Code Architecture Review

Python
# Example review of a problematic class
class UserService:
    def __init__(self):
        self.db = MySQLConnection()  # Tight coupling
        self.email = SMTPClient()    # Multiple responsibilities
    
    def create_user(self, data):
        # Missing validation, error handling
        user = User(**data)
        self.db.save(user)
        self.email.send_welcome(user.email)
        return user

# Issues identified:
# 1. Violates Single Responsibility Principle
# 2. Tight coupling to concrete implementations
# 3. Missing input validation
# 4. No error handling
# 5. Technical debt: hardcoded dependencies

Progress:

• Scan for architectural smells - Large classes, long methods, deep nesting
• Check SOLID principles - SRP, OCP, LSP, ISP, DIP violations
• Identify technical debt - TODO comments, deprecated APIs, workarounds
• Review error handling - Missing try-catch, silent failures
• Assess coupling/cohesion - Tight coupling, low cohesion patterns
• Check security issues - SQL injection, XSS, hardcoded secrets
• Evaluate testability - Static dependencies, complex constructors
• Suggest refactoring - Concrete improvement recommendations

Example 1: Input: Method with 150 lines handling user registration, validation, email sending, and logging Output:

• Violation: Single Responsibility Principle
• Technical Debt: Monolithic method reduces maintainability
• Solution: Extract ValidationService, EmailService, AuditService
• Refactor: Use Command pattern with discrete handlers

Example 2: Input: Repository class directly instantiating database connections Output:

• Violation: Dependency Inversion Principle
• Bug Risk: Connection leaks, no transaction management
• Solution: Inject IDbConnection interface, use connection pooling
• Pattern: Repository with Unit of Work

• Start with high-impact issues - Focus on bugs and security first
• Provide specific solutions - Don't just identify problems, suggest fixes
• Consider context - Legacy systems may need gradual refactoring approach
• Balance pragmatism - Not every principle violation needs immediate fixing
• Use architectural patterns - Suggest established patterns (Strategy, Factory, Observer)
• Think in layers - Separate concerns across presentation, business, data layers
• Consider non-functional requirements - Performance, scalability, maintainability

• Over-engineering - Don't suggest complex patterns for simple problems
• Ignoring business context - Consider delivery timelines and technical debt tolerance
• Missing the forest for trees - Focus on systemic issues, not just code style
• Perfectionism - Prioritize critical issues over minor violations
• One-size-fits-all - Tailor suggestions to team skill level and codebase maturity
• Forgetting testing - Always consider how changes affect test coverage and complexity