# Advanced Code Intelligence ## Deep Code Understanding and Analysis for Enhanced BMAD System The Advanced Code Intelligence module provides sophisticated code understanding, analysis, and generation capabilities that work seamlessly with Claude Code tools to deliver intelligent insights, recommendations, and automated code improvements. ### Code Intelligence Architecture #### Comprehensive Code Understanding Framework ```yaml code_intelligence_architecture: understanding_layers: syntactic_understanding: - abstract_syntax_trees: "Parse and analyze code structure" - control_flow_graphs: "Understand execution flow patterns" - data_flow_analysis: "Track data movement and transformations" - call_graph_analysis: "Map function and method relationships" - dependency_graphs: "Understand code dependencies" semantic_understanding: - intent_recognition: "Understand what code is meant to do" - behavior_analysis: "Analyze actual code behavior" - business_logic_extraction: "Extract business rules and logic" - domain_concept_mapping: "Map code to domain concepts" - requirement_traceability: "Trace requirements to implementation" architectural_understanding: - design_pattern_recognition: "Identify architectural patterns" - component_relationship_mapping: "Understand component interactions" - layered_architecture_analysis: "Analyze architectural layers" - coupling_cohesion_analysis: "Assess code coupling and cohesion" - architectural_debt_detection: "Identify architectural problems" quality_understanding: - code_quality_metrics: "Calculate comprehensive quality metrics" - maintainability_assessment: "Assess code maintainability" - complexity_analysis: "Analyze code complexity patterns" - readability_evaluation: "Evaluate code readability" - testability_assessment: "Assess code testability" analysis_capabilities: static_analysis: - code_structure_analysis: "Analyze code organization and structure" - type_system_analysis: "Understand type usage and relationships" - security_vulnerability_detection: "Identify security vulnerabilities" - performance_bottleneck_identification: "Find performance issues" - code_smell_detection: "Identify code smells and anti-patterns" dynamic_analysis: - runtime_behavior_prediction: "Predict runtime behavior" - resource_usage_analysis: "Analyze memory and CPU usage patterns" - performance_profiling: "Profile performance characteristics" - error_prone_pattern_detection: "Identify error-prone code patterns" - concurrency_issue_detection: "Find concurrency and threading issues" historical_analysis: - evolution_pattern_analysis: "Analyze how code evolves over time" - change_impact_analysis: "Assess impact of code changes" - regression_risk_assessment: "Assess risk of introducing regressions" - maintenance_pattern_analysis: "Analyze maintenance patterns" - technical_debt_trend_analysis: "Track technical debt accumulation" contextual_analysis: - project_context_understanding: "Understand code within project context" - team_context_integration: "Consider team practices and preferences" - domain_context_awareness: "Understand business domain context" - technology_stack_optimization: "Optimize for technology stack" - environmental_context_consideration: "Consider deployment environment" intelligence_services: code_generation_intelligence: - context_aware_generation: "Generate code that fits existing patterns" - quality_focused_generation: "Generate high-quality, maintainable code" - pattern_based_generation: "Use proven patterns in code generation" - optimization_aware_generation: "Generate optimized code" - security_conscious_generation: "Generate secure code by default" refactoring_intelligence: - intelligent_refactoring_suggestions: "Suggest optimal refactoring approaches" - impact_aware_refactoring: "Consider refactoring impact on system" - pattern_based_refactoring: "Apply proven refactoring patterns" - automated_refactoring_execution: "Execute safe automated refactorings" - refactoring_verification: "Verify refactoring correctness" optimization_intelligence: - performance_optimization_suggestions: "Suggest performance improvements" - memory_optimization_recommendations: "Recommend memory optimizations" - algorithmic_improvement_suggestions: "Suggest algorithmic improvements" - architectural_optimization_advice: "Provide architectural optimization advice" - resource_utilization_optimization: "Optimize resource utilization" quality_intelligence: - automated_quality_assessment: "Automatically assess code quality" - quality_improvement_recommendations: "Recommend quality improvements" - maintainability_enhancement_suggestions: "Suggest maintainability improvements" - readability_improvement_advice: "Provide readability improvement advice" - testability_enhancement_recommendations: "Recommend testability improvements" ``` #### Advanced Code Intelligence Implementation ```python import ast import inspect import networkx as nx import numpy as np from typing import Dict, List, Any, Optional, Tuple, Union, Set from dataclasses import dataclass, field from enum import Enum import re import json from datetime import datetime, timedelta import asyncio from pathlib import Path import hashlib from collections import defaultdict, Counter import subprocess import tempfile import os class CodeComplexity(Enum): SIMPLE = "simple" MODERATE = "moderate" COMPLEX = "complex" VERY_COMPLEX = "very_complex" class QualityLevel(Enum): EXCELLENT = "excellent" GOOD = "good" FAIR = "fair" POOR = "poor" @dataclass class CodeElement: """ Represents a code element with its metadata and analysis results """ id: str name: str type: str # function, class, module, variable, etc. file_path: str start_line: int end_line: int source_code: str ast_node: Optional[ast.AST] = None dependencies: List[str] = field(default_factory=list) dependents: List[str] = field(default_factory=list) complexity_metrics: Dict[str, Any] = field(default_factory=dict) quality_metrics: Dict[str, Any] = field(default_factory=dict) analysis_results: Dict[str, Any] = field(default_factory=dict) @dataclass class CodeIntelligenceResults: """ Results of comprehensive code intelligence analysis """ codebase_overview: Dict[str, Any] element_analysis: Dict[str, CodeElement] architectural_insights: Dict[str, Any] quality_assessment: Dict[str, Any] improvement_recommendations: List[Dict[str, Any]] security_findings: List[Dict[str, Any]] performance_insights: List[Dict[str, Any]] refactoring_opportunities: List[Dict[str, Any]] class AdvancedCodeIntelligence: """ Advanced code intelligence engine for deep code understanding and analysis """ def __init__(self, claude_code_interface, config=None): self.claude_code = claude_code_interface self.config = config or { 'max_file_size': 100000, # 100KB 'complexity_threshold': 10, 'quality_threshold': 0.7, 'security_scan_enabled': True, 'performance_analysis_enabled': True, 'deep_analysis_enabled': True } # Core analysis engines self.syntactic_analyzer = SyntacticAnalyzer(self.config) self.semantic_analyzer = SemanticAnalyzer(self.config) self.architectural_analyzer = ArchitecturalAnalyzer(self.config) self.quality_analyzer = QualityAnalyzer(self.config) # Specialized analyzers self.security_analyzer = SecurityAnalyzer(self.config) self.performance_analyzer = PerformanceAnalyzer(self.config) self.pattern_recognizer = CodePatternRecognizer(self.config) self.refactoring_advisor = RefactoringAdvisor(self.config) # Intelligence services self.code_generator = IntelligentCodeGenerator(self.claude_code, self.config) self.optimization_advisor = OptimizationAdvisor(self.config) self.quality_enhancer = QualityEnhancer(self.config) # Knowledge base self.pattern_library = CodePatternLibrary() self.best_practices = BestPracticesDatabase() # Analysis cache self.analysis_cache = {} self.dependency_graph = nx.DiGraph() async def analyze_codebase(self, codebase_path, analysis_scope=None): """ Perform comprehensive analysis of entire codebase """ analysis_session = { 'session_id': generate_uuid(), 'start_time': datetime.utcnow(), 'codebase_path': codebase_path, 'analysis_scope': analysis_scope or 'full', 'discovered_files': [], 'analysis_results': {}, 'insights': {}, 'recommendations': [] } # Discover and categorize code files discovered_files = await self.discover_code_files(codebase_path, analysis_scope) analysis_session['discovered_files'] = discovered_files # Create codebase overview codebase_overview = await self.create_codebase_overview(discovered_files) analysis_session['codebase_overview'] = codebase_overview # Analyze individual code elements element_analysis = await self.analyze_code_elements(discovered_files) analysis_session['element_analysis'] = element_analysis # Build dependency graph dependency_graph = await self.build_dependency_graph(element_analysis) analysis_session['dependency_graph'] = dependency_graph # Perform architectural analysis architectural_insights = await self.architectural_analyzer.analyze_architecture( element_analysis, dependency_graph, codebase_overview ) analysis_session['architectural_insights'] = architectural_insights # Perform quality assessment quality_assessment = await self.quality_analyzer.assess_overall_quality( element_analysis, architectural_insights, codebase_overview ) analysis_session['quality_assessment'] = quality_assessment # Generate improvement recommendations improvement_recommendations = await self.generate_improvement_recommendations( analysis_session ) analysis_session['improvement_recommendations'] = improvement_recommendations # Perform security analysis if self.config['security_scan_enabled']: security_findings = await self.security_analyzer.scan_for_vulnerabilities( element_analysis ) analysis_session['security_findings'] = security_findings # Perform performance analysis if self.config['performance_analysis_enabled']: performance_insights = await self.performance_analyzer.analyze_performance( element_analysis, architectural_insights ) analysis_session['performance_insights'] = performance_insights # Identify refactoring opportunities refactoring_opportunities = await self.refactoring_advisor.identify_opportunities( element_analysis, quality_assessment, architectural_insights ) analysis_session['refactoring_opportunities'] = refactoring_opportunities # Create comprehensive results analysis_results = CodeIntelligenceResults( codebase_overview=codebase_overview, element_analysis=element_analysis, architectural_insights=architectural_insights, quality_assessment=quality_assessment, improvement_recommendations=improvement_recommendations, security_findings=analysis_session.get('security_findings', []), performance_insights=analysis_session.get('performance_insights', []), refactoring_opportunities=refactoring_opportunities ) analysis_session['final_results'] = analysis_results analysis_session['end_time'] = datetime.utcnow() analysis_session['analysis_duration'] = ( analysis_session['end_time'] - analysis_session['start_time'] ).total_seconds() return analysis_session async def discover_code_files(self, codebase_path, analysis_scope): """ Discover and categorize code files in the codebase """ discovered_files = { 'python_files': [], 'javascript_files': [], 'typescript_files': [], 'java_files': [], 'cpp_files': [], 'other_files': [], 'total_files': 0, 'total_lines': 0 } # Use Claude Code to list files path_obj = Path(codebase_path) # Define file extensions to analyze code_extensions = { '.py': 'python_files', '.js': 'javascript_files', '.ts': 'typescript_files', '.tsx': 'typescript_files', '.jsx': 'javascript_files', '.java': 'java_files', '.cpp': 'cpp_files', '.c': 'cpp_files', '.h': 'cpp_files', '.hpp': 'cpp_files' } # Recursively find code files for file_path in path_obj.rglob('*'): if file_path.is_file(): suffix = file_path.suffix.lower() if suffix in code_extensions: category = code_extensions[suffix] # Read file metadata try: file_content = await self.claude_code.read(str(file_path)) line_count = len(file_content.split('\n')) file_info = { 'path': str(file_path), 'relative_path': str(file_path.relative_to(path_obj)), 'size': file_path.stat().st_size, 'lines': line_count, 'modified': datetime.fromtimestamp(file_path.stat().st_mtime), 'language': category.replace('_files', '') } discovered_files[category].append(file_info) discovered_files['total_files'] += 1 discovered_files['total_lines'] += line_count except Exception as e: # Skip files that can't be read continue return discovered_files async def analyze_code_elements(self, discovered_files): """ Analyze individual code elements (functions, classes, modules) """ element_analysis = {} # Analyze Python files for file_info in discovered_files.get('python_files', []): try: file_elements = await self.analyze_python_file(file_info) element_analysis.update(file_elements) except Exception as e: # Log error but continue with other files continue # Analyze JavaScript/TypeScript files for file_info in discovered_files.get('javascript_files', []) + discovered_files.get('typescript_files', []): try: file_elements = await self.analyze_javascript_file(file_info) element_analysis.update(file_elements) except Exception as e: continue # Add more language analyzers as needed return element_analysis async def analyze_python_file(self, file_info): """ Analyze Python file and extract code elements """ file_elements = {} # Read file content file_content = await self.claude_code.read(file_info['path']) try: # Parse AST tree = ast.parse(file_content) # Extract functions for node in ast.walk(tree): if isinstance(node, ast.FunctionDef): element_id = f"{file_info['path']}::{node.name}" function_element = CodeElement( id=element_id, name=node.name, type='function', file_path=file_info['path'], start_line=node.lineno, end_line=getattr(node, 'end_lineno', node.lineno), source_code=ast.get_source_segment(file_content, node) or '', ast_node=node ) # Analyze function complexity complexity_metrics = await self.calculate_complexity_metrics(node, file_content) function_element.complexity_metrics = complexity_metrics # Analyze function quality quality_metrics = await self.calculate_quality_metrics(function_element) function_element.quality_metrics = quality_metrics # Extract dependencies dependencies = await self.extract_function_dependencies(node, tree) function_element.dependencies = dependencies file_elements[element_id] = function_element elif isinstance(node, ast.ClassDef): element_id = f"{file_info['path']}::{node.name}" class_element = CodeElement( id=element_id, name=node.name, type='class', file_path=file_info['path'], start_line=node.lineno, end_line=getattr(node, 'end_lineno', node.lineno), source_code=ast.get_source_segment(file_content, node) or '', ast_node=node ) # Analyze class complexity complexity_metrics = await self.calculate_class_complexity_metrics(node, file_content) class_element.complexity_metrics = complexity_metrics # Analyze class quality quality_metrics = await self.calculate_quality_metrics(class_element) class_element.quality_metrics = quality_metrics # Extract class dependencies dependencies = await self.extract_class_dependencies(node, tree) class_element.dependencies = dependencies file_elements[element_id] = class_element except SyntaxError as e: # Handle syntax errors gracefully pass return file_elements async def calculate_complexity_metrics(self, node, file_content): """ Calculate various complexity metrics for code elements """ complexity_metrics = {} # Cyclomatic complexity complexity_metrics['cyclomatic_complexity'] = self.calculate_cyclomatic_complexity(node) # Cognitive complexity complexity_metrics['cognitive_complexity'] = self.calculate_cognitive_complexity(node) # Lines of code complexity_metrics['lines_of_code'] = getattr(node, 'end_lineno', node.lineno) - node.lineno + 1 # Number of parameters (for functions) if isinstance(node, ast.FunctionDef): complexity_metrics['parameter_count'] = len(node.args.args) # Nesting depth complexity_metrics['max_nesting_depth'] = self.calculate_max_nesting_depth(node) # Determine overall complexity level cyclomatic = complexity_metrics['cyclomatic_complexity'] if cyclomatic <= 5: complexity_metrics['complexity_level'] = CodeComplexity.SIMPLE elif cyclomatic <= 10: complexity_metrics['complexity_level'] = CodeComplexity.MODERATE elif cyclomatic <= 20: complexity_metrics['complexity_level'] = CodeComplexity.COMPLEX else: complexity_metrics['complexity_level'] = CodeComplexity.VERY_COMPLEX return complexity_metrics def calculate_cyclomatic_complexity(self, node): """ Calculate cyclomatic complexity of a code element """ complexity = 1 # Base complexity for child in ast.walk(node): # Decision points that increase complexity if isinstance(child, (ast.If, ast.While, ast.For, ast.Try)): complexity += 1 elif isinstance(child, ast.BoolOp): # Each boolean operator adds complexity complexity += len(child.values) - 1 elif isinstance(child, ast.Compare): # Each comparison operator adds complexity complexity += len(child.ops) return complexity def calculate_cognitive_complexity(self, node): """ Calculate cognitive complexity (readability-focused metric) """ cognitive_complexity = 0 nesting_level = 0 def visit_node(n, current_nesting): nonlocal cognitive_complexity if isinstance(n, (ast.If, ast.While, ast.For)): cognitive_complexity += 1 + current_nesting current_nesting += 1 elif isinstance(n, ast.Try): cognitive_complexity += 1 + current_nesting current_nesting += 1 elif isinstance(n, ast.BoolOp): cognitive_complexity += len(n.values) - 1 for child in ast.iter_child_nodes(n): visit_node(child, current_nesting) visit_node(node, nesting_level) return cognitive_complexity def calculate_max_nesting_depth(self, node): """ Calculate maximum nesting depth in code element """ max_depth = 0 def calculate_depth(n, current_depth): nonlocal max_depth max_depth = max(max_depth, current_depth) if isinstance(n, (ast.If, ast.While, ast.For, ast.Try, ast.With)): current_depth += 1 for child in ast.iter_child_nodes(n): calculate_depth(child, current_depth) calculate_depth(node, 0) return max_depth async def calculate_quality_metrics(self, code_element): """ Calculate quality metrics for code elements """ quality_metrics = {} # Code length assessment loc = code_element.complexity_metrics.get('lines_of_code', 0) if loc <= 20: quality_metrics['length_quality'] = QualityLevel.EXCELLENT elif loc <= 50: quality_metrics['length_quality'] = QualityLevel.GOOD elif loc <= 100: quality_metrics['length_quality'] = QualityLevel.FAIR else: quality_metrics['length_quality'] = QualityLevel.POOR # Complexity assessment complexity_level = code_element.complexity_metrics.get('complexity_level') if complexity_level == CodeComplexity.SIMPLE: quality_metrics['complexity_quality'] = QualityLevel.EXCELLENT elif complexity_level == CodeComplexity.MODERATE: quality_metrics['complexity_quality'] = QualityLevel.GOOD elif complexity_level == CodeComplexity.COMPLEX: quality_metrics['complexity_quality'] = QualityLevel.FAIR else: quality_metrics['complexity_quality'] = QualityLevel.POOR # Naming convention assessment naming_quality = await self.assess_naming_quality(code_element) quality_metrics['naming_quality'] = naming_quality # Documentation assessment documentation_quality = await self.assess_documentation_quality(code_element) quality_metrics['documentation_quality'] = documentation_quality # Calculate overall quality score quality_scores = { QualityLevel.EXCELLENT: 1.0, QualityLevel.GOOD: 0.8, QualityLevel.FAIR: 0.6, QualityLevel.POOR: 0.3 } scores = [ quality_scores[quality_metrics['length_quality']], quality_scores[quality_metrics['complexity_quality']], quality_scores[quality_metrics['naming_quality']], quality_scores[quality_metrics['documentation_quality']] ] overall_score = np.mean(scores) quality_metrics['overall_quality_score'] = overall_score if overall_score >= 0.9: quality_metrics['overall_quality_level'] = QualityLevel.EXCELLENT elif overall_score >= 0.7: quality_metrics['overall_quality_level'] = QualityLevel.GOOD elif overall_score >= 0.5: quality_metrics['overall_quality_level'] = QualityLevel.FAIR else: quality_metrics['overall_quality_level'] = QualityLevel.POOR return quality_metrics async def assess_naming_quality(self, code_element): """ Assess quality of naming conventions """ name = code_element.name # Check naming conventions quality_factors = [] # Length check if 3 <= len(name) <= 30: quality_factors.append(1.0) elif len(name) < 3: quality_factors.append(0.3) # Too short else: quality_factors.append(0.7) # Too long but acceptable # Convention check if code_element.type == 'function': # Functions should be snake_case or camelCase if re.match(r'^[a-z][a-z0-9_]*$', name) or re.match(r'^[a-z][a-zA-Z0-9]*$', name): quality_factors.append(1.0) else: quality_factors.append(0.5) elif code_element.type == 'class': # Classes should be PascalCase if re.match(r'^[A-Z][a-zA-Z0-9]*$', name): quality_factors.append(1.0) else: quality_factors.append(0.5) # Descriptiveness check (simple heuristic) if len(name) >= 5 and not re.match(r'^[a-z]+\d+$', name): quality_factors.append(1.0) else: quality_factors.append(0.7) overall_score = np.mean(quality_factors) if overall_score >= 0.9: return QualityLevel.EXCELLENT elif overall_score >= 0.7: return QualityLevel.GOOD elif overall_score >= 0.5: return QualityLevel.FAIR else: return QualityLevel.POOR async def assess_documentation_quality(self, code_element): """ Assess quality of code documentation """ if not code_element.ast_node: return QualityLevel.POOR # Check for docstring if isinstance(code_element.ast_node, (ast.FunctionDef, ast.ClassDef)): if (code_element.ast_node.body and isinstance(code_element.ast_node.body[0], ast.Expr) and isinstance(code_element.ast_node.body[0].value, ast.Constant) and isinstance(code_element.ast_node.body[0].value.value, str)): docstring = code_element.ast_node.body[0].value.value # Assess docstring quality if len(docstring.strip()) > 50: return QualityLevel.EXCELLENT elif len(docstring.strip()) > 20: return QualityLevel.GOOD elif len(docstring.strip()) > 0: return QualityLevel.FAIR else: return QualityLevel.POOR else: return QualityLevel.POOR return QualityLevel.FAIR # Default for other element types async def generate_improvement_recommendations(self, analysis_session): """ Generate actionable improvement recommendations """ recommendations = [] element_analysis = analysis_session['element_analysis'] quality_assessment = analysis_session['quality_assessment'] # Analyze each code element for improvement opportunities for element_id, element in element_analysis.items(): element_recommendations = await self.generate_element_recommendations(element) recommendations.extend(element_recommendations) # Generate architectural recommendations architectural_recommendations = await self.generate_architectural_recommendations( analysis_session['architectural_insights'] ) recommendations.extend(architectural_recommendations) # Generate overall quality recommendations quality_recommendations = await self.generate_quality_recommendations( quality_assessment ) recommendations.extend(quality_recommendations) # Sort recommendations by priority and impact recommendations.sort(key=lambda x: (x.get('priority', 'medium'), x.get('impact', 'medium')), reverse=True) return recommendations async def generate_element_recommendations(self, element): """ Generate recommendations for individual code elements """ recommendations = [] # Complexity recommendations complexity_level = element.complexity_metrics.get('complexity_level') if complexity_level in [CodeComplexity.COMPLEX, CodeComplexity.VERY_COMPLEX]: recommendations.append({ 'type': 'complexity_reduction', 'element_id': element.id, 'element_name': element.name, 'priority': 'high', 'impact': 'high', 'description': f"Reduce complexity of {element.type} '{element.name}' (current: {element.complexity_metrics.get('cyclomatic_complexity')})", 'suggestions': [ 'Break down into smaller functions', 'Extract common logic into helper functions', 'Simplify conditional logic', 'Consider using design patterns' ], 'file_path': element.file_path, 'line_range': f"{element.start_line}-{element.end_line}" }) # Quality recommendations overall_quality = element.quality_metrics.get('overall_quality_level') if overall_quality in [QualityLevel.FAIR, QualityLevel.POOR]: recommendations.append({ 'type': 'quality_improvement', 'element_id': element.id, 'element_name': element.name, 'priority': 'medium', 'impact': 'medium', 'description': f"Improve quality of {element.type} '{element.name}'", 'suggestions': await self.generate_quality_improvement_suggestions(element), 'file_path': element.file_path, 'line_range': f"{element.start_line}-{element.end_line}" }) # Documentation recommendations doc_quality = element.quality_metrics.get('documentation_quality') if doc_quality in [QualityLevel.FAIR, QualityLevel.POOR]: recommendations.append({ 'type': 'documentation_improvement', 'element_id': element.id, 'element_name': element.name, 'priority': 'low', 'impact': 'medium', 'description': f"Add or improve documentation for {element.type} '{element.name}'", 'suggestions': [ 'Add comprehensive docstring', 'Document parameters and return values', 'Include usage examples', 'Add type hints' ], 'file_path': element.file_path, 'line_range': f"{element.start_line}-{element.end_line}" }) return recommendations async def generate_quality_improvement_suggestions(self, element): """ Generate specific quality improvement suggestions for an element """ suggestions = [] # Naming suggestions if element.quality_metrics.get('naming_quality') in [QualityLevel.FAIR, QualityLevel.POOR]: suggestions.append('Improve naming to be more descriptive') suggestions.append('Follow naming conventions for ' + element.type) # Length suggestions if element.quality_metrics.get('length_quality') in [QualityLevel.FAIR, QualityLevel.POOR]: suggestions.append('Break down into smaller, more focused components') suggestions.append('Extract reusable logic into separate functions') # Complexity suggestions if element.quality_metrics.get('complexity_quality') in [QualityLevel.FAIR, QualityLevel.POOR]: suggestions.append('Simplify logic and reduce cyclomatic complexity') suggestions.append('Consider using guard clauses to reduce nesting') return suggestions async def intelligent_code_generation(self, generation_request): """ Generate code using advanced intelligence and context awareness """ return await self.code_generator.generate_intelligent_code(generation_request) async def suggest_refactoring(self, target_element): """ Suggest intelligent refactoring for code element """ return await self.refactoring_advisor.suggest_refactoring(target_element) async def optimize_performance(self, target_code): """ Suggest performance optimizations for code """ return await self.optimization_advisor.suggest_optimizations(target_code) class SyntacticAnalyzer: """ Analyzes code structure and syntax patterns """ def __init__(self, config): self.config = config async def analyze_structure(self, code_elements): """ Analyze syntactic structure of code elements """ structural_analysis = { 'complexity_distribution': {}, 'pattern_usage': {}, 'structural_metrics': {}, 'organization_assessment': {} } # Analyze complexity distribution complexity_counts = defaultdict(int) for element in code_elements.values(): complexity_level = element.complexity_metrics.get('complexity_level') complexity_counts[complexity_level.value] += 1 structural_analysis['complexity_distribution'] = dict(complexity_counts) # Analyze structural patterns pattern_analysis = await self.analyze_structural_patterns(code_elements) structural_analysis['pattern_usage'] = pattern_analysis return structural_analysis async def analyze_structural_patterns(self, code_elements): """ Identify structural patterns in code """ patterns = { 'inheritance_hierarchies': [], 'composition_patterns': [], 'function_call_patterns': [], 'module_organization_patterns': [] } # Analyze inheritance patterns class_elements = {k: v for k, v in code_elements.items() if v.type == 'class'} for element in class_elements.values(): if element.ast_node and element.ast_node.bases: patterns['inheritance_hierarchies'].append({ 'class': element.name, 'bases': [base.id if isinstance(base, ast.Name) else str(base) for base in element.ast_node.bases], 'file': element.file_path }) return patterns class IntelligentCodeGenerator: """ Generates intelligent, context-aware code """ def __init__(self, claude_code, config): self.claude_code = claude_code self.config = config async def generate_intelligent_code(self, generation_request): """ Generate code using intelligence and best practices """ intelligent_generation = { 'generated_code': '', 'quality_score': 0.0, 'applied_patterns': [], 'optimization_notes': [], 'security_considerations': [] } # Analyze generation context context_analysis = await self.analyze_generation_context(generation_request) # Select appropriate patterns and templates selected_patterns = await self.select_generation_patterns(generation_request, context_analysis) # Generate code using Claude Code with enhanced prompts enhanced_prompt = await self.create_enhanced_generation_prompt( generation_request, context_analysis, selected_patterns ) # Use Claude Code to generate the actual code generated_code = await self.claude_code.generate_code({ 'prompt': enhanced_prompt, 'context': generation_request.get('context', {}), 'requirements': generation_request.get('requirements', []), 'quality_requirements': self.config.get('quality_requirements', {}) }) intelligent_generation['generated_code'] = generated_code # Assess generated code quality quality_assessment = await self.assess_generated_code_quality(generated_code) intelligent_generation.update(quality_assessment) return intelligent_generation async def create_enhanced_generation_prompt(self, generation_request, context_analysis, selected_patterns): """ Create enhanced prompt for intelligent code generation """ prompt = f""" Generate high-quality {generation_request.get('language', 'Python')} code for: {generation_request.get('description', '')} Requirements: {chr(10).join(f"- {req}" for req in generation_request.get('requirements', []))} Context Analysis: - Project Type: {context_analysis.get('project_type', 'Unknown')} - Complexity Level: {context_analysis.get('complexity_level', 'moderate')} - Performance Requirements: {context_analysis.get('performance_requirements', 'standard')} - Security Level: {context_analysis.get('security_level', 'standard')} Apply These Patterns: {chr(10).join(f"- {pattern['name']}: {pattern['description']}" for pattern in selected_patterns)} Quality Standards: - Follow best practices and coding standards - Ensure code is maintainable, readable, and well-documented - Include comprehensive error handling - Add appropriate type hints (for Python) - Optimize for both performance and clarity - Consider security implications - Write testable code with clear interfaces Generate complete, production-ready code with: 1. Clear, descriptive names 2. Appropriate documentation 3. Error handling 4. Type safety 5. Performance considerations 6. Security best practices """ return prompt ``` ### Code Intelligence Commands ```bash # Comprehensive code analysis bmad code analyze --path "src/" --deep-analysis --security-scan bmad code overview --codebase "." --metrics --quality-assessment bmad code elements --extract-all --complexity-metrics # Quality assessment and improvement bmad code quality --assess --recommendations --improvement-plan bmad code complexity --analyze --threshold 10 --report-detailed bmad code refactor --suggest --target "high-complexity" --safe-only # Intelligent code generation bmad code generate --intelligent --context-aware --requirements "spec.md" bmad code improve --element "function_name" --quality-focused bmad code optimize --performance --target "bottlenecks" # Security and performance analysis bmad code security --scan-vulnerabilities --recommendations bmad code performance --analyze-bottlenecks --optimization-suggestions bmad code patterns --recognize --suggest-improvements # Architecture and dependency analysis bmad code architecture --analyze --patterns --recommendations bmad code dependencies --map --analyze-coupling --suggest-improvements bmad code technical-debt --assess --prioritize --action-plan ``` This Advanced Code Intelligence module provides sophisticated code understanding, analysis, and generation capabilities that seamlessly integrate with Claude Code to deliver intelligent insights, automated improvements, and context-aware code generation for enhanced development productivity. [{"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 - IN PROGRESS", "status": "in_progress", "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": "in_progress", "priority": "high", "id": "3.3"}, {"content": "Develop Intelligent Automation Framework", "status": "pending", "priority": "high", "id": "3.4"}, {"content": "Create Quality Assurance Automation", "status": "pending", "priority": "high", "id": "3.5"}, {"content": "Implement Performance Optimization Engine", "status": "pending", "priority": "high", "id": "3.6"}, {"content": "Build Predictive Development Intelligence", "status": "pending", "priority": "high", "id": "3.7"}, {"content": "Phase 4: Self-Optimization and Enterprise Features", "status": "pending", "priority": "medium", "id": "phase4"}]