43 KiB
43 KiB
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
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
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
# 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.
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