# Pattern Mining Engine ## Automated Knowledge Discovery and Insight Generation for Enhanced BMAD System The Pattern Mining Engine provides sophisticated automated discovery of patterns, trends, and insights from development activities, code repositories, and team collaboration data to generate actionable intelligence for software development. ### Knowledge Discovery Architecture #### Comprehensive Discovery Framework ```yaml pattern_mining_architecture: discovery_domains: code_pattern_mining: - structural_patterns: "AST-based code structure patterns" - semantic_patterns: "Meaning and intent patterns in code" - anti_patterns: "Code patterns leading to issues" - evolution_patterns: "How code patterns change over time" - performance_patterns: "Code patterns affecting performance" development_process_mining: - workflow_patterns: "Effective development workflow patterns" - collaboration_patterns: "Successful team collaboration patterns" - decision_patterns: "Patterns in technical decision making" - communication_patterns: "Effective communication patterns" - productivity_patterns: "Patterns leading to high productivity" project_success_mining: - success_factor_patterns: "Factors consistently leading to success" - failure_pattern_analysis: "Common patterns in project failures" - timeline_patterns: "Effective project timeline patterns" - resource_allocation_patterns: "Optimal resource usage patterns" - risk_mitigation_patterns: "Effective risk management patterns" technology_adoption_mining: - adoption_trend_patterns: "Technology adoption lifecycle patterns" - integration_patterns: "Successful technology integration patterns" - migration_patterns: "Effective technology migration patterns" - compatibility_patterns: "Technology compatibility insights" - learning_curve_patterns: "Technology learning and mastery patterns" mining_techniques: statistical_mining: - frequency_analysis: "Identify frequently occurring patterns" - correlation_analysis: "Find correlations between variables" - regression_analysis: "Predict outcomes based on patterns" - clustering_analysis: "Group similar patterns together" - time_series_analysis: "Analyze patterns over time" machine_learning_mining: - supervised_learning: "Pattern classification and prediction" - unsupervised_learning: "Pattern discovery without labels" - reinforcement_learning: "Learn optimal pattern applications" - deep_learning: "Complex pattern recognition" - ensemble_methods: "Combine multiple mining approaches" graph_mining: - network_analysis: "Analyze relationship networks" - community_detection: "Find pattern communities" - centrality_analysis: "Identify important pattern nodes" - path_analysis: "Analyze pattern propagation paths" - evolution_analysis: "Track pattern network evolution" text_mining: - natural_language_processing: "Extract patterns from text" - sentiment_analysis: "Analyze sentiment patterns" - topic_modeling: "Discover topic patterns" - entity_extraction: "Extract entity relationship patterns" - semantic_analysis: "Understand meaning patterns" insight_generation: predictive_insights: - success_prediction: "Predict project success likelihood" - failure_prediction: "Predict potential failure points" - performance_prediction: "Predict performance outcomes" - timeline_prediction: "Predict realistic timelines" - resource_prediction: "Predict resource requirements" prescriptive_insights: - optimization_recommendations: "Recommend optimization strategies" - process_improvements: "Suggest process improvements" - technology_recommendations: "Recommend technology choices" - team_recommendations: "Suggest team configurations" - architecture_recommendations: "Recommend architectural patterns" diagnostic_insights: - problem_identification: "Identify current problems" - root_cause_analysis: "Find root causes of issues" - bottleneck_identification: "Identify process bottlenecks" - risk_assessment: "Assess current risks" - quality_assessment: "Assess current quality levels" ``` #### Pattern Mining Engine Implementation ```python import numpy as np import pandas as pd from sklearn.cluster import DBSCAN, KMeans from sklearn.ensemble import RandomForestClassifier, IsolationForest from sklearn.decomposition import PCA, NMF from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.metrics.pairwise import cosine_similarity import networkx as nx from scipy import stats from collections import defaultdict, Counter import ast import re from datetime import datetime, timedelta import asyncio from typing import Dict, List, Any, Optional, Tuple import joblib class PatternMiningEngine: """ Advanced pattern mining and knowledge discovery engine """ def __init__(self, config=None): self.config = config or { 'min_pattern_frequency': 0.05, 'pattern_confidence_threshold': 0.7, 'anomaly_detection_threshold': 0.1, 'time_window_days': 90, 'max_patterns_per_category': 100 } # Mining components self.code_pattern_miner = CodePatternMiner(self.config) self.process_pattern_miner = ProcessPatternMiner(self.config) self.success_pattern_miner = SuccessPatternMiner(self.config) self.technology_pattern_miner = TechnologyPatternMiner(self.config) # Analytics components self.statistical_analyzer = StatisticalAnalyzer() self.ml_analyzer = MachineLearningAnalyzer() self.graph_analyzer = GraphAnalyzer() self.text_analyzer = TextAnalyzer() # Insight generation self.insight_generator = InsightGenerator() self.prediction_engine = PredictionEngine() # Pattern storage self.discovered_patterns = {} self.pattern_history = [] async def discover_patterns(self, data_sources, discovery_config=None): """ Discover patterns across all domains from multiple data sources """ if discovery_config is None: discovery_config = { 'domains': ['code', 'process', 'success', 'technology'], 'techniques': ['statistical', 'ml', 'graph', 'text'], 'insight_types': ['predictive', 'prescriptive', 'diagnostic'], 'time_range': {'start': None, 'end': None} } discovery_session = { 'session_id': generate_uuid(), 'start_time': datetime.utcnow(), 'data_sources': data_sources, 'discovery_config': discovery_config, 'domain_patterns': {}, 'cross_domain_insights': {}, 'generated_insights': {} } # Discover patterns in each domain domain_tasks = [] if 'code' in discovery_config['domains']: domain_tasks.append( self.discover_code_patterns(data_sources.get('code', {}), discovery_config) ) if 'process' in discovery_config['domains']: domain_tasks.append( self.discover_process_patterns(data_sources.get('process', {}), discovery_config) ) if 'success' in discovery_config['domains']: domain_tasks.append( self.discover_success_patterns(data_sources.get('success', {}), discovery_config) ) if 'technology' in discovery_config['domains']: domain_tasks.append( self.discover_technology_patterns(data_sources.get('technology', {}), discovery_config) ) # Execute pattern discovery in parallel domain_results = await asyncio.gather(*domain_tasks, return_exceptions=True) # Store domain patterns domain_names = [d for d in discovery_config['domains']] for i, result in enumerate(domain_results): if i < len(domain_names) and not isinstance(result, Exception): discovery_session['domain_patterns'][domain_names[i]] = result # Find cross-domain insights cross_domain_insights = await self.find_cross_domain_insights( discovery_session['domain_patterns'], discovery_config ) discovery_session['cross_domain_insights'] = cross_domain_insights # Generate actionable insights generated_insights = await self.generate_actionable_insights( discovery_session['domain_patterns'], cross_domain_insights, discovery_config ) discovery_session['generated_insights'] = generated_insights # Store patterns for future reference await self.store_discovered_patterns(discovery_session) discovery_session['end_time'] = datetime.utcnow() discovery_session['discovery_duration'] = ( discovery_session['end_time'] - discovery_session['start_time'] ).total_seconds() return discovery_session async def discover_code_patterns(self, code_data, discovery_config): """ Discover patterns in code repositories and development activities """ code_pattern_results = { 'structural_patterns': {}, 'semantic_patterns': {}, 'anti_patterns': {}, 'evolution_patterns': {}, 'performance_patterns': {} } # Extract structural patterns using AST analysis if 'structural' in discovery_config.get('pattern_types', ['structural']): structural_patterns = await self.code_pattern_miner.mine_structural_patterns( code_data ) code_pattern_results['structural_patterns'] = structural_patterns # Extract semantic patterns using NLP and code semantics if 'semantic' in discovery_config.get('pattern_types', ['semantic']): semantic_patterns = await self.code_pattern_miner.mine_semantic_patterns( code_data ) code_pattern_results['semantic_patterns'] = semantic_patterns # Identify anti-patterns that lead to issues if 'anti_pattern' in discovery_config.get('pattern_types', ['anti_pattern']): anti_patterns = await self.code_pattern_miner.mine_anti_patterns( code_data ) code_pattern_results['anti_patterns'] = anti_patterns # Analyze code evolution patterns if 'evolution' in discovery_config.get('pattern_types', ['evolution']): evolution_patterns = await self.code_pattern_miner.mine_evolution_patterns( code_data ) code_pattern_results['evolution_patterns'] = evolution_patterns # Identify performance-related patterns if 'performance' in discovery_config.get('pattern_types', ['performance']): performance_patterns = await self.code_pattern_miner.mine_performance_patterns( code_data ) code_pattern_results['performance_patterns'] = performance_patterns return code_pattern_results async def discover_success_patterns(self, success_data, discovery_config): """ Discover patterns that lead to project and team success """ success_pattern_results = { 'success_factors': {}, 'failure_indicators': {}, 'timeline_patterns': {}, 'resource_patterns': {}, 'quality_patterns': {} } # Identify success factor patterns success_factors = await self.success_pattern_miner.mine_success_factors( success_data ) success_pattern_results['success_factors'] = success_factors # Identify failure indicator patterns failure_indicators = await self.success_pattern_miner.mine_failure_indicators( success_data ) success_pattern_results['failure_indicators'] = failure_indicators # Analyze timeline patterns timeline_patterns = await self.success_pattern_miner.mine_timeline_patterns( success_data ) success_pattern_results['timeline_patterns'] = timeline_patterns # Analyze resource allocation patterns resource_patterns = await self.success_pattern_miner.mine_resource_patterns( success_data ) success_pattern_results['resource_patterns'] = resource_patterns # Analyze quality patterns quality_patterns = await self.success_pattern_miner.mine_quality_patterns( success_data ) success_pattern_results['quality_patterns'] = quality_patterns return success_pattern_results async def find_cross_domain_insights(self, domain_patterns, discovery_config): """ Find insights that span across multiple domains """ cross_domain_insights = { 'code_process_correlations': {}, 'success_technology_patterns': {}, 'performance_quality_relationships': {}, 'evolution_adoption_trends': {} } # Analyze correlations between code patterns and process patterns if 'code' in domain_patterns and 'process' in domain_patterns: code_process_correlations = await self.analyze_code_process_correlations( domain_patterns['code'], domain_patterns['process'] ) cross_domain_insights['code_process_correlations'] = code_process_correlations # Analyze relationships between success patterns and technology patterns if 'success' in domain_patterns and 'technology' in domain_patterns: success_tech_patterns = await self.analyze_success_technology_relationships( domain_patterns['success'], domain_patterns['technology'] ) cross_domain_insights['success_technology_patterns'] = success_tech_patterns # Analyze performance-quality relationships performance_quality_relationships = await self.analyze_performance_quality_relationships( domain_patterns ) cross_domain_insights['performance_quality_relationships'] = performance_quality_relationships # Analyze evolution and adoption trends evolution_adoption_trends = await self.analyze_evolution_adoption_trends( domain_patterns ) cross_domain_insights['evolution_adoption_trends'] = evolution_adoption_trends return cross_domain_insights async def generate_actionable_insights(self, domain_patterns, cross_domain_insights, discovery_config): """ Generate actionable insights from discovered patterns """ actionable_insights = { 'predictive_insights': {}, 'prescriptive_insights': {}, 'diagnostic_insights': {} } # Generate predictive insights if 'predictive' in discovery_config.get('insight_types', ['predictive']): predictive_insights = await self.insight_generator.generate_predictive_insights( domain_patterns, cross_domain_insights ) actionable_insights['predictive_insights'] = predictive_insights # Generate prescriptive insights if 'prescriptive' in discovery_config.get('insight_types', ['prescriptive']): prescriptive_insights = await self.insight_generator.generate_prescriptive_insights( domain_patterns, cross_domain_insights ) actionable_insights['prescriptive_insights'] = prescriptive_insights # Generate diagnostic insights if 'diagnostic' in discovery_config.get('insight_types', ['diagnostic']): diagnostic_insights = await self.insight_generator.generate_diagnostic_insights( domain_patterns, cross_domain_insights ) actionable_insights['diagnostic_insights'] = diagnostic_insights return actionable_insights class CodePatternMiner: """ Specialized mining for code patterns and anti-patterns """ def __init__(self, config): self.config = config self.ast_analyzer = ASTPatternAnalyzer() self.semantic_analyzer = SemanticCodeAnalyzer() async def mine_structural_patterns(self, code_data): """ Mine structural patterns from code using AST analysis """ structural_patterns = { 'function_patterns': {}, 'class_patterns': {}, 'module_patterns': {}, 'architecture_patterns': {} } # Analyze function patterns function_patterns = await self.ast_analyzer.analyze_function_patterns(code_data) structural_patterns['function_patterns'] = function_patterns # Analyze class patterns class_patterns = await self.ast_analyzer.analyze_class_patterns(code_data) structural_patterns['class_patterns'] = class_patterns # Analyze module patterns module_patterns = await self.ast_analyzer.analyze_module_patterns(code_data) structural_patterns['module_patterns'] = module_patterns # Analyze architectural patterns architecture_patterns = await self.ast_analyzer.analyze_architecture_patterns(code_data) structural_patterns['architecture_patterns'] = architecture_patterns return structural_patterns async def mine_semantic_patterns(self, code_data): """ Mine semantic patterns from code using NLP and semantic analysis """ semantic_patterns = { 'intent_patterns': {}, 'naming_patterns': {}, 'comment_patterns': {}, 'documentation_patterns': {} } # Analyze code intent patterns intent_patterns = await self.semantic_analyzer.analyze_intent_patterns(code_data) semantic_patterns['intent_patterns'] = intent_patterns # Analyze naming convention patterns naming_patterns = await self.semantic_analyzer.analyze_naming_patterns(code_data) semantic_patterns['naming_patterns'] = naming_patterns # Analyze comment patterns comment_patterns = await self.semantic_analyzer.analyze_comment_patterns(code_data) semantic_patterns['comment_patterns'] = comment_patterns # Analyze documentation patterns doc_patterns = await self.semantic_analyzer.analyze_documentation_patterns(code_data) semantic_patterns['documentation_patterns'] = doc_patterns return semantic_patterns async def mine_anti_patterns(self, code_data): """ Identify anti-patterns that lead to technical debt and issues """ anti_patterns = { 'code_smells': {}, 'architectural_anti_patterns': {}, 'performance_anti_patterns': {}, 'security_anti_patterns': {} } # Detect code smells code_smells = await self.detect_code_smells(code_data) anti_patterns['code_smells'] = code_smells # Detect architectural anti-patterns arch_anti_patterns = await self.detect_architectural_anti_patterns(code_data) anti_patterns['architectural_anti_patterns'] = arch_anti_patterns # Detect performance anti-patterns perf_anti_patterns = await self.detect_performance_anti_patterns(code_data) anti_patterns['performance_anti_patterns'] = perf_anti_patterns # Detect security anti-patterns security_anti_patterns = await self.detect_security_anti_patterns(code_data) anti_patterns['security_anti_patterns'] = security_anti_patterns return anti_patterns async def detect_code_smells(self, code_data): """ Detect various code smells in the codebase """ code_smells = { 'long_methods': [], 'large_classes': [], 'duplicate_code': [], 'dead_code': [], 'complex_conditionals': [] } for file_path, file_content in code_data.items(): try: # Parse AST tree = ast.parse(file_content) # Detect long methods long_methods = self.detect_long_methods(tree, file_path) code_smells['long_methods'].extend(long_methods) # Detect large classes large_classes = self.detect_large_classes(tree, file_path) code_smells['large_classes'].extend(large_classes) # Detect complex conditionals complex_conditionals = self.detect_complex_conditionals(tree, file_path) code_smells['complex_conditionals'].extend(complex_conditionals) except SyntaxError: # Skip files with syntax errors continue # Detect duplicate code across files duplicate_code = await self.detect_duplicate_code(code_data) code_smells['duplicate_code'] = duplicate_code return code_smells def detect_long_methods(self, tree, file_path): """ Detect methods that are too long """ long_methods = [] max_lines = self.config.get('max_method_lines', 50) for node in ast.walk(tree): if isinstance(node, ast.FunctionDef): method_lines = node.end_lineno - node.lineno + 1 if method_lines > max_lines: long_methods.append({ 'file': file_path, 'method': node.name, 'lines': method_lines, 'start_line': node.lineno, 'end_line': node.end_lineno, 'severity': 'high' if method_lines > max_lines * 2 else 'medium' }) return long_methods def detect_large_classes(self, tree, file_path): """ Detect classes that are too large """ large_classes = [] max_methods = self.config.get('max_class_methods', 20) for node in ast.walk(tree): if isinstance(node, ast.ClassDef): method_count = sum(1 for child in node.body if isinstance(child, ast.FunctionDef)) if method_count > max_methods: large_classes.append({ 'file': file_path, 'class': node.name, 'methods': method_count, 'start_line': node.lineno, 'severity': 'high' if method_count > max_methods * 2 else 'medium' }) return large_classes class SuccessPatternMiner: """ Mine patterns that lead to project and team success """ def __init__(self, config): self.config = config async def mine_success_factors(self, success_data): """ Mine factors that consistently lead to success """ success_factors = { 'team_factors': {}, 'process_factors': {}, 'technical_factors': {}, 'environmental_factors': {} } # Analyze team-related success factors team_factors = await self.analyze_team_success_factors(success_data) success_factors['team_factors'] = team_factors # Analyze process-related success factors process_factors = await self.analyze_process_success_factors(success_data) success_factors['process_factors'] = process_factors # Analyze technical success factors technical_factors = await self.analyze_technical_success_factors(success_data) success_factors['technical_factors'] = technical_factors # Analyze environmental success factors environmental_factors = await self.analyze_environmental_success_factors(success_data) success_factors['environmental_factors'] = environmental_factors return success_factors async def analyze_team_success_factors(self, success_data): """ Analyze team-related factors that lead to success """ team_factors = { 'size_patterns': {}, 'skill_patterns': {}, 'collaboration_patterns': {}, 'communication_patterns': {} } # Get project data with success metrics projects = success_data.get('projects', []) # Analyze team size patterns size_success_correlation = {} for project in projects: team_size = project.get('team_size', 0) success_score = project.get('success_score', 0) size_bucket = self.bucket_team_size(team_size) if size_bucket not in size_success_correlation: size_success_correlation[size_bucket] = {'scores': [], 'count': 0} size_success_correlation[size_bucket]['scores'].append(success_score) size_success_correlation[size_bucket]['count'] += 1 # Calculate average success by team size for size_bucket, data in size_success_correlation.items(): if data['scores']: avg_success = np.mean(data['scores']) team_factors['size_patterns'][size_bucket] = { 'average_success': avg_success, 'project_count': data['count'], 'success_variance': np.var(data['scores']) } return team_factors def bucket_team_size(self, team_size): """ Bucket team sizes for analysis """ if team_size <= 3: return 'small' elif team_size <= 7: return 'medium' elif team_size <= 12: return 'large' else: return 'very_large' class InsightGenerator: """ Generate actionable insights from discovered patterns """ def __init__(self): self.insight_templates = { 'success_prediction': self.generate_success_prediction_insights, 'optimization_recommendation': self.generate_optimization_insights, 'risk_assessment': self.generate_risk_assessment_insights, 'best_practice': self.generate_best_practice_insights } async def generate_predictive_insights(self, domain_patterns, cross_domain_insights): """ Generate insights that predict future outcomes """ predictive_insights = { 'success_predictions': [], 'risk_predictions': [], 'performance_predictions': [], 'timeline_predictions': [] } # Generate success predictions if 'success' in domain_patterns: success_predictions = await self.generate_success_predictions( domain_patterns['success'], cross_domain_insights ) predictive_insights['success_predictions'] = success_predictions # Generate risk predictions risk_predictions = await self.generate_risk_predictions( domain_patterns, cross_domain_insights ) predictive_insights['risk_predictions'] = risk_predictions return predictive_insights async def generate_success_predictions(self, success_patterns, cross_domain_insights): """ Generate predictions about project success """ success_predictions = [] # Analyze success factor patterns success_factors = success_patterns.get('success_factors', {}) for factor_category, factors in success_factors.items(): for factor_name, factor_data in factors.items(): if factor_data.get('average_success', 0) > 0.8: # High success correlation prediction = { 'type': 'success_factor', 'factor': factor_name, 'category': factor_category, 'prediction': f"Projects with {factor_name} have {factor_data['average_success']*100:.1f}% higher success rate", 'confidence': min(factor_data.get('project_count', 0) / 100, 1.0), 'recommendation': f"Ensure {factor_name} is prioritized in project planning" } success_predictions.append(prediction) return success_predictions ``` ### Knowledge Discovery Commands ```bash # Pattern mining and discovery bmad discover patterns --domains "code,process,success" --time-range "90d" bmad discover anti-patterns --codebase "src/" --severity "high" bmad discover trends --technology-adoption --cross-project # Insight generation bmad insights generate --type "predictive" --focus "success-factors" bmad insights analyze --correlations --cross-domain bmad insights recommend --optimization --based-on-patterns # Pattern analysis and exploration bmad patterns explore --category "code-quality" --interactive bmad patterns correlate --pattern1 "team-size" --pattern2 "success-rate" bmad patterns export --discovered --format "detailed-report" # Predictive analytics bmad predict success --project-characteristics "current" bmad predict risks --based-on-patterns --alert-threshold "high" bmad predict performance --code-changes "recent" --model "ml-ensemble" ``` This Pattern Mining Engine provides sophisticated automated discovery of patterns and insights that can transform development practices by identifying what works, what doesn't, and what's likely to happen based on historical data and current trends.