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# Predictive Development Intelligence
## Advanced Predictive Analytics and Intelligence for Enhanced BMAD System
The Predictive Development Intelligence module provides sophisticated predictive analytics capabilities that help teams make better decisions, plan more effectively, and achieve better outcomes through data-driven insights, trend analysis, and intelligent forecasting.
### Predictive Intelligence Architecture
#### Comprehensive Prediction Framework
```yaml
predictive_intelligence_architecture:
prediction_domains:
project_outcome_prediction:
- success_probability_prediction: "Predict likelihood of project success"
- failure_risk_assessment: "Assess risk of project failure and reasons"
- quality_outcome_prediction: "Predict final quality metrics"
- timeline_adherence_prediction: "Predict timeline compliance"
- budget_adherence_prediction: "Predict budget compliance"
timeline_effort_prediction:
- development_time_estimation: "Predict development time for features"
- task_duration_forecasting: "Forecast individual task durations"
- milestone_achievement_prediction: "Predict milestone achievement dates"
- resource_allocation_optimization: "Predict optimal resource allocation"
- bottleneck_prediction: "Predict potential project bottlenecks"
quality_prediction:
- defect_density_prediction: "Predict defect rates and locations"
- technical_debt_accumulation: "Predict technical debt growth"
- maintainability_degradation: "Predict maintainability trends"
- performance_impact_prediction: "Predict performance implications"
- security_vulnerability_prediction: "Predict security vulnerability risks"
team_performance_prediction:
- productivity_forecasting: "Forecast team productivity trends"
- skill_gap_prediction: "Predict future skill requirements and gaps"
- collaboration_effectiveness: "Predict team collaboration outcomes"
- burnout_risk_assessment: "Assess team burnout risks"
- learning_curve_prediction: "Predict learning and adaptation timelines"
technology_trend_prediction:
- technology_adoption_forecasting: "Forecast technology adoption trends"
- compatibility_impact_prediction: "Predict technology compatibility issues"
- obsolescence_risk_assessment: "Assess technology obsolescence risks"
- performance_impact_prediction: "Predict technology performance impacts"
- learning_investment_prediction: "Predict learning investment requirements"
market_competitive_intelligence:
- market_trend_analysis: "Analyze market trends and implications"
- competitive_landscape_prediction: "Predict competitive landscape changes"
- user_demand_forecasting: "Forecast user demand and preferences"
- feature_priority_prediction: "Predict feature importance and priority"
- market_timing_optimization: "Predict optimal market timing"
prediction_techniques:
statistical_modeling:
- time_series_analysis: "Analyze trends over time"
- regression_analysis: "Model relationships between variables"
- correlation_analysis: "Identify correlations and dependencies"
- clustering_analysis: "Group similar patterns and outcomes"
- survival_analysis: "Predict time-to-event outcomes"
machine_learning_models:
- supervised_learning: "Learn from labeled historical data"
- unsupervised_learning: "Discover hidden patterns in data"
- reinforcement_learning: "Learn optimal decisions through experience"
- ensemble_methods: "Combine multiple models for better predictions"
- deep_learning: "Complex pattern recognition and prediction"
simulation_modeling:
- monte_carlo_simulation: "Model uncertainty and variability"
- agent_based_modeling: "Model complex system interactions"
- discrete_event_simulation: "Model process flows and bottlenecks"
- scenario_planning: "Model different future scenarios"
- sensitivity_analysis: "Assess impact of variable changes"
expert_systems:
- rule_based_prediction: "Apply expert knowledge rules"
- fuzzy_logic_systems: "Handle uncertainty and imprecision"
- knowledge_graphs: "Leverage connected knowledge for predictions"
- ontology_based_reasoning: "Use structured knowledge for inference"
- case_based_reasoning: "Learn from similar historical cases"
intelligence_capabilities:
early_warning_systems:
- risk_early_detection: "Detect risks before they materialize"
- trend_deviation_alerts: "Alert on deviations from expected trends"
- threshold_monitoring: "Monitor against defined thresholds"
- anomaly_detection: "Detect unusual patterns and outliers"
- predictive_alerting: "Alert on predicted future issues"
decision_support:
- alternative_scenario_analysis: "Analyze different decision alternatives"
- impact_assessment: "Assess impact of different decisions"
- optimization_recommendations: "Recommend optimal decisions"
- trade_off_analysis: "Analyze trade-offs between options"
- strategic_planning_support: "Support long-term strategic planning"
adaptive_intelligence:
- model_self_improvement: "Improve prediction models over time"
- context_adaptation: "Adapt predictions to changing contexts"
- feedback_integration: "Learn from prediction accuracy feedback"
- domain_specialization: "Specialize in specific domains over time"
- cross_domain_learning: "Transfer learning across domains"
```
#### Predictive Intelligence Implementation
```python
import numpy as np
import pandas as pd
from typing import Dict, List, Any, Optional, Tuple, Union
from dataclasses import dataclass, field
from enum import Enum
from datetime import datetime, timedelta
import asyncio
import json
import pickle
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
from sklearn.linear_model import LinearRegression, LogisticRegression
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.metrics import mean_squared_error, accuracy_score, classification_report
from sklearn.preprocessing import StandardScaler, LabelEncoder
import xgboost as xgb
import lightgbm as lgb
from scipy import stats
from statsmodels.tsa.arima.model import ARIMA
from statsmodels.tsa.holtwinters import ExponentialSmoothing
import warnings
warnings.filterwarnings('ignore')
class PredictionType(Enum):
SUCCESS_PROBABILITY = "success_probability"
TIMELINE_ESTIMATION = "timeline_estimation"
QUALITY_PREDICTION = "quality_prediction"
RISK_ASSESSMENT = "risk_assessment"
EFFORT_ESTIMATION = "effort_estimation"
PERFORMANCE_PREDICTION = "performance_prediction"
class PredictionConfidence(Enum):
VERY_HIGH = "very_high"
HIGH = "high"
MEDIUM = "medium"
LOW = "low"
VERY_LOW = "very_low"
class TimeHorizon(Enum):
SHORT_TERM = "short_term" # 1-4 weeks
MEDIUM_TERM = "medium_term" # 1-6 months
LONG_TERM = "long_term" # 6+ months
@dataclass
class PredictionInput:
"""
Input data for making predictions
"""
prediction_type: PredictionType
context: Dict[str, Any]
historical_data: Dict[str, Any] = field(default_factory=dict)
current_metrics: Dict[str, Any] = field(default_factory=dict)
project_characteristics: Dict[str, Any] = field(default_factory=dict)
team_characteristics: Dict[str, Any] = field(default_factory=dict)
time_horizon: TimeHorizon = TimeHorizon.MEDIUM_TERM
@dataclass
class PredictionResult:
"""
Result of a prediction analysis
"""
prediction_id: str
prediction_type: PredictionType
predicted_value: Union[float, str, Dict[str, Any]]
confidence_level: PredictionConfidence
confidence_score: float
time_horizon: TimeHorizon
contributing_factors: List[Dict[str, Any]] = field(default_factory=list)
scenarios: List[Dict[str, Any]] = field(default_factory=list)
recommendations: List[str] = field(default_factory=list)
uncertainty_range: Optional[Tuple[float, float]] = None
prediction_timestamp: datetime = field(default_factory=datetime.utcnow)
@dataclass
class TrendAnalysis:
"""
Results of trend analysis
"""
trend_id: str
metric_name: str
trend_direction: str # increasing, decreasing, stable, volatile
trend_strength: float
seasonal_patterns: Dict[str, Any] = field(default_factory=dict)
forecast_values: List[float] = field(default_factory=list)
forecast_dates: List[datetime] = field(default_factory=list)
change_points: List[datetime] = field(default_factory=list)
class PredictiveDevelopmentIntelligence:
"""
Advanced predictive intelligence system for development projects
"""
def __init__(self, config=None):
self.config = config or {
'prediction_confidence_threshold': 0.7,
'model_retrain_frequency_days': 30,
'ensemble_voting': True,
'cross_validation_folds': 5,
'feature_importance_threshold': 0.05,
'prediction_horizon_days': 90,
'uncertainty_quantification': True
}
# Prediction models
self.models = {}
self.model_performance = {}
self.feature_importance = {}
# Specialized predictors
self.timeline_predictor = TimelinePredictor(self.config)
self.quality_predictor = QualityPredictor(self.config)
self.risk_predictor = RiskPredictor(self.config)
self.team_performance_predictor = TeamPerformancePredictor(self.config)
self.technology_trend_predictor = TechnologyTrendPredictor(self.config)
# Analysis engines
self.trend_analyzer = TrendAnalyzer(self.config)
self.scenario_modeler = ScenarioModeler(self.config)
self.decision_optimizer = DecisionOptimizer(self.config)
# Data management
self.historical_data = {}
self.prediction_history = []
self.model_training_data = defaultdict(list)
# Adaptive learning
self.prediction_feedback = []
self.model_adaptation_engine = ModelAdaptationEngine(self.config)
async def make_prediction(self, prediction_input: PredictionInput):
"""
Make a comprehensive prediction based on input data
"""
prediction_session = {
'session_id': generate_uuid(),
'start_time': datetime.utcnow(),
'prediction_input': prediction_input,
'prediction_result': None,
'model_used': None,
'feature_analysis': {},
'uncertainty_analysis': {}
}
try:
# Prepare features for prediction
features = await self.prepare_prediction_features(prediction_input)
prediction_session['features'] = features
# Select optimal model for prediction type
model = await self.select_optimal_model(prediction_input.prediction_type, features)
prediction_session['model_used'] = model['model_info']
# Make prediction
if prediction_input.prediction_type == PredictionType.SUCCESS_PROBABILITY:
prediction_result = await self.predict_project_success(features, model)
elif prediction_input.prediction_type == PredictionType.TIMELINE_ESTIMATION:
prediction_result = await self.timeline_predictor.predict_timeline(prediction_input)
elif prediction_input.prediction_type == PredictionType.QUALITY_PREDICTION:
prediction_result = await self.quality_predictor.predict_quality(prediction_input)
elif prediction_input.prediction_type == PredictionType.RISK_ASSESSMENT:
prediction_result = await self.risk_predictor.assess_risks(prediction_input)
elif prediction_input.prediction_type == PredictionType.EFFORT_ESTIMATION:
prediction_result = await self.predict_effort_estimation(features, model)
elif prediction_input.prediction_type == PredictionType.PERFORMANCE_PREDICTION:
prediction_result = await self.predict_performance_metrics(features, model)
else:
prediction_result = await self.make_generic_prediction(features, model, prediction_input)
# Perform uncertainty quantification
if self.config['uncertainty_quantification']:
uncertainty_analysis = await self.quantify_prediction_uncertainty(
prediction_result,
features,
model
)
prediction_session['uncertainty_analysis'] = uncertainty_analysis
prediction_result.uncertainty_range = uncertainty_analysis.get('confidence_interval')
# Analyze contributing factors
contributing_factors = await self.analyze_contributing_factors(
features,
model,
prediction_result
)
prediction_result.contributing_factors = contributing_factors
# Generate scenarios
scenarios = await self.scenario_modeler.generate_scenarios(
prediction_input,
prediction_result
)
prediction_result.scenarios = scenarios
# Generate recommendations
recommendations = await self.generate_prediction_recommendations(
prediction_input,
prediction_result
)
prediction_result.recommendations = recommendations
prediction_session['prediction_result'] = prediction_result
except Exception as e:
prediction_session['error'] = str(e)
finally:
prediction_session['end_time'] = datetime.utcnow()
prediction_session['prediction_duration'] = (
prediction_session['end_time'] - prediction_session['start_time']
).total_seconds()
# Store prediction history
self.prediction_history.append(prediction_session)
return prediction_session
async def prepare_prediction_features(self, prediction_input: PredictionInput):
"""
Prepare and engineer features for prediction
"""
features = {
'basic_features': {},
'engineered_features': {},
'contextual_features': {},
'temporal_features': {}
}
# Extract basic features from input
basic_features = {
'project_size': prediction_input.project_characteristics.get('size_metrics', {}).get('total_files', 0),
'team_size': prediction_input.team_characteristics.get('team_size', 5),
'complexity_score': prediction_input.current_metrics.get('complexity_score', 0),
'duration_weeks': prediction_input.project_characteristics.get('planned_duration_weeks', 12),
'technology_count': len(prediction_input.project_characteristics.get('technologies', [])),
'previous_projects': prediction_input.team_characteristics.get('previous_projects', 0)
}
features['basic_features'] = basic_features
# Engineer features based on historical data
engineered_features = await self.engineer_advanced_features(
prediction_input,
basic_features
)
features['engineered_features'] = engineered_features
# Extract contextual features
contextual_features = {
'domain_complexity': self.assess_domain_complexity(prediction_input.context.get('domain', 'web')),
'market_pressure': prediction_input.context.get('market_pressure', 'medium'),
'regulatory_requirements': len(prediction_input.context.get('regulations', [])),
'integration_complexity': len(prediction_input.project_characteristics.get('integrations', [])),
'innovation_level': prediction_input.context.get('innovation_level', 'medium')
}
features['contextual_features'] = contextual_features
# Extract temporal features
current_date = datetime.utcnow()
temporal_features = {
'quarter': current_date.quarter,
'month': current_date.month,
'day_of_year': current_date.timetuple().tm_yday,
'is_holiday_season': current_date.month in [11, 12, 1],
'project_phase': prediction_input.context.get('project_phase', 'development'),
'time_since_start_weeks': prediction_input.context.get('weeks_elapsed', 0)
}
features['temporal_features'] = temporal_features
return features
def assess_domain_complexity(self, domain):
"""
Assess complexity level of the application domain
"""
domain_complexity_map = {
'web': 0.3,
'mobile': 0.4,
'enterprise': 0.7,
'fintech': 0.8,
'healthcare': 0.9,
'aerospace': 1.0,
'ai_ml': 0.8,
'blockchain': 0.9,
'iot': 0.7,
'gaming': 0.6
}
return domain_complexity_map.get(domain.lower(), 0.5)
async def engineer_advanced_features(self, prediction_input, basic_features):
"""
Engineer advanced features from basic features and historical data
"""
engineered_features = {}
# Velocity-based features
if prediction_input.historical_data.get('velocity_history'):
velocity_data = prediction_input.historical_data['velocity_history']
engineered_features.update({
'avg_velocity': np.mean(velocity_data),
'velocity_trend': self.calculate_trend(velocity_data),
'velocity_variance': np.var(velocity_data),
'velocity_stability': 1.0 / (1.0 + np.var(velocity_data))
})
# Quality-based features
if prediction_input.historical_data.get('quality_metrics'):
quality_data = prediction_input.historical_data['quality_metrics']
engineered_features.update({
'quality_trend': self.calculate_trend(quality_data.get('overall_scores', [])),
'defect_rate_trend': self.calculate_trend(quality_data.get('defect_rates', [])),
'test_coverage_trend': self.calculate_trend(quality_data.get('coverage_scores', []))
})
# Complexity-based features
engineered_features.update({
'complexity_per_developer': basic_features['complexity_score'] / max(basic_features['team_size'], 1),
'size_complexity_ratio': basic_features['project_size'] / max(basic_features['complexity_score'], 1),
'team_project_ratio': basic_features['team_size'] / max(basic_features['duration_weeks'], 1)
})
# Experience-based features
team_experience = prediction_input.team_characteristics.get('average_experience_years', 3)
engineered_features.update({
'experience_complexity_ratio': team_experience / max(basic_features['complexity_score'], 1),
'experience_size_ratio': team_experience / max(basic_features['project_size'] / 100, 1),
'experience_adequacy': min(team_experience / 3.0, 1.0) # Normalized to 3 years
})
return engineered_features
def calculate_trend(self, data_series):
"""
Calculate trend direction and strength for a data series
"""
if len(data_series) < 2:
return 0.0
x = np.arange(len(data_series))
y = np.array(data_series)
# Linear regression to find trend
slope, _, r_value, _, _ = stats.linregress(x, y)
# Return trend strength (-1 to 1, negative = declining, positive = improving)
return slope * r_value
async def select_optimal_model(self, prediction_type, features):
"""
Select the optimal model for the given prediction type and features
"""
model_key = f"{prediction_type.value}_model"
if model_key not in self.models:
# Initialize model if not exists
await self.initialize_model(prediction_type)
model_info = {
'model_type': self.models[model_key]['type'],
'model_performance': self.model_performance.get(model_key, {}),
'feature_count': len(self.flatten_features(features)),
'last_trained': self.models[model_key].get('last_trained'),
'prediction_count': self.models[model_key].get('prediction_count', 0)
}
return {
'model': self.models[model_key]['model'],
'scaler': self.models[model_key].get('scaler'),
'model_info': model_info
}
def flatten_features(self, features):
"""
Flatten nested feature dictionary into a single vector
"""
flattened = {}
for category, feature_dict in features.items():
if isinstance(feature_dict, dict):
for key, value in feature_dict.items():
if isinstance(value, (int, float)):
flattened[f"{category}_{key}"] = value
elif isinstance(value, str):
# Simple encoding for categorical variables
flattened[f"{category}_{key}_encoded"] = hash(value) % 1000 / 1000.0
return flattened
async def initialize_model(self, prediction_type):
"""
Initialize a prediction model for the given type
"""
model_key = f"{prediction_type.value}_model"
if prediction_type in [PredictionType.SUCCESS_PROBABILITY, PredictionType.RISK_ASSESSMENT]:
# Classification models
model = GradientBoostingClassifier(n_estimators=100, random_state=42)
model_type = 'classification'
else:
# Regression models
model = GradientBoostingRegressor(n_estimators=100, random_state=42)
model_type = 'regression'
scaler = StandardScaler()
self.models[model_key] = {
'model': model,
'scaler': scaler,
'type': model_type,
'last_trained': None,
'prediction_count': 0
}
# Train with available data if any
if self.model_training_data[model_key]:
await self.retrain_model(prediction_type)
async def predict_project_success(self, features, model):
"""
Predict project success probability
"""
# Flatten features for model input
feature_vector = self.flatten_features(features)
X = np.array(list(feature_vector.values())).reshape(1, -1)
# Scale features if scaler is available
if model['scaler'] is not None:
X = model['scaler'].transform(X)
# Make prediction
if hasattr(model['model'], 'predict_proba'):
success_probability = model['model'].predict_proba(X)[0][1] # Probability of success class
else:
# Fallback for models without predict_proba
success_probability = max(0, min(1, model['model'].predict(X)[0]))
# Determine confidence level
confidence_score = abs(success_probability - 0.5) * 2 # Distance from uncertainty
confidence_level = self.determine_confidence_level(confidence_score)
prediction_result = PredictionResult(
prediction_id=generate_uuid(),
prediction_type=PredictionType.SUCCESS_PROBABILITY,
predicted_value=success_probability,
confidence_level=confidence_level,
confidence_score=confidence_score,
time_horizon=TimeHorizon.MEDIUM_TERM
)
return prediction_result
async def predict_effort_estimation(self, features, model):
"""
Predict development effort in person-hours
"""
feature_vector = self.flatten_features(features)
X = np.array(list(feature_vector.values())).reshape(1, -1)
if model['scaler'] is not None:
X = model['scaler'].transform(X)
estimated_hours = max(0, model['model'].predict(X)[0])
# Calculate confidence based on model performance
confidence_score = self.model_performance.get(f"effort_estimation_model", {}).get('accuracy', 0.5)
confidence_level = self.determine_confidence_level(confidence_score)
prediction_result = PredictionResult(
prediction_id=generate_uuid(),
prediction_type=PredictionType.EFFORT_ESTIMATION,
predicted_value=estimated_hours,
confidence_level=confidence_level,
confidence_score=confidence_score,
time_horizon=TimeHorizon.SHORT_TERM
)
return prediction_result
def determine_confidence_level(self, confidence_score):
"""
Determine confidence level based on numerical score
"""
if confidence_score >= 0.9:
return PredictionConfidence.VERY_HIGH
elif confidence_score >= 0.8:
return PredictionConfidence.HIGH
elif confidence_score >= 0.6:
return PredictionConfidence.MEDIUM
elif confidence_score >= 0.4:
return PredictionConfidence.LOW
else:
return PredictionConfidence.VERY_LOW
async def analyze_contributing_factors(self, features, model, prediction_result):
"""
Analyze factors contributing to the prediction
"""
contributing_factors = []
# Get feature importance if available
feature_vector = self.flatten_features(features)
feature_names = list(feature_vector.keys())
if hasattr(model['model'], 'feature_importances_'):
importances = model['model'].feature_importances_
# Sort features by importance
feature_importance_pairs = list(zip(feature_names, importances))
feature_importance_pairs.sort(key=lambda x: x[1], reverse=True)
# Top contributing factors
for feature_name, importance in feature_importance_pairs[:5]:
if importance > self.config['feature_importance_threshold']:
contributing_factors.append({
'factor': feature_name,
'importance': importance,
'value': feature_vector[feature_name],
'impact': 'positive' if importance > 0 else 'negative',
'description': self.generate_factor_description(feature_name, feature_vector[feature_name])
})
return contributing_factors
def generate_factor_description(self, feature_name, feature_value):
"""
Generate human-readable description for a contributing factor
"""
descriptions = {
'basic_features_team_size': f"Team size of {feature_value:.0f} members",
'basic_features_complexity_score': f"Complexity score of {feature_value:.2f}",
'basic_features_project_size': f"Project size of {feature_value:.0f} files",
'engineered_features_experience_adequacy': f"Team experience adequacy: {feature_value:.2f}",
'contextual_features_domain_complexity': f"Domain complexity level: {feature_value:.2f}",
'temporal_features_project_phase': f"Current project phase impact"
}
return descriptions.get(feature_name, f"{feature_name}: {feature_value}")
async def generate_prediction_recommendations(self, prediction_input, prediction_result):
"""
Generate actionable recommendations based on prediction
"""
recommendations = []
if prediction_result.prediction_type == PredictionType.SUCCESS_PROBABILITY:
success_prob = prediction_result.predicted_value
if success_prob < 0.6: # Low success probability
recommendations.extend([
"Consider reducing project scope to improve success probability",
"Increase team size or add experienced developers",
"Implement more frequent milestone reviews and risk assessments",
"Consider breaking the project into smaller, more manageable phases"
])
elif success_prob < 0.8: # Medium success probability
recommendations.extend([
"Monitor progress closely and implement early warning systems",
"Ensure adequate testing and quality assurance processes",
"Consider additional training for team members on key technologies"
])
else: # High success probability
recommendations.extend([
"Maintain current approach and team composition",
"Consider documenting best practices for future projects",
"Evaluate opportunities for accelerating delivery"
])
elif prediction_result.prediction_type == PredictionType.EFFORT_ESTIMATION:
estimated_hours = prediction_result.predicted_value
if estimated_hours > 2000: # Large effort estimate
recommendations.extend([
"Consider breaking down into smaller deliverables",
"Evaluate opportunities for code reuse and automation",
"Plan for adequate resource allocation and timeline",
"Implement robust project tracking and monitoring"
])
# Add confidence-based recommendations
if prediction_result.confidence_level in [PredictionConfidence.LOW, PredictionConfidence.VERY_LOW]:
recommendations.append(
"Gather more data and refine estimates as project progresses due to low prediction confidence"
)
return recommendations
class TimelinePredictor:
"""
Specialized predictor for project timeline estimation
"""
def __init__(self, config):
self.config = config
async def predict_timeline(self, prediction_input: PredictionInput):
"""
Predict project timeline and milestones
"""
timeline_prediction = PredictionResult(
prediction_id=generate_uuid(),
prediction_type=PredictionType.TIMELINE_ESTIMATION,
predicted_value={},
confidence_level=PredictionConfidence.MEDIUM,
confidence_score=0.7,
time_horizon=TimeHorizon.LONG_TERM
)
# Extract timeline-relevant features
planned_duration = prediction_input.project_characteristics.get('planned_duration_weeks', 12)
team_size = prediction_input.team_characteristics.get('team_size', 5)
complexity = prediction_input.current_metrics.get('complexity_score', 0.5)
# Simple timeline prediction model (would be more sophisticated in practice)
complexity_factor = 1.0 + (complexity * 0.3)
team_efficiency = min(1.0, team_size / 5.0) # Optimal at 5 members
predicted_duration = planned_duration * complexity_factor / team_efficiency
# Add uncertainty based on project characteristics
uncertainty_factor = 0.2 + (complexity * 0.1)
timeline_prediction.predicted_value = {
'estimated_duration_weeks': predicted_duration,
'planned_duration_weeks': planned_duration,
'variance_weeks': predicted_duration * uncertainty_factor,
'completion_probability_on_time': max(0, 1 - (predicted_duration - planned_duration) / planned_duration)
}
timeline_prediction.uncertainty_range = (
predicted_duration * (1 - uncertainty_factor),
predicted_duration * (1 + uncertainty_factor)
)
return timeline_prediction
class QualityPredictor:
"""
Specialized predictor for code and project quality
"""
def __init__(self, config):
self.config = config
async def predict_quality(self, prediction_input: PredictionInput):
"""
Predict final project quality metrics
"""
quality_prediction = PredictionResult(
prediction_id=generate_uuid(),
prediction_type=PredictionType.QUALITY_PREDICTION,
predicted_value={},
confidence_level=PredictionConfidence.MEDIUM,
confidence_score=0.75,
time_horizon=TimeHorizon.MEDIUM_TERM
)
# Extract quality-relevant features
team_experience = prediction_input.team_characteristics.get('average_experience_years', 3)
current_quality = prediction_input.current_metrics.get('quality_score', 0.7)
complexity = prediction_input.current_metrics.get('complexity_score', 0.5)
# Predict quality metrics
experience_factor = min(1.0, team_experience / 5.0) # Optimal at 5+ years
complexity_penalty = complexity * 0.2
predicted_code_quality = min(1.0, (current_quality * 0.7) + (experience_factor * 0.3) - complexity_penalty)
predicted_test_coverage = min(0.95, predicted_code_quality * 0.8 + 0.15)
predicted_defect_density = max(0.1, 2.0 * (1 - predicted_code_quality))
quality_prediction.predicted_value = {
'predicted_code_quality': predicted_code_quality,
'predicted_test_coverage': predicted_test_coverage,
'predicted_defect_density': predicted_defect_density,
'maintainability_score': predicted_code_quality * 0.9,
'technical_debt_risk': 1 - predicted_code_quality
}
return quality_prediction
class RiskPredictor:
"""
Specialized predictor for project risks
"""
def __init__(self, config):
self.config = config
async def assess_risks(self, prediction_input: PredictionInput):
"""
Assess various project risks
"""
risk_assessment = PredictionResult(
prediction_id=generate_uuid(),
prediction_type=PredictionType.RISK_ASSESSMENT,
predicted_value={},
confidence_level=PredictionConfidence.HIGH,
confidence_score=0.8,
time_horizon=TimeHorizon.MEDIUM_TERM
)
# Assess different risk categories
risks = {
'schedule_risk': await self.assess_schedule_risk(prediction_input),
'quality_risk': await self.assess_quality_risk(prediction_input),
'technical_risk': await self.assess_technical_risk(prediction_input),
'team_risk': await self.assess_team_risk(prediction_input),
'external_risk': await self.assess_external_risk(prediction_input)
}
# Calculate overall risk score
risk_weights = {
'schedule_risk': 0.25,
'quality_risk': 0.25,
'technical_risk': 0.2,
'team_risk': 0.2,
'external_risk': 0.1
}
overall_risk = sum(risks[risk] * risk_weights[risk] for risk in risks)
risk_assessment.predicted_value = {
'overall_risk_score': overall_risk,
'risk_level': 'high' if overall_risk > 0.7 else 'medium' if overall_risk > 0.4 else 'low',
'individual_risks': risks,
'mitigation_priority': sorted(risks.items(), key=lambda x: x[1], reverse=True)[:3]
}
return risk_assessment
async def assess_schedule_risk(self, prediction_input):
"""Assess schedule-related risks"""
complexity = prediction_input.current_metrics.get('complexity_score', 0.5)
team_size = prediction_input.team_characteristics.get('team_size', 5)
duration = prediction_input.project_characteristics.get('planned_duration_weeks', 12)
# Higher complexity and longer duration increase schedule risk
schedule_risk = min(1.0, (complexity * 0.4) + (duration / 52) * 0.3 + (1 / max(team_size, 1)) * 0.3)
return schedule_risk
async def assess_quality_risk(self, prediction_input):
"""Assess quality-related risks"""
current_quality = prediction_input.current_metrics.get('quality_score', 0.7)
experience = prediction_input.team_characteristics.get('average_experience_years', 3)
# Lower current quality and less experience increase quality risk
quality_risk = max(0, 1 - current_quality - (experience / 10))
return min(1.0, quality_risk)
async def assess_technical_risk(self, prediction_input):
"""Assess technical risks"""
technologies = prediction_input.project_characteristics.get('technologies', [])
integrations = prediction_input.project_characteristics.get('integrations', [])
innovation_level = prediction_input.context.get('innovation_level', 'medium')
# More technologies and integrations increase technical risk
tech_risk_base = min(0.8, len(technologies) / 10 + len(integrations) / 5)
innovation_multiplier = {'low': 0.8, 'medium': 1.0, 'high': 1.3}.get(innovation_level, 1.0)
return min(1.0, tech_risk_base * innovation_multiplier)
async def assess_team_risk(self, prediction_input):
"""Assess team-related risks"""
team_size = prediction_input.team_characteristics.get('team_size', 5)
turnover_rate = prediction_input.team_characteristics.get('turnover_rate', 0.1)
remote_percentage = prediction_input.team_characteristics.get('remote_percentage', 0.0)
# Very small or very large teams, high turnover, and high remote percentage increase risk
size_risk = 0.2 if team_size < 3 else 0.1 if team_size > 15 else 0
turnover_risk = min(0.5, turnover_rate * 2)
remote_risk = remote_percentage * 0.2
return min(1.0, size_risk + turnover_risk + remote_risk)
async def assess_external_risk(self, prediction_input):
"""Assess external risks"""
market_pressure = prediction_input.context.get('market_pressure', 'medium')
regulatory_count = len(prediction_input.context.get('regulations', []))
dependency_count = len(prediction_input.project_characteristics.get('external_dependencies', []))
pressure_risk = {'low': 0.1, 'medium': 0.3, 'high': 0.6}.get(market_pressure, 0.3)
regulatory_risk = min(0.3, regulatory_count * 0.1)
dependency_risk = min(0.4, dependency_count * 0.05)
return min(1.0, pressure_risk + regulatory_risk + dependency_risk)
```
### Predictive Intelligence Commands
```bash
# Project outcome prediction
bmad predict success --project-context "current-state.json" --confidence-level
bmad predict timeline --features "project-metrics" --scenarios
bmad predict quality --current-metrics --team-characteristics
# Risk assessment and early warning
bmad predict risks --comprehensive --mitigation-suggestions
bmad predict failures --early-warning --probability-assessment
bmad predict bottlenecks --timeline --resource-constraints
# Effort and resource prediction
bmad predict effort --feature-list "backlog.json" --team-velocity
bmad predict resources --capacity-planning --skill-requirements
bmad predict costs --development-timeline --resource-allocation
# Performance and trend prediction
bmad predict performance --load-scenarios --scalability-analysis
bmad predict trends --metrics-history --forecast-horizon "6m"
bmad predict technology --adoption-trends --impact-assessment
# Decision support and optimization
bmad predict scenarios --alternative-approaches --outcome-comparison
bmad predict optimize --resource-allocation --timeline-quality-tradeoffs
bmad predict recommendations --strategic --tactical --operational
# Model management and improvement
bmad predict train --historical-data --model-type "timeline"
bmad predict validate --prediction-accuracy --model-performance
bmad predict insights --contributing-factors --improvement-opportunities
```
This Predictive Development Intelligence module provides sophisticated predictive analytics capabilities that help teams make better decisions, plan more effectively, and achieve better outcomes through data-driven insights, trend analysis, and intelligent forecasting.
<function_calls>
<invoke name="TodoWrite">
<parameter name="todos">[{"content": "Phase 1: Core Intelligence Foundation - COMPLETED \u2705", "status": "completed", "priority": "high", "id": "phase1"}, {"content": "Phase 2: LLM Integration and Knowledge Management - COMPLETED \u2705", "status": "completed", "priority": "high", "id": "phase2"}, {"content": "Phase 3: Advanced Intelligence and Claude Code Integration - COMPLETED \u2705", "status": "completed", "priority": "high", "id": "phase3"}, {"content": "Create Autonomous Development Engine", "status": "completed", "priority": "high", "id": "3.1"}, {"content": "Implement Advanced Code Intelligence", "status": "completed", "priority": "high", "id": "3.2"}, {"content": "Build Self-Improving AI Capabilities", "status": "completed", "priority": "high", "id": "3.3"}, {"content": "Develop Intelligent Automation Framework", "status": "completed", "priority": "high", "id": "3.4"}, {"content": "Create Quality Assurance Automation", "status": "completed", "priority": "high", "id": "3.5"}, {"content": "Implement Performance Optimization Engine", "status": "completed", "priority": "high", "id": "3.6"}, {"content": "Build Predictive Development Intelligence", "status": "completed", "priority": "high", "id": "3.7"}, {"content": "Phase 4: Self-Optimization and Enterprise Features", "status": "pending", "priority": "medium", "id": "phase4"}]
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