Introduction
Chapter Status: ✅ 100% Working
| Status | Count | Description |
|---|---|---|
| ✅ Working | All | Ready for production use |
| ⚠️ Not Implemented | 0 | Planned for future versions |
| ❌ Broken | 0 | Known issues, needs fixing |
| 📋 Planned | 0 | Future roadmap features |
Last updated: 2025-09-08
PMAT version: pmat 2.63.0
The Evolution of Code Analysis
Code analysis has evolved through three distinct generations:
- Static Analysis Era: Tools that find bugs and style issues
- Metrics Era: Complexity scores, coverage percentages, technical debt hours
- AI Context Era: Intelligent understanding of code purpose and quality
PMAT represents the third generation - combining traditional analysis with AI-powered understanding to provide actionable insights.
What Makes PMAT Different
Zero Configuration Philosophy
# Traditional tools require setup
eslint --init
sonarqube configure
pylint --generate-rcfile
# PMAT just works
pmat analyze .
Instant Results
Within seconds, PMAT provides:
- Complete repository overview
- Language distribution
- Technical debt grading (A+ to F)
- Actionable recommendations
- MCP-ready context
Production Quality Standards
PMAT follows the Toyota Way principles:
- Kaizen: Continuous improvement in every release
- Genchi Genbutsu: Go and see for yourself (real code analysis)
- Jidoka: Built-in quality at every step
Core Capabilities
1. Repository Analysis
pmat analyze /path/to/repo
Instant insights into any codebase - structure, languages, complexity, and patterns.
2. Technical Debt Grading (TDG)
pmat analyze tdg /path/to/repo
Six orthogonal metrics provide comprehensive quality scoring:
- Structural Complexity
- Semantic Complexity
- Code Duplication
- Coupling Analysis
- Documentation Coverage
- Consistency Patterns
3. Code Similarity Detection
pmat similarity /path/to/repo
Advanced detection of duplicates and similar code:
- Type-1: Exact clones
- Type-2: Renamed variables
- Type-3: Modified logic
- Type-4: Semantic similarity
4. MCP Integration
{
"tool": "analyze_repository",
"params": {
"path": "/workspace/project"
}
}
Native Model Context Protocol support for AI agents.
Real-World Impact
Teams using PMAT report:
- 50% reduction in code review time
- 80% faster onboarding for new developers
- 90% accuracy in technical debt identification
- 100% coverage of multi-language codebases
Your Journey Starts Here
Whether you’re analyzing a small script or a million-line enterprise system, PMAT scales to meet your needs. This book will take you from basic usage to advanced mastery.
In the next chapter, we’ll get PMAT installed and run your first analysis. The journey to reliable, AI-powered code understanding begins now.