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TDD Refactor Phase - Improve Quality & Security

Clean up code, apply security best practices, and enhance design whilst keeping all tests green and maintaining GitHub issue compliance.

GitHub Issue Integration

Issue Completion Validation

• Verify all acceptance criteria met - Cross-check implementation against GitHub issue requirements
• Update issue status - Mark issue as completed or identify remaining work
• Document design decisions - Comment on issue with architectural choices made during refactor
• Link related issues - Identify technical debt or follow-up issues created during refactoring

Quality Gates

• Definition of Done adherence - Ensure all issue checklist items are satisfied
• Security requirements - Address any security considerations mentioned in issue
• Performance criteria - Meet any performance requirements specified in issue
• Documentation updates - Update any documentation referenced in issue

Core Principles

Code Quality Improvements

• Remove duplication - Extract common code into reusable functions or classes
• Improve readability - Use intention-revealing names and clear structure aligned with issue domain
• Apply SOLID principles - Single responsibility, dependency inversion, etc.
• Simplify complexity - Break down large functions, reduce cyclomatic complexity

Security Hardening

• Input validation - Validate and sanitize all external inputs per issue security requirements
• Authentication/Authorization - Implement proper access controls if specified in issue
• Data protection - Encrypt sensitive data, use secure connection handling
• Error handling - Avoid information disclosure through exception details
• Dependency scanning - Check for vulnerable packages with pip-audit or safety
• Secrets management - Use environment variables or secret managers, never hard-code credentials
• OWASP compliance - Address security concerns mentioned in issue or related security tickets

Design Excellence

• Design patterns - Apply appropriate patterns (Repository, Factory, Strategy, etc.)
• Dependency injection - Use Protocol-based injection for loose coupling
• Configuration management - Externalize settings using Pydantic Settings or environment variables
• Logging and monitoring - Add structured logging with structlog or standard logging for issue troubleshooting
• Performance optimization - Use async/await, efficient collections, caching when appropriate

Python Best Practices

• Type hints - Add comprehensive type hints with mypy validation
• Modern Python features - Use dataclasses, pattern matching (3.10+), type aliases
• Memory efficiency - Consider generators, context managers for resource management
• Exception handling - Use specific exception types, create custom exceptions for domain errors
• Pythonic idioms - List comprehensions, context managers, decorators where appropriate

Security Checklist

• Input validation on all public methods
• SQL injection prevention (parameterized queries with SQLAlchemy)
• XSS protection for web applications (template escaping)
• Authorization checks on sensitive operations
• Secure configuration (no secrets in code, use env vars)
• Error handling without information disclosure
• Dependency vulnerability scanning (pip-audit, safety)
• OWASP Top 10 considerations addressed for Python

Execution Guidelines

1. Review issue completion - Ensure GitHub issue acceptance criteria are fully met
2. Ensure green tests - Run pytest to confirm all tests pass before refactoring
3. Confirm your plan with the user - Ensure understanding of improvements. NEVER start making changes without user confirmation
4. Small incremental changes - Refactor in tiny steps, running pytest frequently
5. Apply one improvement at a time - Focus on single refactoring technique
6. Run security analysis - Use bandit, pip-audit, safety for Python security scanning
7. Run code quality checks - Use ruff, black, mypy to ensure code quality
8. Update issue - Comment on final implementation and close issue if complete

Example Refactoring

Before (After Green Phase)

def process_order(order_data):
    if order_data["status"] == "pending":
        total = 0
        for item in order_data["items"]:
            total += item["price"] * item["quantity"]
        if order_data["customer_tier"] == "premium":
            total = total * 0.9
        return total
    return 0

After (Refactor Phase)

from dataclasses import dataclass
from typing import Protocol
from decimal import Decimal

@dataclass
class OrderItem:
    price: Decimal
    quantity: int

    def subtotal(self) -> Decimal:
        return self.price * self.quantity

class DiscountStrategy(Protocol):
    def apply(self, amount: Decimal) -> Decimal: ...

class PremiumDiscount:
    def apply(self, amount: Decimal) -> Decimal:
        return amount * Decimal("0.9")

class NoDiscount:
    def apply(self, amount: Decimal) -> Decimal:
        return amount

class OrderProcessor:
    def __init__(self, discount_strategy: DiscountStrategy):
        self._discount = discount_strategy

    def calculate_total(self, items: list[OrderItem]) -> Decimal:
        """Calculate order total with discount applied."""
        subtotal = sum(item.subtotal() for items in items)
        return self._discount.apply(subtotal)

Refactor Phase Checklist

• GitHub issue acceptance criteria fully satisfied
• Code duplication eliminated
• Names clearly express intent aligned with issue domain
• Functions have single responsibility (< 20 lines preferred)
• Security vulnerabilities addressed per issue requirements
• Performance considerations applied
• All tests remain green (pytest passes)
• Code coverage maintained or improved (pytest --cov)
• Type hints added and mypy passes
• Linting passes (Black, Ruff)
• Issue marked as complete or follow-up issues created
• Documentation updated as specified in issue