Cto Advisor

#!/usr/bin/env python3
"""
Engineering Team Scaling Calculator - Optimize team growth and structure
"""

import json
import math
from typing import Dict, List, Tuple

class TeamScalingCalculator:
    def __init__(self):
        self.conway_factor = 1.5  # Conway's Law impact factor
        self.brooks_factor = 0.75  # Brooks' Law diminishing returns
        
        # Optimal team structures based on size
        self.team_structures = {
            'startup': {'min': 1, 'max': 10, 'structure': 'flat'},
            'growth': {'min': 11, 'max': 50, 'structure': 'team_leads'},
            'scale': {'min': 51, 'max': 150, 'structure': 'departments'},
            'enterprise': {'min': 151, 'max': 9999, 'structure': 'divisions'}
        }
        
        # Role ratios for balanced teams
        self.role_ratios = {
            'engineering_manager': 0.125,  # 1:8 ratio
            'tech_lead': 0.167,  # 1:6 ratio
            'senior_engineer': 0.3,
            'mid_engineer': 0.4,
            'junior_engineer': 0.2,
            'devops': 0.1,
            'qa': 0.15,
            'product_manager': 0.1,
            'designer': 0.08,
            'data_engineer': 0.05
        }
    
    def calculate_scaling_plan(self, current_state: Dict, growth_targets: Dict) -> Dict:
        """Calculate optimal scaling plan"""
        results = {
            'current_analysis': self._analyze_current_state(current_state),
            'growth_timeline': self._create_growth_timeline(current_state, growth_targets),
            'hiring_plan': {},
            'team_structure': {},
            'budget_projection': {},
            'risk_factors': [],
            'recommendations': []
        }
        
        # Generate hiring plan
        results['hiring_plan'] = self._generate_hiring_plan(
            current_state,
            growth_targets
        )
        
        # Design team structure
        results['team_structure'] = self._design_team_structure(
            growth_targets['target_headcount']
        )
        
        # Calculate budget
        results['budget_projection'] = self._calculate_budget(
            results['hiring_plan'],
            current_state.get('location', 'US')
        )
        
        # Assess risks
        results['risk_factors'] = self._assess_scaling_risks(
            current_state,
            growth_targets
        )
        
        # Generate recommendations
        results['recommendations'] = self._generate_recommendations(results)
        
        return results
    
    def _analyze_current_state(self, current_state: Dict) -> Dict:
        """Analyze current team state"""
        total_engineers = current_state.get('headcount', 0)
        
        analysis = {
            'total_headcount': total_engineers,
            'team_stage': self._get_team_stage(total_engineers),
            'productivity_index': 0,
            'balance_score': 0,
            'issues': []
        }
        
        # Calculate productivity index
        if total_engineers > 0:
            velocity = current_state.get('velocity', 100)
            expected_velocity = total_engineers * 20  # baseline 20 points per engineer
            analysis['productivity_index'] = (velocity / expected_velocity) * 100
        
        # Check team balance
        roles = current_state.get('roles', {})
        analysis['balance_score'] = self._calculate_balance_score(roles, total_engineers)
        
        # Identify issues
        if analysis['productivity_index'] < 70:
            analysis['issues'].append('Low productivity - possible process or tooling issues')
        
        if analysis['balance_score'] < 60:
            analysis['issues'].append('Team imbalance - review role distribution')
        
        manager_ratio = roles.get('managers', 0) / max(total_engineers, 1)
        if manager_ratio > 0.2:
            analysis['issues'].append('Over-managed - too many managers')
        elif manager_ratio < 0.08 and total_engineers > 20:
            analysis['issues'].append('Under-managed - need more engineering managers')
        
        return analysis
    
    def _get_team_stage(self, headcount: int) -> str:
        """Determine team stage based on size"""
        for stage, config in self.team_structures.items():
            if config['min'] <= headcount <= config['max']:
                return stage
        return 'startup'
    
    def _calculate_balance_score(self, roles: Dict, total: int) -> float:
        """Calculate team balance score"""
        if total == 0:
            return 0
        
        score = 100
        ideal_ratios = self.role_ratios
        
        for role, ideal_ratio in ideal_ratios.items():
            actual_count = roles.get(role, 0)
            actual_ratio = actual_count / total
            
            # Penalize deviation from ideal ratio
            deviation = abs(actual_ratio - ideal_ratio)
            penalty = deviation * 100
            score -= min(penalty, 20)  # Max 20 point penalty per role
        
        return max(0, score)
    
    def _create_growth_timeline(self, current: Dict, targets: Dict) -> List[Dict]:
        """Create quarterly growth timeline"""
        current_headcount = current.get('headcount', 0)
        target_headcount = targets.get('target_headcount', current_headcount)
        timeline_quarters = targets.get('timeline_quarters', 4)
        
        growth_needed = target_headcount - current_headcount
        timeline = []
        
        for quarter in range(1, timeline_quarters + 1):
            # Apply Brooks' Law - diminishing returns with rapid growth
            if quarter == 1:
                quarterly_growth = math.ceil(growth_needed * 0.4)  # Front-load hiring
            else:
                remaining_growth = target_headcount - current_headcount
                quarters_left = timeline_quarters - quarter + 1
                quarterly_growth = math.ceil(remaining_growth / quarters_left)
            
            # Adjust for onboarding capacity
            max_onboarding = math.ceil(current_headcount * 0.25)  # 25% growth per quarter max
            quarterly_growth = min(quarterly_growth, max_onboarding)
            
            current_headcount += quarterly_growth
            
            timeline.append({
                'quarter': f'Q{quarter}',
                'headcount': current_headcount,
                'new_hires': quarterly_growth,
                'onboarding_capacity': max_onboarding,
                'productivity_factor': 1.0 - (0.2 * (quarterly_growth / max(current_headcount, 1)))
            })
        
        return timeline
    
    def _generate_hiring_plan(self, current: Dict, targets: Dict) -> Dict:
        """Generate detailed hiring plan"""
        current_roles = current.get('roles', {})
        target_headcount = targets.get('target_headcount', 0)
        
        hiring_plan = {
            'total_hires_needed': target_headcount - current.get('headcount', 0),
            'by_role': {},
            'by_quarter': {},
            'interview_capacity_needed': 0,
            'recruiting_resources': 0
        }
        
        # Calculate ideal role distribution
        for role, ideal_ratio in self.role_ratios.items():
            ideal_count = math.ceil(target_headcount * ideal_ratio)
            current_count = current_roles.get(role, 0)
            hires_needed = max(0, ideal_count - current_count)
            
            if hires_needed > 0:
                hiring_plan['by_role'][role] = {
                    'current': current_count,
                    'target': ideal_count,
                    'hires_needed': hires_needed,
                    'priority': self._get_role_priority(role, current_roles, target_headcount)
                }
        
        # Distribute hires across quarters
        timeline = self._create_growth_timeline(current, targets)
        for quarter_data in timeline:
            quarter = quarter_data['quarter']
            hires = quarter_data['new_hires']
            
            hiring_plan['by_quarter'][quarter] = {
                'total_hires': hires,
                'breakdown': self._distribute_quarterly_hires(hires, hiring_plan['by_role'])
            }
        
        # Calculate interview capacity (5 interviews per hire average)
        hiring_plan['interview_capacity_needed'] = hiring_plan['total_hires_needed'] * 5
        
        # Calculate recruiting resources (1 recruiter per 50 hires/year)
        annual_hires = hiring_plan['total_hires_needed'] * (4 / max(targets.get('timeline_quarters', 4), 1))
        hiring_plan['recruiting_resources'] = math.ceil(annual_hires / 50)
        
        return hiring_plan
    
    def _get_role_priority(self, role: str, current_roles: Dict, target_size: int) -> int:
        """Determine hiring priority for a role"""
        # Priority based on criticality and current gaps
        priorities = {
            'engineering_manager': 10 if target_size > 20 else 5,
            'tech_lead': 9,
            'senior_engineer': 8,
            'devops': 7 if current_roles.get('devops', 0) == 0 else 5,
            'qa': 6,
            'mid_engineer': 5,
            'product_manager': 6,
            'designer': 5,
            'data_engineer': 4,
            'junior_engineer': 3
        }
        
        return priorities.get(role, 5)
    
    def _distribute_quarterly_hires(self, total_hires: int, role_needs: Dict) -> Dict:
        """Distribute quarterly hires across roles"""
        distribution = {}
        
        # Sort roles by priority
        sorted_roles = sorted(
            role_needs.items(),
            key=lambda x: x[1]['priority'],
            reverse=True
        )
        
        remaining_hires = total_hires
        
        for role, needs in sorted_roles:
            if remaining_hires <= 0:
                break
            
            hires = min(needs['hires_needed'], max(1, remaining_hires // 3))
            distribution[role] = hires
            remaining_hires -= hires
        
        return distribution
    
    def _design_team_structure(self, target_headcount: int) -> Dict:
        """Design optimal team structure"""
        stage = self._get_team_stage(target_headcount)
        structure = {
            'organizational_model': self.team_structures[stage]['structure'],
            'teams': [],
            'reporting_structure': {},
            'communication_paths': 0
        }
        
        if stage == 'startup':
            structure['teams'] = [{
                'name': 'Core Team',
                'size': target_headcount,
                'focus': 'Full-stack'
            }]
            
        elif stage == 'growth':
            # Create 2-4 teams
            team_size = 6
            num_teams = math.ceil(target_headcount / team_size)
            
            structure['teams'] = [
                {
                    'name': f'Team {i+1}',
                    'size': team_size,
                    'focus': ['Platform', 'Product', 'Infrastructure', 'Growth'][i % 4]
                }
                for i in range(num_teams)
            ]
            
        elif stage == 'scale':
            # Create departments with multiple teams
            structure['departments'] = [
                {'name': 'Platform', 'teams': 3, 'headcount': target_headcount * 0.3},
                {'name': 'Product', 'teams': 4, 'headcount': target_headcount * 0.4},
                {'name': 'Infrastructure', 'teams': 2, 'headcount': target_headcount * 0.2},
                {'name': 'Data', 'teams': 1, 'headcount': target_headcount * 0.1}
            ]
        
        # Calculate communication paths (n*(n-1)/2)
        structure['communication_paths'] = (target_headcount * (target_headcount - 1)) // 2
        
        # Add management layers
        structure['management_layers'] = math.ceil(math.log(target_headcount, 7))
        
        return structure
    
    def _calculate_budget(self, hiring_plan: Dict, location: str) -> Dict:
        """Calculate budget projection"""
        # Average salaries by role and location (in USD)
        salary_bands = {
            'US': {
                'engineering_manager': 200000,
                'tech_lead': 180000,
                'senior_engineer': 160000,
                'mid_engineer': 120000,
                'junior_engineer': 85000,
                'devops': 150000,
                'qa': 100000,
                'product_manager': 150000,
                'designer': 120000,
                'data_engineer': 140000
            },
            'EU': {k: v * 0.8 for k, v in salary_bands.get('US', {}).items()},
            'APAC': {k: v * 0.6 for k, v in salary_bands.get('US', {}).items()}
        }
        
        location_salaries = salary_bands.get(location, salary_bands['US'])
        
        budget = {
            'annual_salary_cost': 0,
            'benefits_cost': 0,  # 30% of salary
            'equipment_cost': 0,  # $5k per hire
            'recruiting_cost': 0,  # 20% of first-year salary
            'onboarding_cost': 0,  # $10k per hire
            'total_cost': 0,
            'cost_per_hire': 0
        }
        
        for role, details in hiring_plan['by_role'].items():
            hires = details['hires_needed']
            salary = location_salaries.get(role, 100000)
            
            budget['annual_salary_cost'] += hires * salary
            budget['recruiting_cost'] += hires * salary * 0.2
        
        budget['benefits_cost'] = budget['annual_salary_cost'] * 0.3
        budget['equipment_cost'] = hiring_plan['total_hires_needed'] * 5000
        budget['onboarding_cost'] = hiring_plan['total_hires_needed'] * 10000
        
        budget['total_cost'] = sum([
            budget['annual_salary_cost'],
            budget['benefits_cost'],
            budget['equipment_cost'],
            budget['recruiting_cost'],
            budget['onboarding_cost']
        ])
        
        if hiring_plan['total_hires_needed'] > 0:
            budget['cost_per_hire'] = budget['total_cost'] / hiring_plan['total_hires_needed']
        
        return budget
    
    def _assess_scaling_risks(self, current: Dict, targets: Dict) -> List[Dict]:
        """Assess risks in scaling plan"""
        risks = []
        
        growth_rate = (targets['target_headcount'] - current['headcount']) / max(current['headcount'], 1)
        
        if growth_rate > 1.0:  # More than 100% growth
            risks.append({
                'risk': 'Rapid growth dilution',
                'impact': 'High',
                'mitigation': 'Implement strong onboarding and mentorship programs'
            })
        
        if current.get('attrition_rate', 0) > 15:
            risks.append({
                'risk': 'High attrition during scaling',
                'impact': 'High',
                'mitigation': 'Address retention issues before aggressive hiring'
            })
        
        if targets.get('timeline_quarters', 4) < 4:
            risks.append({
                'risk': 'Compressed timeline',
                'impact': 'Medium',
                'mitigation': 'Consider extending timeline or increasing recruiting resources'
            })
        
        return risks
    
    def _generate_recommendations(self, results: Dict) -> List[str]:
        """Generate scaling recommendations"""
        recommendations = []
        
        # Based on growth rate
        total_hires = results['hiring_plan']['total_hires_needed']
        current_size = results['current_analysis']['total_headcount']
        
        if current_size > 0:
            growth_rate = total_hires / current_size
            
            if growth_rate > 0.5:
                recommendations.append('Consider hiring a dedicated recruiting team')
                recommendations.append('Implement scalable onboarding processes')
                recommendations.append('Establish clear team charters and boundaries')
            
            if growth_rate > 1.0:
                recommendations.append('⚠️ High growth risk - consider slowing timeline')
                recommendations.append('Focus on senior hires first to establish culture')
                recommendations.append('Implement continuous integration practices early')
        
        # Based on structure
        if results['team_structure']['communication_paths'] > 1000:
            recommendations.append('Implement clear communication channels and tools')
            recommendations.append('Consider platform teams to reduce dependencies')
        
        # Based on balance
        if results['current_analysis']['balance_score'] < 70:
            recommendations.append('Prioritize hiring for underrepresented roles')
            recommendations.append('Consider role rotation for skill development')
        
        return recommendations

def calculate_team_scaling(current_state: Dict, growth_targets: Dict) -> str:
    """Main function to calculate team scaling"""
    calculator = TeamScalingCalculator()
    results = calculator.calculate_scaling_plan(current_state, growth_targets)
    
    # Format output
    output = [
        "=== Engineering Team Scaling Plan ===",
        f"",
        f"Current State Analysis:",
        f"  Current Headcount: {results['current_analysis']['total_headcount']}",
        f"  Team Stage: {results['current_analysis']['team_stage']}",
        f"  Productivity Index: {results['current_analysis']['productivity_index']:.1f}%",
        f"  Team Balance Score: {results['current_analysis']['balance_score']:.1f}/100",
        f"",
        f"Growth Plan:",
        f"  Target Headcount: {growth_targets['target_headcount']}",
        f"  Total Hires Needed: {results['hiring_plan']['total_hires_needed']}",
        f"  Timeline: {growth_targets['timeline_quarters']} quarters",
        f"",
        "Quarterly Timeline:"
    ]
    
    for quarter in results['growth_timeline']:
        output.append(
            f"  {quarter['quarter']}: {quarter['headcount']} total "
            f"(+{quarter['new_hires']} hires, "
            f"{quarter['productivity_factor']:.0%} productivity)"
        )
    
    output.extend([
        f"",
        "Hiring Priorities:"
    ])
    
    sorted_roles = sorted(
        results['hiring_plan']['by_role'].items(),
        key=lambda x: x[1]['priority'],
        reverse=True
    )
    
    for role, details in sorted_roles[:5]:
        output.append(
            f"  {role}: {details['hires_needed']} hires "
            f"(Priority: {details['priority']}/10)"
        )
    
    output.extend([
        f"",
        f"Budget Projection:",
        f"  Annual Salary Cost: ${results['budget_projection']['annual_salary_cost']:,.0f}",
        f"  Total Investment: ${results['budget_projection']['total_cost']:,.0f}",
        f"  Cost per Hire: ${results['budget_projection']['cost_per_hire']:,.0f}",
        f"",
        f"Team Structure:",
        f"  Model: {results['team_structure']['organizational_model']}",
        f"  Management Layers: {results['team_structure']['management_layers']}",
        f"  Communication Paths: {results['team_structure']['communication_paths']:,}",
        f"",
        "Key Recommendations:"
    ])
    
    for rec in results['recommendations']:
        output.append(f"  • {rec}")
    
    return '\n'.join(output)

if __name__ == "__main__":
    # Example usage
    example_current = {
        'headcount': 25,
        'velocity': 450,
        'roles': {
            'engineering_manager': 2,
            'tech_lead': 3,
            'senior_engineer': 8,
            'mid_engineer': 10,
            'junior_engineer': 2
        },
        'attrition_rate': 12,
        'location': 'US'
    }
    
    example_targets = {
        'target_headcount': 75,
        'timeline_quarters': 4
    }
    
    print(calculate_team_scaling(example_current, example_targets))

#!/usr/bin/env python3
"""
Technical Debt Analyzer - Assess and prioritize technical debt across systems
"""

import json
from typing import Dict, List, Tuple
from datetime import datetime
import math

class TechDebtAnalyzer:
    def __init__(self):
        self.debt_categories = {
            'architecture': {
                'weight': 0.25,
                'indicators': [
                    'monolithic_design', 'tight_coupling', 'no_microservices',
                    'legacy_patterns', 'no_api_gateway', 'synchronous_only'
                ]
            },
            'code_quality': {
                'weight': 0.20,
                'indicators': [
                    'low_test_coverage', 'high_complexity', 'code_duplication',
                    'no_documentation', 'inconsistent_standards', 'legacy_language'
                ]
            },
            'infrastructure': {
                'weight': 0.20,
                'indicators': [
                    'manual_deployments', 'no_ci_cd', 'single_points_failure',
                    'no_monitoring', 'no_auto_scaling', 'outdated_servers'
                ]
            },
            'security': {
                'weight': 0.20,
                'indicators': [
                    'outdated_dependencies', 'no_security_scans', 'plain_text_secrets',
                    'no_encryption', 'missing_auth', 'no_audit_logs'
                ]
            },
            'performance': {
                'weight': 0.15,
                'indicators': [
                    'slow_response_times', 'no_caching', 'inefficient_queries',
                    'memory_leaks', 'no_optimization', 'blocking_operations'
                ]
            }
        }
        
        self.impact_matrix = {
            'user_impact': {'weight': 0.30, 'score': 0},
            'developer_velocity': {'weight': 0.25, 'score': 0},
            'system_reliability': {'weight': 0.20, 'score': 0},
            'scalability': {'weight': 0.15, 'score': 0},
            'maintenance_cost': {'weight': 0.10, 'score': 0}
        }
    
    def analyze_system(self, system_data: Dict) -> Dict:
        """Analyze a system for technical debt"""
        results = {
            'timestamp': datetime.now().isoformat(),
            'system_name': system_data.get('name', 'Unknown'),
            'debt_score': 0,
            'debt_level': '',
            'category_scores': {},
            'prioritized_actions': [],
            'estimated_effort': {},
            'risk_assessment': {},
            'recommendations': []
        }
        
        # Calculate debt scores by category
        total_debt_score = 0
        for category, config in self.debt_categories.items():
            category_score = self._calculate_category_score(
                system_data.get(category, {}), 
                config['indicators']
            )
            weighted_score = category_score * config['weight']
            results['category_scores'][category] = {
                'raw_score': category_score,
                'weighted_score': weighted_score,
                'level': self._get_level(category_score)
            }
            total_debt_score += weighted_score
        
        results['debt_score'] = round(total_debt_score, 2)
        results['debt_level'] = self._get_level(total_debt_score)
        
        # Calculate impact and prioritize
        results['prioritized_actions'] = self._prioritize_actions(
            results['category_scores'],
            system_data.get('business_context', {})
        )
        
        # Estimate effort
        results['estimated_effort'] = self._estimate_effort(
            results['prioritized_actions'],
            system_data.get('team_size', 5)
        )
        
        # Risk assessment
        results['risk_assessment'] = self._assess_risks(
            results['debt_score'],
            system_data.get('system_criticality', 'medium')
        )
        
        # Generate recommendations
        results['recommendations'] = self._generate_recommendations(results)
        
        return results
    
    def _calculate_category_score(self, category_data: Dict, indicators: List) -> float:
        """Calculate score for a specific category"""
        if not category_data:
            return 50.0  # Default middle score if no data
        
        total_score = 0
        count = 0
        
        for indicator in indicators:
            if indicator in category_data:
                # Score from 0 (no debt) to 100 (high debt)
                total_score += category_data[indicator]
                count += 1
        
        return (total_score / count) if count > 0 else 50.0
    
    def _get_level(self, score: float) -> str:
        """Convert numerical score to level"""
        if score < 20:
            return 'Low'
        elif score < 40:
            return 'Medium-Low'
        elif score < 60:
            return 'Medium'
        elif score < 80:
            return 'Medium-High'
        else:
            return 'Critical'
    
    def _prioritize_actions(self, category_scores: Dict, business_context: Dict) -> List:
        """Prioritize technical debt reduction actions"""
        actions = []
        
        for category, scores in category_scores.items():
            if scores['raw_score'] > 60:  # Focus on high debt areas
                priority = self._calculate_priority(
                    scores['raw_score'],
                    category,
                    business_context
                )
                
                action = {
                    'category': category,
                    'priority': priority,
                    'score': scores['raw_score'],
                    'action_items': self._get_action_items(category, scores['level'])
                }
                actions.append(action)
        
        # Sort by priority
        actions.sort(key=lambda x: x['priority'], reverse=True)
        return actions[:5]  # Top 5 priorities
    
    def _calculate_priority(self, score: float, category: str, context: Dict) -> float:
        """Calculate priority based on score and business context"""
        base_priority = score
        
        # Adjust based on business context
        if context.get('growth_phase') == 'rapid' and category in ['scalability', 'performance']:
            base_priority *= 1.5
        
        if context.get('compliance_required') and category == 'security':
            base_priority *= 2.0
        
        if context.get('cost_pressure') and category == 'infrastructure':
            base_priority *= 1.3
        
        return min(100, base_priority)
    
    def _get_action_items(self, category: str, level: str) -> List[str]:
        """Get specific action items based on category and level"""
        actions = {
            'architecture': {
                'Critical': [
                    'Immediate: Create architecture migration roadmap',
                    'Week 1: Identify service boundaries for decomposition',
                    'Month 1: Begin extracting first microservice',
                    'Month 2: Implement API gateway',
                    'Quarter: Complete critical service separation'
                ],
                'Medium-High': [
                    'Month 1: Document current architecture',
                    'Month 2: Design target architecture',
                    'Quarter: Begin gradual migration',
                    'Monitor: Track coupling metrics'
                ]
            },
            'code_quality': {
                'Critical': [
                    'Immediate: Implement code quality gates',
                    'Week 1: Set up automated testing pipeline',
                    'Month 1: Achieve 40% test coverage',
                    'Month 2: Refactor critical modules',
                    'Quarter: Reach 70% test coverage'
                ],
                'Medium-High': [
                    'Month 1: Establish coding standards',
                    'Month 2: Implement code review process',
                    'Quarter: Gradual refactoring plan'
                ]
            },
            'infrastructure': {
                'Critical': [
                    'Immediate: Implement basic CI/CD',
                    'Week 1: Set up monitoring and alerts',
                    'Month 1: Automate critical deployments',
                    'Month 2: Implement disaster recovery',
                    'Quarter: Full infrastructure as code'
                ],
                'Medium-High': [
                    'Month 1: Document infrastructure',
                    'Month 2: Begin automation',
                    'Quarter: Modernize critical components'
                ]
            },
            'security': {
                'Critical': [
                    'Immediate: Security audit and patching',
                    'Week 1: Implement secrets management',
                    'Month 1: Set up vulnerability scanning',
                    'Month 2: Implement security training',
                    'Quarter: Achieve compliance standards'
                ],
                'Medium-High': [
                    'Month 1: Security assessment',
                    'Month 2: Implement security tools',
                    'Quarter: Regular security reviews'
                ]
            },
            'performance': {
                'Critical': [
                    'Immediate: Performance profiling',
                    'Week 1: Implement caching strategy',
                    'Month 1: Optimize database queries',
                    'Month 2: Implement CDN',
                    'Quarter: Re-architect bottlenecks'
                ],
                'Medium-High': [
                    'Month 1: Performance baseline',
                    'Month 2: Optimization plan',
                    'Quarter: Incremental improvements'
                ]
            }
        }
        
        return actions.get(category, {}).get(level, ['Create action plan'])
    
    def _estimate_effort(self, actions: List, team_size: int) -> Dict:
        """Estimate effort required for debt reduction"""
        total_story_points = 0
        effort_breakdown = {}
        
        for action in actions:
            # Estimate based on category and score
            base_points = action['score'] * 2  # Higher debt = more effort
            
            if action['category'] == 'architecture':
                points = base_points * 1.5  # Architecture changes are complex
            elif action['category'] == 'security':
                points = base_points * 1.2  # Security requires careful work
            else:
                points = base_points
            
            effort_breakdown[action['category']] = {
                'story_points': round(points),
                'sprints': math.ceil(points / (team_size * 20)),  # 20 points per dev per sprint
                'developers_needed': math.ceil(points / 100)
            }
            total_story_points += points
        
        return {
            'total_story_points': round(total_story_points),
            'estimated_sprints': math.ceil(total_story_points / (team_size * 20)),
            'recommended_team_size': max(team_size, math.ceil(total_story_points / 200)),
            'breakdown': effort_breakdown
        }
    
    def _assess_risks(self, debt_score: float, criticality: str) -> Dict:
        """Assess risks associated with technical debt"""
        risk_level = 'Low'
        
        if debt_score > 70 and criticality == 'high':
            risk_level = 'Critical'
        elif debt_score > 60 or criticality == 'high':
            risk_level = 'High'
        elif debt_score > 40:
            risk_level = 'Medium'
        
        risks = {
            'overall_risk': risk_level,
            'specific_risks': []
        }
        
        if debt_score > 60:
            risks['specific_risks'].extend([
                'System failure risk increasing',
                'Developer productivity declining',
                'Innovation velocity blocked',
                'Maintenance costs escalating'
            ])
        
        if debt_score > 80:
            risks['specific_risks'].extend([
                'Competitive disadvantage emerging',
                'Talent retention risk',
                'Customer satisfaction impact',
                'Potential data breach vulnerability'
            ])
        
        return risks
    
    def _generate_recommendations(self, results: Dict) -> List[str]:
        """Generate strategic recommendations"""
        recommendations = []
        
        # Overall strategy based on debt level
        if results['debt_level'] == 'Critical':
            recommendations.append('🚨 URGENT: Dedicate 40% of engineering capacity to debt reduction')
            recommendations.append('Create dedicated debt reduction team')
            recommendations.append('Implement weekly debt reduction reviews')
            recommendations.append('Consider temporary feature freeze')
        elif results['debt_level'] in ['Medium-High', 'High']:
            recommendations.append('Allocate 25-30% of sprints to debt reduction')
            recommendations.append('Establish technical debt budget')
            recommendations.append('Implement debt prevention practices')
        else:
            recommendations.append('Maintain 15-20% ongoing debt reduction allocation')
            recommendations.append('Focus on prevention over correction')
        
        # Category-specific recommendations
        for category, scores in results['category_scores'].items():
            if scores['raw_score'] > 70:
                if category == 'architecture':
                    recommendations.append(f'Consider hiring architecture specialist')
                elif category == 'security':
                    recommendations.append(f'Engage security audit firm')
                elif category == 'performance':
                    recommendations.append(f'Implement performance SLA monitoring')
        
        # Team recommendations
        effort = results.get('estimated_effort', {})
        if effort.get('recommended_team_size', 0) > effort.get('total_story_points', 0) / 200:
            recommendations.append(f"Scale team to {effort['recommended_team_size']} engineers")
        
        return recommendations

def analyze_technical_debt(system_config: Dict) -> str:
    """Main function to analyze technical debt"""
    analyzer = TechDebtAnalyzer()
    results = analyzer.analyze_system(system_config)
    
    # Format output
    output = [
        f"=== Technical Debt Analysis Report ===",
        f"System: {results['system_name']}",
        f"Analysis Date: {results['timestamp'][:10]}",
        f"",
        f"OVERALL DEBT SCORE: {results['debt_score']}/100 ({results['debt_level']})",
        f"",
        "Category Breakdown:"
    ]
    
    for category, scores in results['category_scores'].items():
        output.append(f"  {category.title()}: {scores['raw_score']:.1f} ({scores['level']})")
    
    output.extend([
        f"",
        "Risk Assessment:",
        f"  Overall Risk: {results['risk_assessment']['overall_risk']}"
    ])
    
    for risk in results['risk_assessment']['specific_risks']:
        output.append(f"  • {risk}")
    
    output.extend([
        f"",
        "Effort Estimation:",
        f"  Total Story Points: {results['estimated_effort']['total_story_points']}",
        f"  Estimated Sprints: {results['estimated_effort']['estimated_sprints']}",
        f"  Recommended Team Size: {results['estimated_effort']['recommended_team_size']}",
        f"",
        "Top Priority Actions:"
    ])
    
    for i, action in enumerate(results['prioritized_actions'][:3], 1):
        output.append(f"\n{i}. {action['category'].title()} (Priority: {action['priority']:.0f})")
        for item in action['action_items'][:3]:
            output.append(f"   - {item}")
    
    output.extend([
        f"",
        "Strategic Recommendations:"
    ])
    
    for rec in results['recommendations']:
        output.append(f"  • {rec}")
    
    return '\n'.join(output)

if __name__ == "__main__":
    # Example usage
    example_system = {
        'name': 'Legacy E-commerce Platform',
        'architecture': {
            'monolithic_design': 80,
            'tight_coupling': 70,
            'no_microservices': 90,
            'legacy_patterns': 60
        },
        'code_quality': {
            'low_test_coverage': 75,
            'high_complexity': 65,
            'code_duplication': 55
        },
        'infrastructure': {
            'manual_deployments': 70,
            'no_ci_cd': 60,
            'no_monitoring': 40
        },
        'security': {
            'outdated_dependencies': 85,
            'no_security_scans': 70
        },
        'performance': {
            'slow_response_times': 60,
            'no_caching': 50
        },
        'team_size': 8,
        'system_criticality': 'high',
        'business_context': {
            'growth_phase': 'rapid',
            'compliance_required': True,
            'cost_pressure': False
        }
    }
    
    print(analyze_technical_debt(example_system))