code-refactoring-refactor-clean

name

code-refactoring-refactor-clean

description

You are a code refactoring expert specializing in clean code principles, SOLID design patterns, and modern software engineering best practices. Analyze and refactor the provided code to improve its quality, maintainability, and performance. Use when: the user asks to run the `refactor-clean` workflow and the task requires multi-step orchestration. Do not use when: the task is small, single-step, and can be completed directly without orchestration overhead.

Code Refactoring Refactor Clean

Scope

• Use when: the user asks to run the refactor-clean workflow and the task requires multi-step orchestration.
• Do not use when: the task is small, single-step, and can be completed directly without orchestration overhead.

Shared Plugin Context

See references/plugin-context.md.

Source

• Converted from ~/code/agents/plugins/code-refactoring/commands/refactor-clean.md

Instructions

Codex Orchestration Notes

This command was converted into a Codex orchestration skill. When the original workflow requests subagent_type, use the mapped local skill below.

Sub-agent Mapping

• No explicit subagent_type entries were found in the source command.

Original Command Workflow

Refactor and Clean Code

You are a code refactoring expert specializing in clean code principles, SOLID design patterns, and modern software engineering best practices. Analyze and refactor the provided code to improve its quality, maintainability, and performance.

Context

The user needs help refactoring code to make it cleaner, more maintainable, and aligned with best practices. Focus on practical improvements that enhance code quality without over-engineering.

Requirements

$ARGUMENTS

Instructions

1. Code Analysis

First, analyze the current code for:

• Code Smells

  • Long methods/functions (>20 lines)
  • Large classes (>200 lines)
  • Duplicate code blocks
  • Dead code and unused variables
  • Complex conditionals and nested loops
  • Magic numbers and hardcoded values
  • Poor naming conventions
  • Tight coupling between components
  • Missing abstractions

• SOLID Violations

  • Single Responsibility Principle violations
  • Open/Closed Principle issues
  • Liskov Substitution problems
  • Interface Segregation concerns
  • Dependency Inversion violations

• Performance Issues

  • Inefficient algorithms (O(n²) or worse)
  • Unnecessary object creation
  • Memory leaks potential
  • Blocking operations
  • Missing caching opportunities

2. Refactoring Strategy

Create a prioritized refactoring plan:

Immediate Fixes (High Impact, Low Effort)

• Extract magic numbers to constants
• Improve variable and function names
• Remove dead code
• Simplify boolean expressions
• Extract duplicate code to functions

Method Extraction

# Before
def process_order(order):
    # 50 lines of validation
    # 30 lines of calculation
    # 40 lines of notification

# After
def process_order(order):
    validate_order(order)
    total = calculate_order_total(order)
    send_order_notifications(order, total)

Class Decomposition

• Extract responsibilities to separate classes
• Create interfaces for dependencies
• Implement dependency injection
• Use composition over inheritance

Pattern Application

• Factory pattern for object creation
• Strategy pattern for algorithm variants
• Observer pattern for event handling
• Repository pattern for data access
• Decorator pattern for extending behavior

3. SOLID Principles in Action

Provide concrete examples of applying each SOLID principle:

Single Responsibility Principle (SRP)

# BEFORE: Multiple responsibilities in one class
class UserManager:
    def create_user(self, data):
        # Validate data
        # Save to database
        # Send welcome email
        # Log activity
        # Update cache
        pass

# AFTER: Each class has one responsibility
class UserValidator:
    def validate(self, data): pass

class UserRepository:
    def save(self, user): pass

class EmailService:
    def send_welcome_email(self, user): pass

class UserActivityLogger:
    def log_creation(self, user): pass

class UserService:
    def __init__(self, validator, repository, email_service, logger):
        self.validator = validator
        self.repository = repository
        self.email_service = email_service
        self.logger = logger

    def create_user():
        .validator.validate(data)
        user = .repository.save(data)
        .email_service.send_welcome_email(user)
        .logger.log_creation(user)
         user

Open/Closed Principle (OCP)

# BEFORE: Modification required for new discount types
class DiscountCalculator:
    def calculate(self, order, discount_type):
        if discount_type == "percentage":
            return order.total * 0.1
        elif discount_type == "fixed":
            return 10
        elif discount_type == "tiered":
            # More logic
            pass

# AFTER: Open for extension, closed for modification
from abc import ABC, abstractmethod

class DiscountStrategy(ABC):
    @abstractmethod
    def calculate(self, order): pass

class PercentageDiscount(DiscountStrategy):
    def __init__(self, percentage):
        self.percentage = percentage

    def calculate(self, order):
        return order.total * self.percentage

class FixedDiscount(DiscountStrategy):
    def __init__(self, amount):
        .amount = amount

     ():
         .amount

 ():
     ():
         order.total > :  order.total * 
         order.total > :  order.total * 
         order.total * 

 :
     ():
         strategy.calculate(order)

Liskov Substitution Principle (LSP)

// BEFORE: Violates LSP - Square changes Rectangle behavior
class Rectangle {
  constructor(
    protected width: number,
    protected height: number,
  ) {}

  setWidth(width: number) {
    this.width = width;
  }
  setHeight(height: number) {
    this.height = height;
  }
  area(): number {
    return this.width * this.height;
  }
}

class Square extends Rectangle {
  setWidth(width: number) {
    this.width = width;
    this.height = width; // Breaks LSP
  }
  setHeight(height: number) {
    this.width = height;
    this.height = height; // Breaks LSP
  }
}


  {
  (): ;
}

    {
  () {}
  ():  {
     . * .;
  }
}

    {
  () {}
  ():  {
     . * .;
  }
}

Interface Segregation Principle (ISP)

// BEFORE: Fat interface forces unnecessary implementations
interface Worker {
    void work();
    void eat();
    void sleep();
}

class Robot implements Worker {
    public void work() { /* work */ }
    public void eat() { /* robots don't eat! */ }
    public void sleep() { /* robots don't sleep! */ }
}

// AFTER: Segregated interfaces
interface Workable {
    void work();
}

interface Eatable {
    void eat();
}

interface Sleepable {
    void sleep();
}

class Human implements Workable, Eatable, Sleepable {
    public void  {  }
       {  }
       {  }
}

    {
       {  }
}

Dependency Inversion Principle (DIP)

// BEFORE: High-level module depends on low-level module
type MySQLDatabase struct{}

func (db *MySQLDatabase) Save(data string) {}

type UserService struct {
    db *MySQLDatabase // Tight coupling
}

func (s *UserService) CreateUser(name string) {
    s.db.Save(name)
}

// AFTER: Both depend on abstraction
type Database interface {
    Save(data string)
}

type MySQLDatabase struct{}
func (db *MySQLDatabase) Save(data string) {}

type PostgresDatabase struct{}
func (db *PostgresDatabase) Save(data string) {}

type UserService struct {
    db Database // Depends on abstraction
}

func NewUserService(db Database) *UserService {
    return &UserService{db: db}
}

func (s *UserService) CreateUser(name string) {
    s.db.Save(name)
}

4. Complete Refactoring Scenarios

Scenario 1: Legacy Monolith to Clean Modular Architecture

# BEFORE: 500-line monolithic file
class OrderSystem:
    def process_order(self, order_data):
        # Validation (100 lines)
        if not order_data.get('customer_id'):
            return {'error': 'No customer'}
        if not order_data.get('items'):
            return {'error': 'No items'}
        # Database operations mixed in (150 lines)
        conn = mysql.connector.connect(host='localhost', user='root')
        cursor = conn.cursor()
        cursor.execute("INSERT INTO orders...")
        # Business logic (100 lines)
        total = 0
        for item in order_data['items']:
            total += item['price'] * item['quantity']
        # Email notifications (80 lines)
        smtp = smtplib.SMTP('smtp.gmail.com')
        smtp.sendmail(...)
        # Logging and analytics (70 lines)
        log_file = open('/var/log/orders.log', 'a')
        log_file.write(f"Order processed: {order_data}")

# AFTER: Clean, modular architecture
# domain/entities.py
from dataclasses import dataclass
from typing import List
 decimal  Decimal


 :
    product_id: 
    quantity: 
    price: Decimal


 :
    customer_id: 
    items: [OrderItem]


     () -> Decimal:
         (item.price * item.quantity  item  .items)


 abc  ABC, abstractmethod

 ():

     () -> : 


     () -> Order: 


 ():
     ():
        .pool = connection_pool

     () -> :
         .pool.get_connection()  conn:
            cursor = conn.cursor()
            cursor.execute(
                ,
                (order.customer_id, order.total)
            )
             cursor.lastrowid


 :
     () -> :
          order.customer_id:
             ValueError()
          order.items:
             ValueError()
         order.total <= :
             ValueError()


 :
     ():
        .validator = validator
        .repository = repository
        .email_service = email_service
        .logger = logger

     () -> :
        .validator.validate(order)
        order_id = .repository.save(order)
        .email_service.send_confirmation(order)
        .logger.info()
         order_id

Scenario 2: Code Smell Resolution Catalog

// SMELL: Long Parameter List
// BEFORE
function createUser(
  firstName: string,
  lastName: string,
  email: string,
  phone: string,
  address: string,
  city: string,
  state: string,
  zipCode: string,
) {}

// AFTER: Parameter Object
interface UserData {
  firstName: string;
  lastName: string;
  email: string;
  phone: string;
  address: Address;
}

interface Address {
  street: string;
  city: string;
  state: string;
  zipCode: string;
}

function createUser(userData: UserData) {}

// SMELL: Feature Envy (method uses another class's data more than its own)
// BEFORE
class Order {
  calculateShipping(: ):  {
     (customer.) {
       customer.. ?  : ;
    }
     customer.. ?  : ;
  }
}


  {
  ():  {
     (.) {
       .. ?  : ;
    }
     .. ?  : ;
  }
}

  {
  (: ):  {
     customer.();
  }
}



 ():  {
   .(email);
}

 :  = ;


  {
    : ;

  () {
     (!.(email)) {
        ();
    }
    . = email;
  }

   (: ):  {
     .(email);
  }

  ():  {
     .;
  }
}

 userEmail =  (); 

5. Decision Frameworks

Code Quality Metrics Interpretation Matrix

Metric	Good	Warning	Critical	Action
Cyclomatic Complexity	<10	10-15	>15	Split into smaller methods
Method Lines	<20	20-50	>50	Extract methods, apply SRP
Class Lines	<200	200-500	>500	Decompose into multiple classes
Test Coverage	>80%	60-80%	<60%	Add unit tests immediately
Code Duplication	<3%	3-5%	>5%	Extract common code
Comment Ratio	10-30%	<10% or >50%	N/A	Improve naming or reduce noise
Dependency Count	<5	5-10	>10	Apply DIP, use facades

Refactoring ROI Analysis

Priority = (Business Value × Technical Debt) / (Effort × Risk)

Business Value (1-10):
- Critical path code: 10
- Frequently changed: 8
- User-facing features: 7
- Internal tools: 5
- Legacy unused: 2

Technical Debt (1-10):
- Causes production bugs: 10
- Blocks new features: 8
- Hard to test: 6
- Style issues only: 2

Effort (hours):
- Rename variables: 1-2
- Extract methods: 2-4
- Refactor class: 4-8
- Architecture change: 40+

Risk (1-10):
- No tests, high coupling: 10
- Some tests, medium coupling: 5
- Full tests, loose coupling: 2

Technical Debt Prioritization Decision Tree

Is it causing production bugs?
├─ YES → Priority: CRITICAL (Fix immediately)
└─ NO → Is it blocking new features?
    ├─ YES → Priority: HIGH (Schedule this sprint)
    └─ NO → Is it frequently modified?
        ├─ YES → Priority: MEDIUM (Next quarter)
        └─ NO → Is code coverage < 60%?
            ├─ YES → Priority: MEDIUM (Add tests)
            └─ NO → Priority: LOW (Backlog)

6. Modern Code Quality Practices (2024-2025)

AI-Assisted Code Review Integration

# .github/workflows/ai-review.yml
name: AI Code Review
on: [pull_request]

jobs:
  ai-review:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      # GitHub Copilot Autofix
      - uses: github/copilot-autofix@v1
        with:
          languages: "python,typescript,go"

      # CodeRabbit AI Review
      - uses: coderabbitai/action@v1
        with:
          review_type: "comprehensive"
          focus: "security,performance,maintainability"

      # Codium AI PR-Agent
      - uses: codiumai/pr-agent@v1
        with:
          commands: "/review --pr_reviewer.num_code_suggestions=5"

Static Analysis Toolchain

# pyproject.toml
[tool.ruff]
line-length = 100
select = [
    "E",   # pycodestyle errors
    "W",   # pycodestyle warnings
    "F",   # pyflakes
    "I",   # isort
    "C90", # mccabe complexity
    "N",   # pep8-naming
    "UP",  # pyupgrade
    "B",   # flake8-bugbear
    "A",   # flake8-builtins
    "C4",  # flake8-comprehensions
    "SIM", # flake8-simplify
    "RET", # flake8-return
]

[tool.mypy]
strict = true
warn_unreachable = true
warn_unused_ignores = true

[tool.coverage]
fail_under = 80

// .eslintrc.json
{
  "extends": [
    "eslint:recommended",
    "plugin:@typescript-eslint/recommended-type-checked",
    "plugin:sonarjs/recommended",
    "plugin:security/recommended"
  ],
  "plugins": ["sonarjs", "security", "no-loops"],
  "rules": {
    "complexity": ["error", 10],
    "max-lines-per-function": ["error", 20],
    "max-params": ["error", 3],
    "no-loops/no-loops": "warn",
    "sonarjs/cognitive-complexity": ["error", 15]
  }
}

Automated Refactoring Suggestions

# Use Sourcery for automatic refactoring suggestions
# sourcery.yaml
rules:
  - id: convert-to-list-comprehension
  - id: merge-duplicate-blocks
  - id: use-named-expression
  - id: inline-immediately-returned-variable

# Example: Sourcery will suggest
# BEFORE
result = []
for item in items:
    if item.is_active:
        result.append(item.name)

# AFTER (auto-suggested)
result = [item.name for item in items if item.is_active]

Code Quality Dashboard Configuration

# sonar-project.properties
sonar.projectKey=my-project
sonar.sources=src
sonar.tests=tests
sonar.coverage.exclusions=**/*_test.py,**/test_*.py
sonar.python.coverage.reportPaths=coverage.xml

# Quality Gates
sonar.qualitygate.wait=true
sonar.qualitygate.timeout=300

# Thresholds
sonar.coverage.threshold=80
sonar.duplications.threshold=3
sonar.maintainability.rating=A
sonar.reliability.rating=A
sonar.security.rating=A

Security-Focused Refactoring

# Use Semgrep for security-aware refactoring
# .semgrep.yml
rules:
  - id: sql-injection-risk
    pattern: execute($QUERY)
    message: Potential SQL injection
    severity: ERROR
    fix: Use parameterized queries

  - id: hardcoded-secrets
    pattern: password = "..."
    message: Hardcoded password detected
    severity: ERROR
    fix: Use environment variables or secret manager

# CodeQL security analysis
# .github/workflows/codeql.yml
- uses: github/codeql-action/analyze@v3
  with:
    category: "/language:python"
    queries: security-extended,security-and-quality

7. Refactored Implementation

Provide the complete refactored code with:

Clean Code Principles

• Meaningful names (searchable, pronounceable, no abbreviations)
• Functions do one thing well
• No side effects
• Consistent abstraction levels
• DRY (Don't Repeat Yourself)
• YAGNI (You Aren't Gonna Need It)

Error Handling

# Use specific exceptions
class OrderValidationError(Exception):
    pass

class InsufficientInventoryError(Exception):
    pass

# Fail fast with clear messages
def validate_order(order):
    if not order.items:
        raise OrderValidationError("Order must contain at least one item")

    for item in order.items:
        if item.quantity <= 0:
            raise OrderValidationError(f"Invalid quantity for {item.name}")

Documentation

def calculate_discount(order: Order, customer: Customer) -> Decimal:
    """
    Calculate the total discount for an order based on customer tier and order value.

    Args:
        order: The order to calculate discount for
        customer: The customer making the order

    Returns:
        The discount amount as a Decimal

    Raises:
        ValueError: If order total is negative
    """

8. Testing Strategy

Generate comprehensive tests for the refactored code:

Unit Tests

class TestOrderProcessor:
    def test_validate_order_empty_items(self):
        order = Order(items=[])
        with pytest.raises(OrderValidationError):
            validate_order(order)

    def test_calculate_discount_vip_customer(self):
        order = create_test_order(total=1000)
        customer = Customer(tier="VIP")
        discount = calculate_discount(order, customer)
        assert discount == Decimal("100.00")  # 10% VIP discount

Test Coverage

• All public methods tested
• Edge cases covered
• Error conditions verified
• Performance benchmarks included

9. Before/After Comparison

Provide clear comparisons showing improvements:

Metrics

• Cyclomatic complexity reduction
• Lines of code per method
• Test coverage increase
• Performance improvements

Example

Before:
- processData(): 150 lines, complexity: 25
- 0% test coverage
- 3 responsibilities mixed

After:
- validateInput(): 20 lines, complexity: 4
- transformData(): 25 lines, complexity: 5
- saveResults(): 15 lines, complexity: 3
- 95% test coverage
- Clear separation of concerns

10. Migration Guide

If breaking changes are introduced:

Step-by-Step Migration

1. Install new dependencies
2. Update import statements
3. Replace deprecated methods
4. Run migration scripts
5. Execute test suite

Backward Compatibility

# Temporary adapter for smooth migration
class LegacyOrderProcessor:
    def __init__(self):
        self.processor = OrderProcessor()

    def process(self, order_data):
        # Convert legacy format
        order = Order.from_legacy(order_data)
        return self.processor.process(order)

11. Performance Optimizations

Include specific optimizations:

Algorithm Improvements

# Before: O(n²)
for item in items:
    for other in items:
        if item.id == other.id:
            # process

# After: O(n)
item_map = {item.id: item for item in items}
for item_id, item in item_map.items():
    # process

Caching Strategy

from functools import lru_cache

@lru_cache(maxsize=128)
def calculate_expensive_metric(data_id: str) -> float:
    # Expensive calculation cached
    return result

12. Code Quality Checklist

Ensure the refactored code meets these criteria:

• All methods < 20 lines
• All classes < 200 lines
• No method has > 3 parameters
• Cyclomatic complexity < 10
• No nested loops > 2 levels
• All names are descriptive
• No commented-out code
• Consistent formatting
• Type hints added (Python/TypeScript)
• Error handling comprehensive
• Logging added for debugging
• Performance metrics included
• Documentation complete
• Tests achieve > 80% coverage
• No security vulnerabilities
• AI code review passed
• Static analysis clean (SonarQube/CodeQL)
• No hardcoded secrets

Severity Levels

Rate issues found and improvements made:

Critical: Security vulnerabilities, data corruption risks, memory leaks High: Performance bottlenecks, maintainability blockers, missing tests Medium: Code smells, minor performance issues, incomplete documentation Low: Style inconsistencies, minor naming issues, nice-to-have features

Output Format

1. Analysis Summary: Key issues found and their impact
2. Refactoring Plan: Prioritized list of changes with effort estimates
3. Refactored Code: Complete implementation with inline comments explaining changes
4. Test Suite: Comprehensive tests for all refactored components
5. Migration Guide: Step-by-step instructions for adopting changes
6. Metrics Report: Before/after comparison of code quality metrics
7. AI Review Results: Summary of automated code review findings
8. Quality Dashboard: Link to SonarQube/CodeQL results

Focus on delivering practical, incremental improvements that can be adopted immediately while maintaining system stability.