# 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. [{"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"}]