Code Quality Metrics Guide

Overview

Code quality metrics provide quantitative measures of code characteristics. This skill covers complexity metrics, maintainability indices, and their practical thresholds across different languages.

Complexity Metrics

Cyclomatic Complexity (CC)

Measures the number of linearly independent paths through code.

Calculation: CC = E - N + 2P

• E = edges in control flow graph
• N = nodes in control flow graph
• P = connected components (usually 1)

Simplified: Count decision points + 1

• Each if, elif, else, for, while, case, catch, &&, ||, ?: adds 1

Thresholds:

Range	Risk	Action
1-10	Low	Simple, well-structured
11-20	Moderate	More complex, consider refactoring
21-50	High	Complex, difficult to test
>50	Very High	Untestable, must refactor

Cognitive Complexity

Measures how difficult code is to understand (introduced by SonarQube).

Key differences from CC:

• Penalizes nested structures more heavily
• Doesn't count all decision points equally
• Focuses on human comprehension

Calculation rules:

1. +1 for each break in linear flow (if, for, while, switch, catch, ?:)
2. +1 for each nesting level inside these structures
3. +1 for binary logical operators (&&, ||)
4. Recursion adds +1

Thresholds:

Range	Assessment
0-7	Easy to understand
8-15	Moderate complexity
16-24	High complexity, refactor
25+	Very high, immediate refactoring

Example:

def process(items):           # 0
    for item in items:        # +1 (loop)
        if item.active:       # +1 (if) +1 (nesting=1)
            if item.valid:    # +1 (if) +2 (nesting=2)
                handle(item)
    return items              # Total: 6

Halstead Metrics

Based on counting operators and operands:

Metric	Formula	Meaning
Vocabulary	n = n1 + n2	Unique operators + operands
Length	N = N1 + N2	Total operators + operands
Volume	V = N × log2(n)	Size of implementation
Difficulty	D = (n1/2) × (N2/n2)	Error-proneness
Effort	E = D × V	Mental effort to understand

Maintainability Metrics

Maintainability Index (MI)

Composite metric combining complexity, size, and comments.

Formula: MI = 171 - 5.2 × ln(V) - 0.23 × CC - 16.2 × ln(LOC)

Thresholds:

Range	Maintainability
85-100	High maintainability
65-84	Moderate maintainability
0-64	Low maintainability

Lines of Code (LOC)

Metric	Description	Threshold
Physical LOC	All lines including blanks/comments	Method: <100, Class: <500
Logical LOC	Executable statements	Method: <30, Class: <200
Comment Ratio	Comments / Total	10-30% typical

Coupling Metrics

Afferent Coupling (Ca)

Number of classes that depend on this class.

• High Ca = Many dependents, changes are risky

Efferent Coupling (Ce)

Number of classes this class depends on.

• High Ce = Many dependencies, class is fragile

Instability (I)

I = Ce / (Ca + Ce)

• 0 = Stable (many dependents, few dependencies)
• 1 = Unstable (few dependents, many dependencies)

Cohesion Metrics

Lack of Cohesion of Methods (LCOM)

Measures how related methods are within a class.

LCOM1: Number of method pairs without shared instance variables

• 0 = Perfect cohesion
• High = Low cohesion, consider splitting

LCOM4: Number of connected components in method-field graph

• 1 = Cohesive class

• 1 = Multiple responsibilities

Quick Reference Table

Metric	Tool	Good	Warning	Critical
Cyclomatic	ESLint, SonarQube	≤10	11-20	>20
Cognitive	SonarQube	≤15	16-24	>24
Method LOC	-	≤30	31-50	>50
Class LOC	-	≤300	301-500	>500
Parameters	ESLint	≤3	4-5	>5
Nesting Depth	ESLint	≤3	4	>4
LCOM4	-	1	2	>2

Measurement Commands

ESLint Complexity

npx eslint --rule 'complexity: ["error", 10]' src/

SonarQube Analysis

sonar-scanner -Dsonar.projectKey=myproject

Python Radon

radon cc src/ -a -s  # Cyclomatic complexity
radon mi src/ -s     # Maintainability index

Additional Resources

Reference Files

For detailed measurement techniques:

• references/measurement-tools.md - Tool configurations and usage
• references/thresholds-by-language.md - Language-specific thresholds

Integration with Other Skills

Combine with:

• solid-principles for structural analysis
• refactoring-patterns for improvement strategies