31 KiB
31 KiB
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
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
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
# 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.