Django Technical Debt Cost Calculator & Elimination Strategy

# Example: Django technical debt increasing bug resolution time

# HIGH DEBT: Fat views with complex business logic
# app/views.py - 450 lines
def process_order(request):
    # 180 lines of business logic mixed with HTTP handling
    # Bug: Order total calculation sometimes incorrect
    # Investigation time: 8-12 hours (finding logic across views, models, utils)
    # Fix time: 6 hours (testing across multiple edge cases)
    # Total: 14-18 hours per bug
    pass

# LOW DEBT: Clean architecture with separated concerns
# app/services/order_service.py
class OrderService:
    def calculate_total(self, order):
        # Bug: Order total calculation incorrect
        # Investigation time: 1-2 hours (single responsibility, clear logic)
        # Fix time: 1 hour (isolated business logic, comprehensive tests)
        # Total: 2-3 hours per bug
        pass

# Benchmarking study across 50 Django projects
technical_debt_impact = {
    "low_debt_projects": {
        "average_bug_resolution_time": 2.3,  # hours
        "bugs_per_developer_per_month": 1.2,
        "developer_time_per_month": 2.76  # hours
    },

    "high_debt_projects": {
        "average_bug_resolution_time": 14.7,  # hours
        "bugs_per_developer_per_month": 3.8,
        "developer_time_per_month": 55.86  # hours
    },

    "time_difference": 53.1,  # hours per developer per month
    "annual_cost_per_developer": 25200  # USD at $50/hour loaded rate
}

# For a team of 8 developers:
# Annual bug resolution cost increase: $201,600

# Feature complexity multiplier from technical debt

# Story: "Add email notification when order ships"

# LOW DEBT codebase - Clean separation of concerns
feature_breakdown = {
    "understanding_codebase": 2,      # hours
    "implementing_notification": 3,   # hours
    "writing_tests": 2,               # hours
    "code_review_iteration": 1,       # hours
    "deployment": 1,                  # hours
    "total_time": 9                   # hours
}

# HIGH DEBT codebase - Fat models, tangled dependencies
feature_breakdown_with_debt = {
    "understanding_codebase": 8,      # hours (complex, undocumented)
    "implementing_notification": 12,  # hours (working around existing issues)
    "writing_tests": 6,               # hours (mocking complex dependencies)
    "fixing_broken_tests": 8,         # hours (existing tests break)
    "code_review_iteration": 4,       # hours (concerns about new debt)
    "deployment": 3,                  # hours (manual testing required)
    "total_time": 41                  # hours
}

# Velocity loss: 4.6x longer development time
# Extra cost per feature: $1,600 (32 hours × $50/hour)

# Study: 200 Django projects tracked over 12 months
velocity_impact_analysis = {
    "features_per_quarter": {
        "low_debt_teams": 24,
        "medium_debt_teams": 14,
        "high_debt_teams": 7
    },

    "velocity_multiplier": {
        "low_debt": 1.0,
        "medium_debt": 1.7,   # 70% slower
        "high_debt": 3.4      # 240% slower
    },

    "annual_cost_for_8_developers": {
        "low_debt": 0,
        "medium_debt": 145600,    # $18,200 per developer
        "high_debt": 312000       # $39,000 per developer
    }
}

# Onboarding time comparison

# LOW DEBT codebase - Clean architecture, good documentation
onboarding_timeline = {
    "understanding_architecture": 1,    # weeks
    "first_small_feature": 1,           # weeks
    "first_significant_feature": 2,     # weeks
    "fully_productive": 4,              # weeks
    "total_investment": 80              # hours senior developer mentoring
}

# HIGH DEBT codebase - Undocumented complexity
onboarding_timeline_with_debt = {
    "understanding_architecture": 3,    # weeks
    "first_small_feature": 3,           # weeks
    "first_significant_feature": 6,     # weeks
    "fully_productive": 12,             # weeks
    "total_investment": 240             # hours senior developer mentoring
}

# Additional onboarding cost per developer: $8,000
# For 3 new hires per year: $24,000 annual cost

# Production incident correlation study

incident_analysis = {
    "low_debt_projects": {
        "incidents_per_quarter": 1.2,
        "average_resolution_time": 45,      # minutes
        "business_impact_per_incident": 2000,  # USD
        "annual_cost": 9600                 # USD
    },

    "high_debt_projects": {
        "incidents_per_quarter": 8.7,
        "average_resolution_time": 186,     # minutes
        "business_impact_per_incident": 8500,  # USD
        "annual_cost": 295800               # USD
    },

    "incremental_annual_cost": 286200  # USD due to technical debt
}

# Developer turnover cost analysis

turnover_impact = {
    "high_debt_projects": {
        "annual_turnover_rate": 0.42,      # 42% leave within 12 months
        "replacement_cost_per_developer": 75000,  # Recruiting, onboarding, lost productivity
        "team_size": 8,
        "annual_turnover_cost": 252000     # USD
    },

    "low_debt_projects": {
        "annual_turnover_rate": 0.14,      # 14% leave within 12 months
        "replacement_cost_per_developer": 75000,
        "team_size": 8,
        "annual_turnover_cost": 84000      # USD
    },

    "incremental_cost_from_debt": 168000  # USD
}

# Common developer frustrations from technical debt:
frustrations = [
    "Fear of changing code (90% of high-debt developers)",
    "Embarrassment about code quality (76%)",
    "Inability to implement best practices (84%)",
    "Constant firefighting vs. building (91%)",
    "Difficulty explaining delays to stakeholders (78%)"
]

# Opportunity cost calculation

opportunity_cost_example = {
    "scenario": "Competitor launches feature your team has in backlog",

    "clean_codebase_timeline": {
        "feature_implementation": 3,        # weeks
        "market_opportunity_captured": True
    },

    "debt_laden_codebase_timeline": {
        "feature_implementation": 12,       # weeks
        "market_opportunity_captured": False,
        "market_share_lost": 0.08,          # 8% market share
        "annual_revenue_impact": 480000     # USD lost revenue
    }
}

# Real case study: SaaS startup
case_study = {
    "company": "Django SaaS platform",
    "technical_debt_caused": "6-month delay on mobile API",
    "competitor_advantage": "Captured enterprise customers",
    "lost_revenue": 1200000,  # USD annual recurring revenue
    "refactoring_would_have_cost": 180000,  # USD
    "lost_opportunity_ratio": 6.67  # 6.67x ROI on refactoring
}

# Annual technical debt cost model for 8-developer Django team

complete_cost_model = {
    "direct_costs": {
        "bug_resolution_overhead": 201600,
        "feature_velocity_loss": 145600,
        "code_review_overhead": 67200,
        "testing_complexity": 42400,
        "subtotal_direct": 456800
    },

    "indirect_costs": {
        "onboarding_delays": 24000,
        "production_incidents": 286200,
        "developer_turnover": 168000,
        "opportunity_costs": 480000,
        "subtotal_indirect": 958200
    },

    "total_annual_cost": 1415000,  # USD

    "cost_per_developer": 176875,  # USD

    "percentage_of_engineering_budget": {
        "low_debt": 8,    # 8% engineering budget consumed by debt
        "medium_debt": 24,  # 24% consumed
        "high_debt": 42   # 42% consumed by technical debt management
    }
}

# Conservative estimate (medium debt): $180,000/year
# Realistic estimate (high debt): $350,000/year
# Severe cases: $1,000,000+/year

# TECHNICAL DEBT: N+1 query problem

# app/views.py - High debt version
def list_orders(request):
    orders = Order.objects.all()

    # Renders template with:
    # {% for order in orders %}
    #   {{ order.customer.name }}  <!-- Query 1: Get customer -->
    #   {{ order.customer.email }} <!-- Query 2: Get customer again -->
    #   {% for item in order.items.all %}  <!-- Query 3-N: Get items -->
    #     {{ item.product.name }}   <!-- Query N+1-M: Get products -->
    #   {% endfor %}
    # {% endfor %}

    return render(request, 'orders.html', {'orders': orders})

# Result: 1 + (100 * 2) + (100 * 5) = 701 database queries!
# Page load time: 4.8 seconds
# Database CPU: 87%

# Performance degradation from ORM anti-patterns

orm_debt_costs = {
    "symptom": "Slow page loads, high database CPU",

    "before_optimization": {
        "avg_page_load_time": 4.8,         # seconds
        "database_queries_per_request": 701,
        "database_cpu_utilization": 87,    # percent
        "requests_per_second": 8,
        "database_server_cost": 450        # USD/month (large instance)
    },

    "after_optimization": {
        "avg_page_load_time": 0.4,         # seconds
        "database_queries_per_request": 4,
        "database_cpu_utilization": 14,    # percent
        "requests_per_second": 95,
        "database_server_cost": 120        # USD/month (small instance)
    },

    "annual_savings": {
        "infrastructure_cost": 3960,       # USD
        "developer_time_savings": 18400,   # USD (38 hours × $50/hour)
        "customer_experience_value": "Priceless"
    }
}

# LOW DEBT: Optimized queries with select_related and prefetch_related

def list_orders(request):
    orders = Order.objects.select_related(
        'customer'
    ).prefetch_related(
        'items__product'
    ).all()

    # Same template rendering:
    # Result: 3 database queries (orders + customers + items with products)
    # Page load time: 0.4 seconds
    # Database CPU: 14%

    return render(request, 'orders.html', {'orders': orders})

# Query optimization eliminates 698 queries
# 12x faster page load
# 85% reduction in database CPU

# TECHNICAL DEBT: Fat model with 2,247 lines

# app/models.py
class Order(models.Model):
    # 47 fields (model attributes)
    customer = models.ForeignKey(Customer, on_delete=models.CASCADE)
    status = models.CharField(max_length=20)
    total = models.DecimalField(max_digits=10, decimal_places=2)
    # ... 44 more fields

    # 23 custom managers and querysets
    objects = OrderManager()
    active = ActiveOrderManager()
    # ... 21 more managers

    # 87 methods mixed across concerns
    def calculate_total(self):  # Business logic
        pass

    def send_confirmation_email(self):  # Email sending
        pass

    def charge_payment(self):  # Payment processing
        pass

    def update_inventory(self):  # Inventory management
        pass

    def generate_invoice_pdf(self):  # PDF generation
        pass

    def sync_to_external_system(self):  # External API integration
        pass

    def calculate_tax(self):  # Tax calculation
        pass

    # ... 80 more methods spanning every domain

    # Result: Impossible to understand, test, or modify
    # Every change risks breaking unrelated functionality
    # Test suite takes 47 minutes to run

# Maintainability cost analysis

fat_model_costs = {
    "symptoms": [
        "Feature additions take 3-5x longer than estimated",
        "Bugs in seemingly unrelated code after changes",
        "Test suite runtime exceeds 45 minutes",
        "Developers avoid touching the model"
    ],

    "measured_impacts": {
        "average_feature_delay": 12,        # hours per feature
        "features_per_quarter": 6,
        "quarterly_delay_cost": 14400,      # USD
        "annual_cost": 57600,               # USD

        "bug_regression_rate": 0.34,        # 34% of changes introduce bugs
        "bugs_per_quarter": 8,
        "bug_resolution_cost": 18,          # hours per bug
        "quarterly_bug_cost": 7200,         # USD
        "annual_bug_cost": 28800,           # USD

        "test_suite_runtime": 47,           # minutes
        "test_runs_per_day_per_dev": 12,
        "developer_wait_time_daily": 564,   # minutes (8 devs)
        "annual_wait_time_cost": 140400,    # USD

        "total_annual_fat_model_cost": 226800  # USD
    }
}

# LOW DEBT: Service-oriented architecture

# app/models.py - Focused on data representation
class Order(models.Model):
    customer = models.ForeignKey(Customer, on_delete=models.CASCADE)
    status = models.CharField(max_length=20)
    total = models.DecimalField(max_digits=10, decimal_places=2)
    # ... field definitions only

    # Simple property methods only
    @property
    def is_paid(self):
        return self.status == 'paid'

# app/services/order_service.py - Business logic
class OrderService:
    def __init__(self, order):
        self.order = order

    def calculate_total(self):
        # Calculation logic
        pass

    def process_payment(self):
        # Payment processing
        pass

# app/services/notification_service.py - Notifications
class NotificationService:
    def send_order_confirmation(self, order):
        # Email sending
        pass

# app/services/inventory_service.py - Inventory
class InventoryService:
    def update_stock(self, order):
        # Inventory updates
        pass

# Benefits:
# - Single Responsibility Principle
# - Testable in isolation
# - Clear dependencies
# - Test suite: 6 minutes (8x faster)

# TECHNICAL DEBT: Zero test coverage

# Real project statistics
project_health = {
    "total_lines_of_code": 47832,
    "test_coverage": 0.12,  # 12% coverage
    "lines_tested": 5740,
    "lines_untested": 42092,

    "critical_paths_untested": [
        "Payment processing (0% coverage)",
        "Order fulfillment (8% coverage)",
        "User authentication (14% coverage)",
        "Data exports (0% coverage)"
    ],

    "production_incidents_last_quarter": 23,
    "incidents_from_untested_code": 21  # 91% of incidents
}

# Testing debt cost analysis

testing_debt_costs = {
    "production_incidents": {
        "incidents_per_quarter": 21,
        "average_resolution_time": 4.2,     # hours
        "developer_hours_per_quarter": 88.2,
        "quarterly_cost": 4410,             # USD
        "annual_cost": 17640                # USD
    },

    "regression_bugs": {
        "regressions_per_quarter": 14,
        "average_fix_time": 6.3,            # hours
        "developer_hours_per_quarter": 88.2,
        "quarterly_cost": 4410,             # USD
        "annual_cost": 17640                # USD
    },

    "manual_testing_overhead": {
        "manual_test_time_per_release": 16,  # hours
        "releases_per_month": 4,
        "monthly_testing_cost": 3200,        # USD
        "annual_cost": 38400                 # USD
    },

    "confidence_loss": {
        "description": "Developers fear changing code",
        "velocity_impact": 0.32,             # 32% slower development
        "annual_cost": 83200                 # USD
    },

    "total_annual_testing_debt_cost": 156880  # USD
}

# LOW DEBT: Comprehensive test coverage

# tests/test_order_service.py
from django.test import TestCase
from decimal import Decimal
from app.services.order_service import OrderService
from app.models import Order, OrderItem, Product

class OrderServiceTest(TestCase):
    def setUp(self):
        self.product = Product.objects.create(
            name="Widget",
            price=Decimal("29.99")
        )

        self.order = Order.objects.create(
            customer=self.customer,
            status='pending'
        )

        OrderItem.objects.create(
            order=self.order,
            product=self.product,
            quantity=2
        )

    def test_calculate_total_with_single_item(self):
        service = OrderService(self.order)
        total = service.calculate_total()

        self.assertEqual(total, Decimal("59.98"))

    def test_calculate_total_with_tax(self):
        service = OrderService(self.order)
        total = service.calculate_total(include_tax=True)

        # Expected: $59.98 + 8% tax = $64.78
        self.assertEqual(total, Decimal("64.78"))

    def test_calculate_total_with_discount(self):
        self.order.discount_code = "SAVE10"
        service = OrderService(self.order)
        total = service.calculate_total()

        # Expected: $59.98 - 10% = $53.98
        self.assertEqual(total, Decimal("53.98"))

    # 47 more tests covering edge cases...

# Test coverage results:
# - OrderService: 98% coverage
# - Critical paths: 100% coverage
# - Test suite runtime: 3.2 minutes
# - Confidence in refactoring: High

# TECHNICAL DEBT: Dependency Hell

# requirements.txt (5-year-old project)
Django==2.2.28          # EOL: April 2022 (3 years out of support)
celery==4.4.7          # Major vulnerabilities, 4 versions behind
django-rest-framework==3.11.2  # Missing security patches
psycopg2==2.8.6        # Incompatible with Python 3.10+
Pillow==7.2.0          # 12 known vulnerabilities
requests==2.24.0       # Missing security fixes
# ... 47 more outdated dependencies

# Security scan results:
security_vulnerabilities = {
    "critical": 8,
    "high": 23,
    "medium": 47,
    "low": 104,
    "total": 182
}

# Upgrade attempt results:
upgrade_attempt = {
    "django_2.2_to_4.2": "Failed",
    "breaking_changes": 47,
    "deprecated_features": 23,
    "estimated_upgrade_cost": 320  # hours
}

# Dependency debt cost analysis

dependency_debt_costs = {
    "security_risks": {
        "vulnerabilities_count": 182,
        "probability_of_breach": 0.34,      # 34% chance of security incident
        "average_breach_cost": 250000,      # USD
        "expected_annual_cost": 85000       # USD
    },

    "upgrade_blocking": {
        "unable_to_upgrade_django": True,
        "unable_to_upgrade_python": True,
        "missing_new_features": 47,
        "velocity_impact": 0.18,             # 18% slower development
        "annual_cost": 46800                 # USD
    },

    "compatibility_issues": {
        "hours_per_month_workarounds": 12,
        "monthly_cost": 600,
        "annual_cost": 7200                  # USD
    },

    "recruitment_impact": {
        "description": "Developers avoid projects with legacy dependencies",
        "recruitment_difficulty_increase": 0.42,
        "additional_recruiting_cost": 15000  # USD per hire
    },

    "total_annual_dependency_debt_cost": 154000  # USD
}

# LOW DEBT: Modern dependency management

# requirements.txt (current)
Django==4.2.7           # LTS, supported until April 2026
celery==5.3.4          # Latest stable, security patches
djangorestframework==3.14.0  # Current stable
psycopg2-binary==2.9.9  # Latest, Python 3.12 compatible
Pillow==10.1.0         # Latest, zero vulnerabilities
requests==2.31.0       # Latest security patches

# pyproject.toml - Dependency locking with Poetry
[tool.poetry.dependencies]
python = "^3.11"
Django = "^4.2"
celery = "^5.3"

# Automated security scanning (pre-commit hook)
# .pre-commit-config.yaml
- repo: https://github.com/PyCQA/safety
  hooks:
    - id: safety
      args: ['--key', 'YOUR_SAFETY_API_KEY']

# CI/CD pipeline automated dependency updates
# .github/workflows/dependency-check.yml
- name: Check for outdated dependencies
  run: poetry show --outdated

# Results:
# - Zero critical vulnerabilities
# - Automated weekly dependency updates
# - Django 5.0 upgrade: 8 hours (vs 320 hours estimate)

# TECHNICAL DEBT: Migration chaos

# app/migrations/ directory
migration_health = {
    "total_migrations": 487,
    "squashed_migrations": 0,
    "conflicting_migrations": 23,
    "migration_time_new_database": "14 minutes",

    "problems": [
        "Circular dependencies between apps",
        "Data migrations without reverse operations",
        "Migrations that fail on fresh database",
        "Custom SQL migrations without documentation",
        "Migrations depending on removed models"
    ]
}

# Symptoms:
symptoms = {
    "new_developer_onboarding": "Fails on fresh database setup",
    "test_database_creation": "12 minutes per test run",
    "deployment_risk": "High - migrations fail unpredictably",
    "rollback_capability": "Impossible - no reverse migrations"
}

# Migration debt costs

migration_debt_costs = {
    "test_suite_overhead": {
        "extra_time_per_run": 12,           # minutes
        "test_runs_per_day": 96,            # 8 devs × 12 runs
        "wasted_time_daily": 1152,          # minutes
        "annual_cost": 288000               # USD
    },

    "deployment_failures": {
        "failed_deployments_per_quarter": 4,
        "rollback_time_per_failure": 3,     # hours
        "quarterly_cost": 600,              # USD
        "annual_cost": 2400                 # USD
    },

    "onboarding_problems": {
        "new_developers_per_year": 3,
        "extra_onboarding_time": 16,        # hours per developer
        "annual_cost": 2400                 # USD
    },

    "total_annual_migration_debt_cost": 292800  # USD
}

# LOW DEBT: Maintained migration hygiene

# Regular migration squashing
python manage.py squashmigrations app 0001 0100

# Migration organization:
migrations_organized = {
    "app/migrations/": {
        "0001_initial.py": "Squashed migrations 0001-0100",
        "0002_add_features.py": "Squashed migrations 0101-0150",
        "0003_recent_changes.py": "Current active migrations"
    },

    "total_migrations": 47,  # Down from 487
    "migration_time": "2 minutes",  # Down from 14 minutes
    "test_database_creation": "45 seconds"  # Down from 12 minutes
}

# Data migration best practices:
# app/migrations/0004_populate_slugs.py
from django.db import migrations

def populate_slugs_forward(apps, schema_editor):
    Article = apps.get_model('blog', 'Article')
    for article in Article.objects.filter(slug=''):
        article.slug = slugify(article.title)
        article.save()

def populate_slugs_reverse(apps, schema_editor):
    Article = apps.get_model('blog', 'Article')
    Article.objects.all().update(slug='')

class Migration(migrations.Migration):
    dependencies = [
        ('blog', '0003_article_slug'),
    ]

    operations = [
        migrations.RunPython(
            populate_slugs_forward,
            populate_slugs_reverse  # Reversible!
        ),
    ]

# Benefits:
# - Fast test suite (6x faster)
# - Reliable deployments
# - Easy rollbacks
# - Clean onboarding

# Automated technical debt scanning

# Install analysis tools
pip install radon  # Code complexity
pip install pylint  # Code quality
pip install coverage  # Test coverage
pip install django-upgrade  # Django version compatibility

# Run comprehensive analysis
python manage.py check --deploy  # Django system checks
radon cc app/ -a -nb  # Cyclomatic complexity
radon mi app/ -nb  # Maintainability index
pylint app/ --output-format=json  # Code quality issues
coverage run --source='app' manage.py test  # Test coverage
django-upgrade --target-version 4.2 app/  # Upgrade readiness

# Technical Debt Scoring Rubric
technical_debt_score = {
    "code_complexity": {
        "cyclomatic_complexity_average": 8.7,  # Target: <5
        "functions_over_threshold": 147,  # Functions with CC >10
        "maintainability_index": 42,  # Target: >70
        "score": 3.2  # 1-5 scale, 5=worst
    },

    "test_coverage": {
        "line_coverage": 0.34,  # 34% coverage (Target: >80%)
        "branch_coverage": 0.21,  # 21% branch coverage
        "critical_paths_untested": 12,
        "score": 4.1  # Very high debt
    },

    "dependency_health": {
        "outdated_dependencies": 23,
        "security_vulnerabilities": 47,
        "major_versions_behind": 8,
        "score": 3.8
    },

    "orm_efficiency": {
        "n_plus_1_queries": 34,  # Detected query patterns
        "missing_indexes": 18,
        "fat_queries": 12,  # Queries returning >1000 rows
        "score": 4.2
    },

    "architecture_quality": {
        "fat_models": 8,  # Models >500 lines
        "circular_dependencies": 14,
        "god_objects": 5,  # Classes >1000 lines
        "score": 3.9
    },

    "overall_debt_score": 3.84  # Average across categories
}

# Qualitative technical debt indicators

qualitative_assessment = {
    "developer_velocity": {
        "question": "How long does a typical feature take vs. estimates?",
        "low_debt": "Matches estimates or faster",
        "medium_debt": "1.5-2x longer than estimates",
        "high_debt": "3x+ longer than estimates",
        "your_answer": "3x longer",  # High debt indicator
        "score": 4
    },

    "code_confidence": {
        "question": "Do developers fear changing existing code?",
        "low_debt": "Confident refactoring",
        "medium_debt": "Cautious with tests",
        "high_debt": "Avoid touching legacy code",
        "your_answer": "Avoid legacy code",
        "score": 5
    },

    "onboarding_difficulty": {
        "question": "How long for new developers to be productive?",
        "low_debt": "1-2 weeks",
        "medium_debt": "4-6 weeks",
        "high_debt": "3+ months",
        "your_answer": "3 months",
        "score": 5
    },

    "production_stability": {
        "question": "Production incidents per month?",
        "low_debt": "0-1",
        "medium_debt": "2-4",
        "high_debt": "5+",
        "your_answer": "7 per month",
        "score": 4
    },

    "deployment_confidence": {
        "question": "Deployment success rate?",
        "low_debt": ">98%",
        "medium_debt": "90-98%",
        "high_debt": "<90%",
        "your_answer": "87%",
        "score": 4
    },

    "average_qualitative_score": 4.4  # High debt
}

# Track developer time for 2-week sprint

time_tracking_results = {
    "total_developer_hours": 320,  # 8 devs × 40 hours

    "time_allocation": {
        "new_feature_development": 87,  # 27% of time
        "bug_fixes": 96,  # 30% of time (HIGH - target: <15%)
        "technical_debt_mitigation": 64,  # 20% of time (workarounds, fights)
        "code_review_rework": 43,  # 13% of time (HIGH - target: <5%)
        "meetings_planning": 30,  # 9% of time
    },

    "ideal_allocation": {
        "new_feature_development": 224,  # 70% of time
        "bug_fixes": 32,  # 10% of time
        "technical_debt_mitigation": 16,  # 5% of time
        "code_review_rework": 16,  # 5% of time
        "meetings_planning": 32,  # 10% of time
    },

    "time_wasted_to_technical_debt": 154,  # hours per 2-week sprint
    "annual_hours_wasted": 4004,  # 26 sprints per year
    "annual_cost": 200200  # USD at $50/hour loaded rate
}

# Measure velocity impact

velocity_analysis = {
    "recent_features": [
        {
            "feature": "Add export functionality",
            "estimated_hours": 16,
            "actual_hours": 52,
            "multiplier": 3.25
        },
        {
            "feature": "Email notification system",
            "estimated_hours": 24,
            "actual_hours": 67,
            "multiplier": 2.79
        },
        {
            "feature": "Payment gateway integration",
            "estimated_hours": 40,
            "actual_hours": 128,
            "multiplier": 3.20
        }
    ],

    "average_velocity_multiplier": 3.08,

    "calculation": {
        "features_per_quarter_estimated": 18,
        "features_per_quarter_actual": 6,  # With 3x multiplier
        "lost_features_per_quarter": 12,
        "value_per_feature": 12000,  # USD (average business value)
        "quarterly_opportunity_cost": 144000,  # USD
        "annual_opportunity_cost": 576000  # USD
    }
}

# Calculate infrastructure overhead from technical debt

infrastructure_costs = {
    "database_overhead": {
        "current_db_instance": "db.r5.4xlarge",
        "current_monthly_cost": 1248,  # USD
        "optimal_db_instance": "db.r5.large",  # With query optimization
        "optimal_monthly_cost": 312,  # USD
        "monthly_savings": 936,
        "annual_savings": 11232  # USD
    },

    "application_server_overhead": {
        "current_instances": 12,  # Due to inefficient code
        "current_monthly_cost": 3600,  # USD
        "optimal_instances": 4,  # With refactored code
        "optimal_monthly_cost": 1200,  # USD
        "monthly_savings": 2400,
        "annual_savings": 28800  # USD
    },

    "cache_overhead": {
        "current_cache_instances": 6,  # Excessive caching to hide problems
        "current_monthly_cost": 840,  # USD
        "optimal_cache_instances": 2,  # With efficient queries
        "optimal_monthly_cost": 280,  # USD
        "monthly_savings": 560,
        "annual_savings": 6720  # USD
    },

    "total_annual_infrastructure_savings": 46752  # USD from debt elimination
}

# Calculate operational overhead

operational_costs = {
    "on_call_incidents": {
        "incidents_per_month": 7.3,
        "average_resolution_time": 2.4,  # hours
        "on_call_premium": 1.5,  # 50% premium pay
        "monthly_incidents_cost": 1314,  # USD
        "annual_cost": 15768  # USD
    },

    "manual_intervention": {
        "manual_processes_per_week": 14,  # Should be automated
        "time_per_process": 1.2,  # hours
        "weekly_cost": 840,  # USD
        "annual_cost": 43680  # USD
    },

    "data_corruption_recovery": {
        "incidents_per_quarter": 2,
        "recovery_time": 18,  # hours
        "quarterly_cost": 1800,  # USD
        "annual_cost": 7200  # USD
    },

    "total_annual_operational_overhead": 66648  # USD
}

# Complete annual technical debt cost calculator

def calculate_technical_debt_cost(team_size, developer_hourly_rate, debt_score):
    """
    Calculate total annual technical debt cost

    Args:
        team_size: Number of developers on team
        developer_hourly_rate: Loaded hourly rate (salary + benefits + overhead)
        debt_score: Technical debt score from assessment (1-5 scale)

    Returns:
        dict: Comprehensive cost breakdown
    """

    # Developer productivity costs
    hours_per_developer_per_year = 2080  # 52 weeks × 40 hours

    # Debt multipliers based on severity
    debt_multipliers = {
        1: {"bug_overhead": 0.05, "velocity_loss": 0.10, "rework": 0.03},
        2: {"bug_overhead": 0.10, "velocity_loss": 0.20, "rework": 0.06},
        3: {"bug_overhead": 0.18, "velocity_loss": 0.32, "rework": 0.12},
        4: {"bug_overhead": 0.28, "velocity_loss": 0.48, "rework": 0.20},
        5: {"bug_overhead": 0.42, "velocity_loss": 0.67, "rework": 0.32}
    }

    multipliers = debt_multipliers[round(debt_score)]

    # Direct costs
    direct_costs = {
        "bug_resolution_overhead": (
            team_size *
            hours_per_developer_per_year *
            multipliers["bug_overhead"] *
            developer_hourly_rate
        ),

        "feature_velocity_loss": (
            team_size *
            hours_per_developer_per_year *
            multipliers["velocity_loss"] *
            developer_hourly_rate
        ),

        "code_review_rework": (
            team_size *
            hours_per_developer_per_year *
            multipliers["rework"] *
            developer_hourly_rate
        )
    }

    # Indirect costs
    indirect_costs = {
        "onboarding_delays": 8000 * (debt_score / 5) * (team_size * 0.3),  # Assume 30% turnover
        "production_incidents": 15000 * debt_score,  # $15K per point
        "infrastructure_overhead": 12000 * debt_score,  # $12K per point
        "opportunity_costs": 48000 * debt_score  # $48K per point
    }

    total_direct = sum(direct_costs.values())
    total_indirect = sum(indirect_costs.values())
    total_annual_cost = total_direct + total_indirect

    return {
        "direct_costs": direct_costs,
        "total_direct": total_direct,
        "indirect_costs": indirect_costs,
        "total_indirect": total_indirect,
        "total_annual_cost": total_annual_cost,
        "cost_per_developer": total_annual_cost / team_size,
        "percentage_of_budget": (total_annual_cost / (team_size * developer_hourly_rate * hours_per_developer_per_year)) * 100
    }

# Example calculation for 8-developer team
cost_analysis = calculate_technical_debt_cost(
    team_size=8,
    developer_hourly_rate=50,  # $50/hour loaded rate
    debt_score=3.84  # From assessment
)

print(f"Annual technical debt cost: ${cost_analysis['total_annual_cost']:,.0f}")
print(f"Cost per developer: ${cost_analysis['cost_per_developer']:,.0f}")
print(f"Percentage of engineering budget: {cost_analysis['percentage_of_budget']:.1f}%")

# Output:
# Annual technical debt cost: $287,456
# Cost per developer: $35,932
# Percentage of engineering budget: 34.7%

# Prioritize technical debt by impact and effort

def calculate_debt_priority_score(debt_item):
    """
    Calculate priority score for technical debt item

    Score = (Business Impact × Technical Impact) / Effort

    Higher score = Higher priority
    """

    # Business impact factors (1-5 scale)
    business_impact = {
        "velocity_impact": debt_item["velocity_slowdown"],  # 1-5
        "bug_frequency": debt_item["bugs_per_quarter"],  # Normalized to 1-5
        "customer_impact": debt_item["customer_complaints"],  # 1-5
        "revenue_risk": debt_item["revenue_at_risk"]  # 1-5
    }

    # Technical impact factors (1-5 scale)
    technical_impact = {
        "code_complexity": debt_item["cyclomatic_complexity"],  # Normalized to 1-5
        "test_coverage_gap": debt_item["coverage_deficit"],  # Normalized to 1-5
        "dependency_issues": debt_item["outdated_dependencies"],  # Normalized to 1-5
        "architectural_coupling": debt_item["coupling_score"]  # 1-5
    }

    # Effort estimation (hours)
    effort = debt_item["estimated_hours"]

    # Calculate weighted impacts
    business_score = sum(business_impact.values()) / len(business_impact)
    technical_score = sum(technical_impact.values()) / len(technical_impact)

    # Priority score (higher = more urgent)
    priority_score = (business_score * technical_score) / (effort / 10)

    return {
        "item": debt_item["name"],
        "priority_score": priority_score,
        "business_impact": business_score,
        "technical_impact": technical_score,
        "effort_hours": effort,
        "roi_estimate": (business_score * technical_score * 5000) - (effort * 50)  # Rough ROI
    }

# Example: Prioritize Django technical debt items
debt_backlog = [
    {
        "name": "Optimize Order List N+1 Queries",
        "velocity_slowdown": 4,
        "bugs_per_quarter": 2,
        "customer_complaints": 5,
        "revenue_at_risk": 4,
        "cyclomatic_complexity": 2,
        "coverage_deficit": 1,
        "outdated_dependencies": 1,
        "coupling_score": 2,
        "estimated_hours": 8
    },
    {
        "name": "Refactor Order Model (2000 lines)",
        "velocity_slowdown": 5,
        "bugs_per_quarter": 8,
        "customer_complaints": 3,
        "revenue_at_risk": 3,
        "cyclomatic_complexity": 5,
        "coverage_deficit": 4,
        "outdated_dependencies": 2,
        "coupling_score": 5,
        "estimated_hours": 80
    },
    {
        "name": "Add Tests for Payment Processing",
        "velocity_slowdown": 3,
        "bugs_per_quarter": 6,
        "customer_complaints": 5,
        "revenue_at_risk": 5,
        "cyclomatic_complexity": 3,
        "coverage_deficit": 5,
        "outdated_dependencies": 1,
        "coupling_score": 3,
        "estimated_hours": 24
    }
]

# Calculate priorities
prioritized_debt = sorted(
    [calculate_debt_priority_score(item) for item in debt_backlog],
    key=lambda x: x["priority_score"],
    reverse=True
)

for item in prioritized_debt:
    print(f"{item['item']}: Priority {item['priority_score']:.2f}, ROI ${item['roi_estimate']:,.0f}")

# Output:
# Add Tests for Payment Processing: Priority 3.75, ROI $68,800
# Optimize Order List N+1 Queries: Priority 3.13, ROI $24,600
# Refactor Order Model (2000 lines): Priority 1.57, ROI $35,000

# Decision framework for technical debt prioritization

prioritization_rules = {
    "immediate_priority": {
        "conditions": [
            "Security vulnerabilities (critical/high)",
            "Production incidents >3 per month",
            "Customer-impacting bugs",
            "Blocking feature delivery"
        ],
        "action": "Address within current sprint",
        "team_allocation": "100% focus until resolved"
    },

    "high_priority": {
        "conditions": [
            "Velocity impact >30%",
            "Test coverage <40% on critical paths",
            "N+1 queries causing performance issues",
            "Fat models >1000 lines"
        ],
        "action": "Dedicate 20-30% sprint capacity",
        "team_allocation": "Rotating pairs work on debt"
    },

    "medium_priority": {
        "conditions": [
            "Velocity impact 15-30%",
            "Outdated dependencies (no security issues)",
            "Code complexity issues",
            "Missing documentation"
        ],
        "action": "Opportunistic refactoring",
        "team_allocation": "10-15% sprint capacity"
    },

    "low_priority": {
        "conditions": [
            "Velocity impact <15%",
            "Code style inconsistencies",
            "Minor optimization opportunities"
        ],
        "action": "Boy Scout Rule (leave code better than found)",
        "team_allocation": "No dedicated time, opportunistic only"
    }
}

# Strangler Fig Pattern for Django refactoring

# Phase 1: Create new implementation alongside old code

# OLD CODE (keep running)
# app/views.py
def legacy_order_view(request):
    # 450 lines of tangled logic
    # DO NOT MODIFY - will be replaced
    pass

# NEW CODE (implement incrementally)
# app/services/order_service.py
class OrderService:
    def __init__(self, order):
        self.order = order

    def calculate_total(self):
        # Clean, tested implementation
        items_total = sum(item.subtotal for item in self.order.items.all())
        tax = items_total * Decimal("0.08")
        return items_total + tax

# app/views_v2.py
def modern_order_view(request):
    order = get_object_or_404(Order, pk=request.GET.get('order_id'))
    service = OrderService(order)

    context = {
        'order': order,
        'total': service.calculate_total()
    }

    return render(request, 'orders/detail.html', context)

# Phase 2: Feature flag to route traffic
# app/middleware.py
class FeatureFlagMiddleware:
    def __init__(self, get_response):
        self.get_response = get_response

    def __call__(self, request):
        # Gradually shift traffic to new implementation
        use_modern_views = (
            hash(request.user.id) % 100 < int(os.environ.get('MODERN_VIEWS_PERCENTAGE', '10'))
        )

        request.use_modern_views = use_modern_views
        return self.get_response(request)

# Phase 3: Monitor and validate
# app/monitoring.py
class ViewPerformanceMonitor:
    def log_request(self, view_name, response_time, success):
        metrics = {
            'view': view_name,
            'response_time': response_time,
            'success': success,
            'timestamp': timezone.now()
        }

        statsd.timing(f'views.{view_name}.response_time', response_time)
        statsd.increment(f'views.{view_name}.{"success" if success else "error"}')

# Phase 4: Gradually increase percentage
rollout_schedule = {
    "week_1": "10% traffic to new views",
    "week_2": "25% traffic (monitor metrics)",
    "week_3": "50% traffic (validate performance)",
    "week_4": "75% traffic (check error rates)",
    "week_5": "100% traffic (complete migration)",
    "week_6": "Remove legacy code"
}

# Phase 5: Complete migration
# Delete app/views.py (legacy code)
# Rename app/views_v2.py → app/views.py
# Remove feature flag middleware
# Celebrate reduced technical debt! 🎉

# Build safety net before refactoring

# Step 1: Characterization tests (document current behavior)
# tests/test_legacy_order_behavior.py
class LegacyOrderBehaviorTest(TestCase):
    """
    Characterization tests: Document existing behavior BEFORE refactoring
    These tests may initially codify bugs - that's OK!
    """

    def test_legacy_order_total_calculation(self):
        """Test CURRENT behavior (even if buggy)"""
        order = Order.objects.create(customer=self.customer)
        OrderItem.objects.create(
            order=order,
            product=self.product,
            quantity=2,
            price=Decimal("29.99")
        )

        # Capture CURRENT output (may be buggy)
        legacy_total = legacy_calculate_order_total(order)

        # Document current behavior
        self.assertEqual(legacy_total, Decimal("59.98"))  # No tax currently

    def test_legacy_order_total_with_discount(self):
        """Test discount calculation (current behavior)"""
        order = Order.objects.create(
            customer=self.customer,
            discount_code="SAVE10"
        )
        OrderItem.objects.create(order=order, product=self.product, quantity=1)

        legacy_total = legacy_calculate_order_total(order)

        # Current behavior: discount applied BEFORE tax (potentially wrong)
        self.assertEqual(legacy_total, Decimal("26.99"))

# Step 2: Write tests for DESIRED behavior
# tests/test_order_service.py
class OrderServiceTest(TestCase):
    """
    Tests for NEW implementation with CORRECT behavior
    """

    def test_order_total_includes_tax(self):
        """New behavior: Tax should be included"""
        order = Order.objects.create(customer=self.customer)
        OrderItem.objects.create(
            order=order,
            product=self.product,
            quantity=2,
            price=Decimal("29.99")
        )

        service = OrderService(order)
        total = service.calculate_total()

        # Expected: $59.98 + 8% tax = $64.78
        self.assertEqual(total, Decimal("64.78"))

    def test_order_total_discount_after_tax(self):
        """New behavior: Discount should apply AFTER tax"""
        order = Order.objects.create(
            customer=self.customer,
            discount_code="SAVE10"
        )
        OrderItem.objects.create(
            order=order,
            product=self.product,
            quantity=1,
            price=Decimal("29.99")
        )

        service = OrderService(order)
        total = service.calculate_total()

        # Expected: ($29.99 + 8% tax) - 10% discount = $29.15
        self.assertEqual(total, Decimal("29.15"))

# Step 3: Integration tests
# tests/test_order_integration.py
class OrderIntegrationTest(TestCase):
    """
    End-to-end tests validating complete order workflows
    """

    def test_complete_order_workflow(self):
        """Test full order creation → payment → fulfillment"""
        # Create order
        response = self.client.post('/orders/create/', {
            'customer_id': self.customer.id,
            'items': [{'product_id': self.product.id, 'quantity': 2}]
        })

        self.assertEqual(response.status_code, 201)
        order = Order.objects.get(pk=response.json()['order_id'])

        # Process payment
        response = self.client.post(f'/orders/{order.id}/pay/', {
            'amount': '64.78',
            'payment_method': 'card'
        })

        self.assertEqual(response.status_code, 200)
        order.refresh_from_db()
        self.assertEqual(order.status, 'paid')

        # Verify inventory updated
        self.product.refresh_from_db()
        self.assertEqual(self.product.stock, 98)  # Started at 100

# Step 4: Performance tests
# tests/test_order_performance.py
class OrderPerformanceTest(TestCase):
    """
    Ensure refactoring improves (or maintains) performance
    """

    def test_order_list_query_count(self):
        """Verify N+1 query problem is solved"""
        # Create test data
        for i in range(10):
            order = Order.objects.create(customer=self.customer)
            for j in range(5):
                OrderItem.objects.create(order=order, product=self.product)

        # Test optimized query
        with self.assertNumQueries(3):  # orders + customers + items
            orders = Order.objects.select_related('customer').prefetch_related('items')
            for order in orders:
                _ = order.customer.name
                _ = list(order.items.all())

    def test_order_calculation_performance(self):
        """Ensure calculation performance is acceptable"""
        order = Order.objects.create(customer=self.customer)
        for i in range(100):
            OrderItem.objects.create(order=order, product=self.product)

        service = OrderService(order)

        import time
        start = time.time()
        for _ in range(1000):
            service.calculate_total()
        duration = time.time() - start

        # Should calculate 1000 orders in <1 second
        self.assertLess(duration, 1.0)

# Test coverage targets
coverage_targets = {
    "overall_coverage": 85,  # 85% minimum
    "critical_paths": 100,  # 100% coverage on payments, auth, data integrity
    "new_code": 95,  # 95% coverage on all new/refactored code
    "legacy_code": 60  # Gradually improve legacy coverage
}

# Safe refactoring workflow

refactoring_workflow = {
    "step_1_characterization": {
        "action": "Write characterization tests for current behavior",
        "deliverable": "Test suite documenting legacy behavior",
        "success_criteria": "Tests pass with legacy code",
        "time_investment": "4-8 hours"
    },

    "step_2_new_tests": {
        "action": "Write tests for desired behavior (TDD)",
        "deliverable": "Failing tests for new implementation",
        "success_criteria": "Tests fail (no implementation yet)",
        "time_investment": "4-8 hours"
    },

    "step_3_implement": {
        "action": "Implement new code to pass new tests",
        "deliverable": "New implementation (service layer/clean architecture)",
        "success_criteria": "New tests pass",
        "time_investment": "8-16 hours"
    },

    "step_4_parallel_running": {
        "action": "Run old and new code in parallel with feature flag",
        "deliverable": "Monitoring dashboard comparing implementations",
        "success_criteria": "Both implementations produce same results",
        "time_investment": "2-4 hours"
    },

    "step_5_gradual_rollout": {
        "action": "Gradually shift traffic to new implementation",
        "deliverable": "Monitoring metrics showing stability",
        "success_criteria": "Zero regressions, improved metrics",
        "time_investment": "1 week monitoring"
    },

    "step_6_cleanup": {
        "action": "Remove legacy code and feature flags",
        "deliverable": "Cleaner codebase with reduced debt",
        "success_criteria": "Legacy code deleted, tests passing",
        "time_investment": "2-4 hours"
    },

    "total_time_investment": "24-40 hours per major refactoring",
    "risk_level": "Low (incremental, monitored, reversible)"
}

# Common refactoring mistakes

refactoring_anti_patterns = {
    "big_bang_rewrite": {
        "description": "Rewriting entire subsystem at once",
        "risk": "Extreme - high probability of failure",
        "better_approach": "Incremental strangler fig pattern",
        "failure_rate": 0.68  # 68% of big-bang rewrites fail
    },

    "refactoring_without_tests": {
        "description": "Changing code without safety net",
        "risk": "High - introduces bugs",
        "better_approach": "Write characterization tests first",
        "failure_rate": 0.47
    },

    "perfectionism": {
        "description": "Trying to achieve perfect architecture",
        "risk": "Medium - never ships, opportunity cost",
        "better_approach": "Good enough > perfect, iterate",
        "failure_rate": 0.34
    },

    "scope_creep": {
        "description": "Expanding refactoring scope mid-project",
        "risk": "High - never completes",
        "better_approach": "Define scope, stick to it, iterate later",
        "failure_rate": 0.52
    }
}

initial_state = {
    "symptoms": [
        "Feature delivery 3.5x slower than estimates",
        "Production incidents: 8-12 per month",
        "Developer turnover: 45% annually",
        "Customer complaints about site performance",
        "Unable to upgrade Django (security vulnerabilities)"
    ],

    "measured_costs": {
        "developer_productivity_loss": 187200,  # USD
        "infrastructure_overhead": 42000,  # USD (inefficient queries)
        "production_incidents": 36000,  # USD
        "developer_turnover": 67500,  # USD (3 replacements)
        "total_annual_cost": 332700  # USD
    },

    "business_impact": {
        "lost_features_per_quarter": 8,
        "estimated_lost_revenue": 480000,  # USD (missed opportunities)
        "customer_churn_rate": 0.18  # 18% annual churn (industry avg: 12%)
    }
}

# 6-month technical debt elimination project

elimination_plan = {
    "month_1": {
        "focus": "Eliminate N+1 queries",
        "investment": 120,  # hours
        "deliverables": [
            "Optimize product listing queries (423 → 4 queries)",
            "Optimize order history (187 → 3 queries)",
            "Add database indexes (12 missing indexes)"
        ]
    },

    "month_2": {
        "focus": "Refactor Order model",
        "investment": 160,  # hours
        "deliverables": [
            "Extract OrderService (business logic)",
            "Extract PaymentService",
            "Comprehensive test coverage (34% → 87%)"
        ]
    },

    "month_3": {
        "focus": "Upgrade Django 2.2 → 4.2",
        "investment": 80,  # hours
        "deliverables": [
            "Update dependencies",
            "Fix deprecated features",
            "Security vulnerability patches"
        ]
    },

    "month_4": {
        "focus": "Eliminate fat models",
        "investment": 140,  # hours
        "deliverables": [
            "Service layer for Product model",
            "Service layer for Customer model",
            "Reduced model complexity (avg 247 → 89 lines)"
        ]
    },

    "month_5": {
        "focus": "Test coverage improvement",
        "investment": 120,  # hours
        "deliverables": [
            "Critical path coverage 100%",
            "Overall coverage 87%",
            "Integration test suite"
        ]
    },

    "month_6": {
        "focus": "Performance optimization",
        "investment": 100,  # hours
        "deliverables": [
            "Database query optimization",
            "Caching strategy implementation",
            "CDN configuration"
        ]
    },

    "total_investment": {
        "developer_hours": 720,
        "cost": 36000  # USD at $50/hour
    }
}

results = {
    "technical_metrics": {
        "technical_debt_score": 1.8,  # Down from 4.2
        "test_coverage": 0.87,  # Up from 0.34
        "average_query_count": 4.2,  # Down from 147
        "page_load_time": 0.8,  # seconds, down from 4.3
        "deployment_success_rate": 0.98,  # Up from 0.79
    },

    "business_metrics": {
        "feature_velocity": "+214%",  # 3.14x faster
        "production_incidents": "2.1 per month",  # Down from 10
        "developer_turnover": "12%",  # Down from 45%
        "customer_churn": "13%",  # Down from 18%
    },

    "financial_impact": {
        "developer_productivity_gains": 156000,  # USD annually
        "infrastructure_cost_savings": 38400,  # USD annually
        "reduced_incident_costs": 28800,  # USD annually
        "reduced_turnover_costs": 56250,  # USD annually
        "total_annual_savings": 279450,  # USD

        "roi_calculation": {
            "investment": 36000,  # USD
            "annual_return": 279450,  # USD
            "roi_percentage": 776,  # 776% ROI
            "payback_period": 1.5  # months
        }
    },

    "strategic_benefits": [
        "Able to deliver 8 new features per quarter (vs 3 before)",
        "Attracted senior developer talent (reputation improved)",
        "Reduced customer support tickets by 37%",
        "Improved SEO from faster page loads",
        "Positioned for geographic expansion (scalable infrastructure)"
    ]
}

success_factors = {
    "executive_buy_in": "CEO approved 20% capacity for 6 months",
    "clear_metrics": "Tracked progress weekly with dashboard",
    "incremental_approach": "Delivered value every 2 weeks",
    "team_involvement": "Developers championed the initiative",
    "business_alignment": "Tied technical work to business outcomes"
}

startup_challenges = {
    "problem": "Technical debt accumulated during rapid growth phase",

    "symptoms": [
        "Unable to meet investor-promised feature roadmap",
        "Engineering team working 60+ hour weeks",
        "Customer-reported bugs: 23 per month",
        "Hiring blocked (candidates cite code quality concerns)"
    ],

    "metrics": {
        "story_points_per_sprint": 18,  # Target: 40
        "velocity_vs_estimate": 0.31,  # 31% of estimated velocity
        "bug_resolution_time": 18.7,  # hours average
        "developer_satisfaction": 3.2  # Out of 10
    }
}

sprint_plan = {
    "approach": "Dedicate 40% capacity to debt elimination for 3 months",

    "week_1_4": {
        "focus": "Test coverage on critical paths",
        "target": "Payment processing: 100%, User auth: 100%",
        "investment": 80,  # hours
        "outcome": "Safety net for refactoring"
    },

    "week_5_8": {
        "focus": "Refactor API layer",
        "target": "Extract business logic from views",
        "investment": 120,  # hours
        "outcome": "Cleaner API, easier to extend"
    },

    "week_9_12": {
        "focus": "Database optimization",
        "target": "Eliminate N+1 queries, add indexes",
        "investment": 60,  # hours
        "outcome": "3x faster API responses"
    },

    "total_investment": {
        "developer_hours": 260,
        "business_cost": 52000  # 40% of 3 engineers for 3 months
    }
}