---name: code-metrics-analysisdescription: >---

Code Metrics Analysis

Table of Contents

• Overview
• When to Use
• Quick Start
• Reference Guides
• Best Practices

Overview

Measure and analyze code quality metrics to identify complexity, maintainability issues, and areas for improvement.

When to Use

• Code quality assessment
• Identifying refactoring candidates
• Technical debt monitoring
• Code review automation
• CI/CD quality gates
• Team performance tracking
• Legacy code analysis

Quick Start

Minimal working example:

import * as ts from "typescript";
import * as fs from "fs";

interface ComplexityMetrics {
  cyclomaticComplexity: number;
  cognitiveComplexity: number;
  linesOfCode: number;
  functionCount: number;
  classCount: number;
  maxNestingDepth: number;
}

class CodeMetricsAnalyzer {
  analyzeFile(filePath: string): ComplexityMetrics {
    const sourceCode = fs.readFileSync(filePath, "utf-8");
    const sourceFile = ts.createSourceFile(
      filePath,
      sourceCode,
      ts.ScriptTarget.Latest,
      true,
    );

    const metrics: ComplexityMetrics = {
      cyclomaticComplexity: 0,
      cognitiveComplexity: 0,
// ... (see reference guides for full implementation)

Reference Guides

Detailed implementations in the references/ directory:

Guide	Contents
TypeScript Complexity Analyzer	TypeScript Complexity Analyzer
Python Code Metrics (using radon)	Python Code Metrics (using radon)
ESLint Plugin for Complexity	ESLint Plugin for Complexity
CI/CD Quality Gates	CI/CD Quality Gates

Best Practices

✅ DO

• Monitor metrics over time
• Set reasonable thresholds
• Focus on trends, not absolute numbers
• Automate metric collection
• Use metrics to guide refactoring
• Combine multiple metrics
• Include metrics in code reviews

❌ DON'T

• Use metrics as sole quality indicator
• Set unrealistic thresholds
• Ignore context and domain
• Punish developers for metrics
• Focus only on one metric
• Skip documentation