and type checking patterns
• LSP (Liskov Substitution Principle): Validates inheritance hierarchies and contracts
• ISP (Interface Segregation Principle): Identifies interface bloat and segregation issues
• DIP (Dependency Inversion Principle): Finds tight coupling and dependency issues

🎯 Advanced Scoring System

• Weighted Scoring: Different emphasis on principles based on project needs
• Complexity Analysis: Evaluates project size, class count, and architectural complexity
• Color-Coded Results: Visual indicators with adaptive thresholds
• Detailed Violation Reports: Specific issues with actionable insights

📈 Multiple Output Formats

• Interactive Reports: Rich console output with emoji indicators and detailed breakdowns
• JSON Export: Machine-readable format for CI/CD integration and further analysis
• Detailed Analysis: In-depth violation analysis with specific recommendations

🚀 Quick Start

Installation

pip install colid-checker

Basic Usage

# Smart analysis (default) - automatically adapts to your project type
python solid-checker your_file.py

# Detailed report with violation breakdown
python solid-checker --report your_file.py

# JSON output for integration with other tools
python solid-checker --json your_file.py > analysis.json

# Legacy analysis (classic SOLID scoring without adaptation)
python solid-checker --legacy your_file.py

🧠 Smart Analysis in Action

The tool automatically adapts its analysis based on your project characteristics:

Simple Scripts (< 100 lines, 1-2 classes)

🧠 Smart Analysis:
   📋 Project Type: Simple Script
   📊 Complexity Score: 0.0/1.00
   
⚖️ Adaptive Weights:
   📋 SRP: 40.0% | 🔓 OCP: 10.0% | 🔄 LSP: 10.0%
   🎯 ISP: 10.0% | 🔄 DIP: 30.0%

Focus: Simplicity and single purpose. Lenient thresholds to avoid over-engineering.

Libraries/Frameworks

🧠 Smart Analysis:
   📋 Project Type: Library Framework
   📊 Complexity Score: 0.7/1.00
   
⚖️ Adaptive Weights:
   📋 SRP: 20.0% | 🔓 OCP: 30.0% | 🔄 LSP: 20.0%
   🎯 ISP: 25.0% | 🔄 DIP: 5.0%

Focus: Extensibility (OCP) and clean interfaces (ISP). Strict thresholds for public APIs.

Large Applications

🧠 Smart Analysis:
   📋 Project Type: Large Application
   📊 Complexity Score: 0.8/1.00
   
⚖️ Adaptive Weights:
   📋 SRP: 30.0% | 🔓 OCP: 25.0% | 🔄 LSP: 20.0%
   🎯 ISP: 15.0% | 🔄 DIP: 10.0%

Focus: Maintainability (SRP) and extensibility (OCP). Strict compliance for long-term success.

📋 CLI Options

Option	Description
file	Python file to analyze
--report	Show detailed violation analysis
--json	Output results in JSON format
--verbose	Enable verbose error output
--smart	Use smart contextual analysis (default)
--legacy	Use classic analysis without adaptation
--no-smart-info	Hide smart analysis information in report

📊 Understanding the Output

Smart Analysis Section

🧠 Smart Analysis:
   📋 Project Type: Small App
   📊 Complexity Score: 0.30/1.00
   📈 Lines of Code: 589
   🏗️ Classes: 8

⚖️ Adaptive Weights (for this project type):
   📋 SRP: 25.0% | 🔓 OCP: 20.0% | 🔄 LSP: 20.0%
   🎯 ISP: 15.0% | 🔄 DIP: 20.0%

SOLID Scores

📊 SOLID Scores (with context-aware thresholds):
   📋 SRP: 0.62/1.00 🟡
   🔓 OCP: 0.50/1.00 🔴
   🔄 LSP: 1.00/1.00 🟢
   🎯 ISP: 1.00/1.00 🟢
   🔄 DIP: 0.57/1.00 🔴

Color Indicators:

• 🟢 Green: Excellent compliance (above "good" threshold)
• 🟡 Yellow: Acceptable compliance (above "acceptable" threshold)
• 🔴 Red: Needs improvement (below "acceptable" threshold)

Note: Thresholds adapt based on project type

🎯 Project Types & Adaptive Behavior

1. Simple Script

• Characteristics: < 100 lines, 1-2 classes
• Focus: Single purpose, avoid over-engineering
• Thresholds: Lenient (Good: 0.6, Acceptable: 0.4)
• Weight Emphasis: SRP (40%), DIP (30%)

2. Utility Module

• Characteristics: 100-500 lines, few classes
• Focus: Balance simplicity with reusability
• Thresholds: Standard (Good: 0.8, Acceptable: 0.6)
• Weight Emphasis: Balanced approach

3. Small Application

• Characteristics: 500-1500 lines, moderate complexity
• Focus: Standard SOLID compliance
• Thresholds: Standard (Good: 0.8, Acceptable: 0.6)
• Weight Emphasis: Equal weighting

4. Large Application

• Characteristics: 1500+ lines, many classes
• Focus: Maintainability and structure
• Thresholds: Standard (Good: 0.8, Acceptable: 0.6)
• Weight Emphasis: SRP (30%), OCP (25%)

5. Library/Framework

• Characteristics: Public APIs, reusable components
• Focus: Extensibility and interface design
• Thresholds: Strict (Good: 0.9, Acceptable: 0.7)
• Weight Emphasis: OCP (30%), ISP (25%)

🛠️ Architecture

The tool follows a clean, modular architecture adhering to SOLID principles:

solid-checker/
├── __main__.py               # Main entry point
├── solid_scorer.py           # Main smart analysis engine
├── srp/                      # Single Responsibility Principle analyzer
│   ├── __init__.py
│   ├── core.py              # Core SRP analysis logic
│   ├── facade.py            # Simple API facade
│   └── protocols.py         # Type protocols and interfaces
├── ocp/                      # Open/Closed Principle analyzer
├── lsp/                      # Liskov Substitution Principle analyzer  
├── isp/                      # Interface Segregation Principle analyzer
├── dip/                      # Dependency Inversion Principle analyzer
└── README.md                # This documentation

Key Components

ProjectComplexityAnalyzer

Analyzes project characteristics to determine appropriate SOLID standards:

• Lines of code counting
• Class and method analysis
• Inheritance depth calculation
• External dependency tracking

AdaptiveWeightCalculator

Calculates context-aware weights and thresholds:

• Project type-based weight adjustment
• Quality threshold adaptation
• Complex project stricter requirements

ContextualRecommendationGenerator

Generates smart, targeted recommendations:

• Project type-specific advice
• Context-appropriate violation analysis
• Prevents over-engineering warnings

SOLIDScorer

Enhanced scorer with adaptive capabilities:

• Smart recommendation generation
• Contextual summary creation
• Weighted scoring with project awareness

📈 Example Analyses

Simple Python Script

#!/usr/bin/env python3
def main():
    name = input("Enter your name: ")
    print(f"Hello, {name}!")

if __name__ == "__main__":
    main()

Analysis Result:

🧠 Smart Analysis:
   📋 Project Type: Simple Script
   📊 Complexity Score: 0.0/1.00

🎯 Overall SOLID Score: 1.00/1.00
📋 Summary: Perfect script organization! 🎯

💡 Smart Recommendations:
   ✅ Excellent! Your script follows good practices while staying simple
   💡 Insight: This level of organization is perfect for scripts

🔧 Integration & CI/CD

JSON Output for Automation

python __main__.py --json my_code.py

Sample JSON output:

{
  "file_path": "my_code.py",
  "scores": {
    "srp_score": 0.85,
    "ocp_score": 0.75,
    "lsp_score": 1.0,
    "isp_score": 0.9,
    "dip_score": 0.65,
    "overall_score": 0.83
  },
  "summary": "Great SOLID architecture! 🌟",
  "smart_analysis": {
    "project_type": "small_app",
    "complexity_score": 0.4,
    "lines_of_code": 350,
    "class_count": 5,
    "adaptive_weights": {
      "srp": 0.25,
      "ocp": 0.2,
      "lsp": 0.2,
      "isp": 0.15,
      "dip": 0.2
    }
  }
}

GitHub Actions Integration

name: SOLID Analysis
on: [push, pull_request]
jobs:
  solid-check:
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v2
    - name: Setup Python
      uses: actions/setup-python@v2
      with:
        python-version: 3.13
    - name: Run SOLID Analysis
      run: |
        python __main__.py --json src/main.py > solid_report.json
        # Add custom logic to parse results and set exit codes

🎓 Educational Value

This tool serves as both a practical analysis instrument and an educational resource:

1. Learn SOLID Principles: Understand how each principle applies in real code
2. Contextual Understanding: See how requirements change based on project type
3. Practical Application: Get actionable advice rather than abstract theory
4. Progressive Improvement: Track improvements over time with consistent metrics

🏆 Why This Tool is Different

Unlike traditional static analysis tools that apply rigid rules uniformly, SOLID Checker understands that context matters:

• No More Over-Engineering Warnings for simple scripts that don't need complex architecture
• Stricter Standards for libraries and frameworks where quality is paramount
• Balanced Approach for typical applications with practical recommendations
• Educational Feedback that helps developers understand why certain principles matter more in different contexts

Result: More practical, actionable, and contextually appropriate SOLID analysis that helps you write better code without unnecessary complexity.

🤝 Contributing

Contributions are welcome! Areas for enhancement:

• New Project Type Detection: Additional heuristics for specialized domains
• Language Support: Extend analysis to other programming languages
• Custom Rules: User-defined weighting and threshold configurations
• IDE Integration: Plugins for popular development environments
• Continuous Monitoring: Integration with code quality dashboards

📜 License

MIT License - feel free to use, modify, and distribute.

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Built with ❤️ for developers who care about code quality and practical software architecture.