Code Quality Standards

Overview

Comprehensive code quality standards that ensure consistent, maintainable, and robust software development across all AzmX engineering teams.

Quality Metrics and Gates

SonarQube Quality Gates

graph TD
    A[Code Commit] --> B[SonarQube Analysis]
    B --> C{Quality Gate}
    C -->|"Pass ≥85%"| D[Merge Approved]
    C -->|"Fail <85%"| E[Fix Required]
    E --> F[Address Issues]
    F --> B
    D --> G[Production Ready]

Quality Gate Criteria

• Overall Score: ≥85% (minimum passing grade)
• Code Coverage: ≥80% for new code
• Duplicated Lines: ≤3% of total codebase
• Maintainability Rating: A or B rating required
• Reliability Rating: A rating required (no bugs)
• Security Rating: A rating required (no vulnerabilities)

Critical Issues (Zero Tolerance)

• Security Vulnerabilities: Any security issue blocks merge
• Bugs: Critical and major bugs block deployment
• Code Smells: Major code smells require resolution
• Coverage Gaps: New code without tests blocks merge

Language-Specific Standards

Python/Django Standards

# Code formatting with Black
# Import organization with isort
# Type hints for all functions
from typing import List, Optional, Dict, Any
from django.http import JsonResponse
from .models import User
from .serializers import UserSerializer

def get_user_list(
    request: HttpRequest,
    filters: Optional[Dict[str, Any]] = None
) -> JsonResponse:
    """
    Retrieve filtered list of users.

    Args:
        request: HTTP request object
        filters: Optional filters to apply

    Returns:
        JsonResponse with user data

    Raises:
        ValidationError: When filters are invalid
    """
    try:
        # Implementation with proper error handling
        users = User.objects.filter(**(filters or {}))
        serializer = UserSerializer(users, many=True)
        return JsonResponse({
            'success': True,
            'data': serializer.data,
            'count': len(serializer.data)
        })
    except Exception as e:
        return JsonResponse({
            'success': False,
            'error': str(e)
        }, status=400)

Python Quality Checklist

• [ ] Type Hints: All function parameters and returns
• [ ] Docstrings: Google/NumPy style for all public functions
• [ ] Error Handling: Explicit exception handling
• [ ] Logging: Appropriate log levels and messages
• [ ] Imports: Organized with isort, no unused imports
• [ ] Formatting: Black formatter applied
• [ ] Linting: Flake8 or Ruff passes without errors

TypeScript/React Standards

// Strict type definitions
interface UserProps {
  id: number;
  name: string;
  email: string;
  isActive?: boolean;
}

interface UserListProps {
  users: UserProps[];
  onUserSelect: (user: UserProps) => void;
  loading?: boolean;
}

// Proper component structure with error boundaries
export const UserList: React.FC<UserListProps> = ({
  users,
  onUserSelect,
  loading = false
}) => {
  const [selectedUser, setSelectedUser] = useState<UserProps | null>(null);

  const handleUserClick = useCallback((user: UserProps) => {
    setSelectedUser(user);
    onUserSelect(user);
  }, [onUserSelect]);

  if (loading) {
    return <LoadingSpinner data-testid="user-list-loading" />;
  }

  return (
    <div className="user-list" data-testid="user-list">
      {users.map(user => (
        <UserCard
          key={user.id}
          user={user}
          isSelected={selectedUser?.id === user.id}
          onClick={() => handleUserClick(user)}
        />
      ))}
    </div>
  );
};

TypeScript Quality Checklist

• [ ] Strict Types: No any types in production code
• [ ] Interface Definitions: Clear, well-documented interfaces
• [ ] Error Boundaries: Proper error handling in React
• [ ] Performance: Memoization where appropriate
• [ ] Accessibility: ARIA labels and semantic HTML
• [ ] Testing: Component tests with React Testing Library
• [ ] Formatting: Prettier and ESLint configuration

Testing Standards

Test Coverage Requirements

pie title Test Coverage Targets
    "Unit Tests" : 60
    "Integration Tests" : 25
    "E2E Tests" : 10
    "Manual Testing" : 5

Coverage Targets by Component

• Business Logic: 90% unit test coverage
• API Endpoints: 100% integration test coverage
• UI Components: 80% component test coverage
• Critical Paths: 100% end-to-end test coverage

Test Quality Standards

# Example: Well-structured test case
class TestUserService:
    """Test suite for UserService with proper setup and teardown."""

    def setup_method(self):
        """Setup test data before each test."""
        self.user_service = UserService()
        self.test_user_data = {
            'name': 'Test User',
            'email': '[email protected]'
        }

    def test_create_user_success(self):
        """Test successful user creation with valid data."""
        # Arrange
        user_data = self.test_user_data

        # Act
        result = self.user_service.create_user(user_data)

        # Assert
        assert result.success is True
        assert result.user.name == user_data['name']
        assert result.user.email == user_data['email']
        assert result.user.id is not None

    def test_create_user_invalid_email(self):
        """Test user creation fails with invalid email."""
        # Arrange
        user_data = {**self.test_user_data, 'email': 'invalid-email'}

        # Act & Assert
        with pytest.raises(ValidationError) as exc_info:
            self.user_service.create_user(user_data)

        assert "Invalid email format" in str(exc_info.value)

Test Quality Checklist

• [ ] Descriptive Names: Tests clearly describe what they test
• [ ] AAA Pattern: Arrange, Act, Assert structure
• [ ] Independent Tests: No dependencies between test cases
• [ ] Data Isolation: Clean setup and teardown
• [ ] Edge Cases: Test boundary conditions and error cases
• [ ] Performance: Tests run quickly (<100ms per unit test)
• [ ] Maintainability: Tests are easy to understand and modify

Code Review Standards

Review Process Flow

sequenceDiagram
    participant Dev as Developer
    participant PR as Pull Request
    participant Rev as Reviewer
    participant Lead as Tech Lead
    participant CI as CI/CD

    Dev->>PR: Create PR
    PR->>CI: Trigger automated checks
    CI-->>PR: Quality gates results

    alt Quality gates pass
        PR->>Rev: Request review
        Rev->>PR: Code review

        alt Changes needed
            Rev-->>Dev: Request changes
            Dev->>PR: Push updates
            PR->>CI: Re-run checks
        else Approved
            Rev-->>Lead: Review complete
            Lead->>PR: Final approval
            PR->>Dev: Merge authorized
        end
    else Quality gates fail
        CI-->>Dev: Fix quality issues
        Dev->>PR: Push fixes
    end

Review Criteria

Architecture and Design

• [ ] SOLID Principles: Code follows SOLID design principles
• [ ] Design Patterns: Appropriate pattern usage
• [ ] Separation of Concerns: Clear responsibility boundaries
• [ ] DRY Principle: No unnecessary code duplication
• [ ] YAGNI: No over-engineering or premature optimization

Code Quality

• [ ] Readability: Code is self-documenting and clear
• [ ] Naming: Variables, functions, classes have descriptive names
• [ ] Function Size: Functions are focused and reasonably sized
• [ ] Complexity: Cyclomatic complexity is manageable
• [ ] Error Handling: Proper exception handling and logging

Security Review

• [ ] Input Validation: All inputs are properly validated
• [ ] Authentication: Proper authentication mechanisms
• [ ] Authorization: Appropriate access controls
• [ ] Data Protection: Sensitive data is properly handled
• [ ] SQL Injection: No unsafe database queries
• [ ] XSS Prevention: Output is properly escaped

Performance Review

• [ ] Efficiency: Algorithms and data structures are efficient
• [ ] Database Queries: Optimized queries, proper indexing
• [ ] Caching: Appropriate caching strategies
• [ ] Resource Usage: Memory and CPU usage considered
• [ ] Scalability: Code scales with increased load

Documentation Standards

Code Documentation

class UserService:
    """
    Service class for managing user operations.

    This service handles all user-related business logic including
    creation, validation, and data persistence.

    Attributes:
        repository: User data repository
        validator: User data validator

    Example:
        >>> service = UserService()
        >>> user = service.create_user({'name': 'John', 'email': '[email protected]'})
        >>> print(user.name)
        'John'
    """

    def create_user(
        self,
        user_data: Dict[str, Any],
        validate: bool = True
    ) -> User:
        """
        Create a new user with the provided data.

        Args:
            user_data: Dictionary containing user information
                Required keys: 'name', 'email'
                Optional keys: 'phone', 'address'
            validate: Whether to perform data validation

        Returns:
            User: Created user instance with assigned ID

        Raises:
            ValidationError: When user_data is invalid
            DuplicateError: When email already exists

        Example:
            >>> user_data = {'name': 'Jane Doe', 'email': '[email protected]'}
            >>> user = service.create_user(user_data)
        """
        # Implementation here

Documentation Checklist

• [ ] API Documentation: All public APIs documented
• [ ] Complex Logic: Non-obvious code has explanatory comments
• [ ] Examples: Usage examples for public interfaces
• [ ] Error Scenarios: Documented exception conditions
• [ ] Dependencies: External dependencies and requirements noted
• [ ] Configuration: Setup and configuration documented

Automated Quality Checks

Pre-commit Hooks

# .pre-commit-config.yaml
repos:
  - repo: https://github.com/psf/black
    rev: 23.1.0
    hooks:
      - id: black
        language_version: python3.11

  - repo: https://github.com/pycqa/isort
    rev: 5.12.0
    hooks:
      - id: isort

  - repo: https://github.com/pycqa/flake8
    rev: 6.0.0
    hooks:
      - id: flake8
        args: [--max-line-length=88]

  - repo: https://github.com/pre-commit/mirrors-mypy
    rev: v1.0.1
    hooks:
      - id: mypy
        additional_dependencies: [types-requests]

CI/CD Quality Gates

# GitHub Actions workflow
name: Quality Checks
on: [push, pull_request]

jobs:
  quality:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3

      - name: Setup Python
        uses: actions/setup-python@v4
        with:
          python-version: '3.11'

      - name: Install dependencies
        run: |
          pip install -r requirements.txt
          pip install -r requirements-dev.txt

      - name: Run linting
        run: |
          black --check .
          isort --check-only .
          flake8 .
          mypy .

      - name: Run tests
        run: |
          pytest --cov=. --cov-report=xml

      - name: SonarQube Scan
        uses: sonarqube-quality-gate-action@master
        with:
          scanMetadataReportFile: target/sonar/report-task.txt

Monitoring and Metrics

Code Quality Metrics Dashboard

• Overall Quality Score: Team and individual scores
• Test Coverage Trends: Coverage over time
• Technical Debt: SonarQube debt metrics
• Review Cycle Time: Time from PR creation to merge
• Defect Density: Bugs per lines of code
• Code Churn: Files changed most frequently

Quality Improvement Process

1. Weekly Reviews: Team quality metric review
2. Monthly Goals: Set quality improvement targets
3. Quarterly Retrospectives: Process improvement sessions
4. Annual Standards Update: Update standards based on learnings

Quality Culture

Team Responsibilities

• Developers: Write quality code, participate in reviews
• Tech Leads: Monitor metrics, guide improvements
• DevOps: Maintain quality automation tools
• QA: Validate quality standards in testing

Continuous Improvement

• Feedback Loops: Regular feedback on quality processes
• Tool Updates: Keep quality tools current
• Training: Regular quality training sessions
• Innovation: Experiment with new quality practices

References

• SonarQube Quality Gates
• Clean Code Principles
• Testing Best Practices
• Code Review Best Practices