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⚙️ Session 6 Advanced: Code GraphRAG Implementation

⚙️ IMPLEMENTER PATH CONTENT Prerequisites: Complete 🎯 Observer and 📝 Participant paths Time Investment: 2-3 hours Outcome: Master code analysis with graph-based reasoning

Advanced Learning Outcomes

After completing this module, you will master:

  • AST-based code analysis for graph construction
  • Software dependency modeling with GraphRAG
  • Code pattern recognition through graph traversal
  • Integration with development workflow tools

Understanding Software Knowledge Graphs

Code GraphRAG transforms software repositories into queryable knowledge graphs where:

  • Nodes represent functions, classes, modules, and dependencies
  • Edges represent calls, imports, inheritance, and data flow
  • Attributes capture metrics, documentation, and metadata

This enables complex queries like "Find all functions that use deprecated APIs" or "Trace data flow from input validation to database operations."

Advanced AST-Based Graph Construction

Multi-Language AST Processing

Different programming languages require specialized AST processing approaches:

class MultiLanguageASTProcessor:
    """Advanced AST processor for multiple programming languages"""

    def __init__(self):
        self.language_processors = {
            'python': PythonASTProcessor(),
            'javascript': JavaScriptASTProcessor(),
            'java': JavaASTProcessor(),
            'typescript': TypeScriptASTProcessor()
        }

The multi-language approach enables consistent graph representation across polyglot codebases while respecting language-specific semantics.

    def process_file(self, file_path, content):
        """Process source file based on language detection"""

        language = self.detect_language(file_path)
        processor = self.language_processors.get(language)

        if not processor:
            return self.fallback_processing(file_path, content)

        # Language-specific AST parsing
        ast_tree = processor.parse_ast(content)

        # Extract unified node representations
        code_nodes = self.extract_unified_nodes(ast_tree, language)
        code_edges = self.extract_relationships(ast_tree, language)

        return {
            'nodes': code_nodes,
            'edges': code_edges,
            'metadata': {
                'language': language,
                'file_path': file_path,
                'complexity_metrics': processor.calculate_complexity(ast_tree)
            }
        }

Advanced Function Analysis

Function nodes require rich metadata for meaningful graph queries:

def analyze_function_advanced(func_node, ast_tree, file_context):
    """Extract comprehensive function metadata"""

    # Basic function information
    func_info = {
        'name': func_node.name,
        'line_start': func_node.lineno,
        'line_end': func_node.end_lineno,
        'type': 'function'
    }

Basic information provides the foundation for graph construction and source code linking.

    # Advanced static analysis
    func_info.update({
        'cyclomatic_complexity': calculate_cyclomatic_complexity(func_node),
        'parameter_count': len(func_node.args.args),
        'return_complexity': analyze_return_patterns(func_node),
        'side_effects': detect_side_effects(func_node),
        'external_dependencies': find_external_calls(func_node)
    })

Static analysis metrics enable quality assessment and architectural pattern detection through graph queries.

    # Documentation and semantic analysis
    docstring = ast.get_docstring(func_node)
    if docstring:
        func_info.update({
            'documentation': docstring,
            'semantic_tags': extract_semantic_tags(docstring),
            'api_stability': assess_api_stability(docstring)
        })

Semantic analysis of documentation enables natural language queries about code functionality and API stability assessments.

    # Control flow analysis
    func_info['control_flow'] = {
        'branches': count_conditional_branches(func_node),
        'loops': identify_loop_structures(func_node),
        'error_handling': analyze_exception_handling(func_node),
        'async_patterns': detect_async_patterns(func_node)
    }

    return func_info

Dependency Modeling and Analysis

Advanced Dependency Graph Construction

Software dependencies form complex networks requiring sophisticated modeling:

class AdvancedDependencyAnalyzer:
    """Comprehensive dependency analysis for code graphs"""

    def __init__(self, project_root):
        self.project_root = project_root
        self.dependency_types = {
            'import': 'direct module import',
            'call': 'function/method invocation',
            'inheritance': 'class inheritance relationship',
            'composition': 'object composition pattern',
            'data_flow': 'data passing between components'
        }

Different dependency types require different analysis approaches and have different implications for code understanding.

    def analyze_project_dependencies(self):
        """Build comprehensive dependency graph"""

        all_files = self.discover_source_files()
        dependency_graph = nx.DiGraph()

        # First pass: identify all code entities
        for file_path in all_files:
            entities = self.extract_code_entities(file_path)
            for entity in entities:
                dependency_graph.add_node(
                    entity['id'],
                    **entity['metadata']
                )

The two-pass approach ensures all entities are registered before analyzing relationships, enabling forward references and circular dependencies.

        # Second pass: analyze relationships
        for file_path in all_files:
            relationships = self.analyze_file_relationships(
                file_path,
                dependency_graph
            )

            for rel in relationships:
                if rel['target'] in dependency_graph.nodes:
                    dependency_graph.add_edge(
                        rel['source'],
                        rel['target'],
                        relationship_type=rel['type'],
                        strength=rel['strength'],
                        evidence=rel['evidence']
                    )

        return dependency_graph

Circular Dependency Detection

Circular dependencies indicate architectural issues and can be detected through graph analysis:

def detect_circular_dependencies(dependency_graph, max_cycle_length=10):
    """Identify circular dependencies with detailed analysis"""

    cycles = []

    # Find all strongly connected components
    scc_list = list(nx.strongly_connected_components(dependency_graph))

    for scc in scc_list:
        if len(scc) > 1:  # True circular dependency
            # Analyze the cycle structure
            subgraph = dependency_graph.subgraph(scc)
            cycle_info = {
                'components': list(scc),
                'cycle_length': len(scc),
                'complexity_score': calculate_cycle_complexity(subgraph)
            }

Strongly connected components reveal the structure of circular dependencies and their complexity impact.

            # Find the shortest cycle path for explanation
            try:
                first_node = list(scc)[0]
                cycle_path = nx.shortest_path(
                    subgraph,
                    first_node,
                    first_node
                )
                cycle_info['example_path'] = cycle_path
                cycle_info['path_relationships'] = [
                    dependency_graph[cycle_path[i]][cycle_path[i+1]]
                    for i in range(len(cycle_path)-1)
                ]
            except nx.NetworkXNoPath:
                cycle_info['example_path'] = None

            cycles.append(cycle_info)

    return sorted(cycles, key=lambda x: x['complexity_score'], reverse=True)

Advanced Code Pattern Recognition

Architectural Pattern Detection

Graph traversal can identify common architectural patterns in codebases:

def detect_architectural_patterns(code_graph):
    """Identify common architectural patterns through graph analysis"""

    patterns_detected = {}

    # Singleton pattern detection
    singletons = detect_singleton_pattern(code_graph)
    if singletons:
        patterns_detected['singleton'] = {
            'instances': singletons,
            'confidence': calculate_pattern_confidence(singletons, 'singleton')
        }

Pattern detection uses graph structure and code analysis to identify design patterns with confidence scores.

    # Factory pattern detection
    factories = detect_factory_pattern(code_graph)
    if factories:
        patterns_detected['factory'] = {
            'instances': factories,
            'variants': classify_factory_variants(factories),
            'complexity': assess_factory_complexity(factories)
        }

Factory pattern detection identifies different variants (Simple Factory, Factory Method, Abstract Factory) based on graph structure.

    # Observer pattern detection
    observers = detect_observer_pattern(code_graph)
    if observers:
        patterns_detected['observer'] = {
            'subjects': [obs['subject'] for obs in observers],
            'observers': [obs['observers'] for obs in observers],
            'event_flow': trace_event_propagation(observers, code_graph)
        }

    return patterns_detected

Singleton Pattern Detection Algorithm

def detect_singleton_pattern(code_graph):
    """Detect Singleton pattern through graph analysis"""

    potential_singletons = []

    for node_id, node_data in code_graph.nodes(data=True):
        if node_data.get('type') != 'class':
            continue

        class_info = node_data

        # Check for singleton characteristics
        singleton_indicators = {
            'private_constructor': False,
            'static_instance_method': False,
            'instance_storage': False,
            'thread_safety': False
        }

Singleton detection analyzes class structure for pattern-specific characteristics rather than relying on naming conventions.

        # Analyze class methods
        class_methods = get_class_methods(code_graph, node_id)

        for method in class_methods:
            method_data = code_graph.nodes[method]

            # Check for private constructor
            if (method_data.get('name') == '__init__' and
                method_data.get('visibility') == 'private'):
                singleton_indicators['private_constructor'] = True

            # Check for getInstance-style method
            if (method_data.get('is_static') and
                'instance' in method_data.get('name', '').lower()):
                singleton_indicators['static_instance_method'] = True

The analysis examines method characteristics to identify the structural elements of the Singleton pattern.

        # Check for instance storage (class-level variable)
        class_variables = get_class_variables(code_graph, node_id)
        for var in class_variables:
            if ('instance' in var.get('name', '').lower() and
                var.get('is_static')):
                singleton_indicators['instance_storage'] = True
                break

        # Calculate confidence score
        confidence = sum(singleton_indicators.values()) / len(singleton_indicators)

        if confidence >= 0.5:  # At least half the indicators present
            potential_singletons.append({
                'class_id': node_id,
                'class_name': class_info.get('name'),
                'confidence': confidence,
                'indicators': singleton_indicators,
                'thread_safety': analyze_thread_safety(code_graph, node_id)
            })

    return potential_singletons

Integration with Development Workflows

Git History Integration

Code GraphRAG can incorporate version control history for temporal analysis:

class GitIntegratedCodeAnalysis:
    """Integrate Git history with code graph analysis"""

    def __init__(self, repo_path):
        self.repo = git.Repo(repo_path)
        self.code_graph = nx.DiGraph()
        self.temporal_data = {}

Git integration enables analysis of code evolution, author contributions, and change impact assessment.

    def analyze_code_evolution(self, file_path, commit_range=None):
        """Analyze how code structure evolved over time"""

        if commit_range is None:
            commit_range = f"HEAD~10..HEAD"  # Last 10 commits

        commits = list(self.repo.iter_commits(commit_range, paths=file_path))
        evolution_data = []

        for commit in commits:
            try:
                # Get file content at this commit
                file_content = self.repo.git.show(f"{commit.hexsha}:{file_path}")

                # Analyze code structure at this point in time
                ast_analysis = self.analyze_file_ast(file_content)

                evolution_data.append({
                    'commit': commit.hexsha,
                    'timestamp': commit.committed_datetime,
                    'author': commit.author.name,
                    'message': commit.message.strip(),
                    'code_metrics': ast_analysis['metrics'],
                    'structure_changes': self.detect_structural_changes(
                        ast_analysis,
                        evolution_data[-1] if evolution_data else None
                    )
                })

            except Exception as e:
                continue  # Skip commits where file doesn't exist

        return evolution_data

Code Quality Assessment Through Graph Metrics

Graph metrics provide objective code quality indicators:

def assess_code_quality_through_graph(code_graph):
    """Calculate code quality metrics using graph analysis"""

    quality_metrics = {}

    # Coupling metrics
    quality_metrics['coupling'] = {
        'afferent_coupling': calculate_afferent_coupling(code_graph),
        'efferent_coupling': calculate_efferent_coupling(code_graph),
        'instability': calculate_instability_metric(code_graph)
    }

Coupling metrics assess how interconnected different code components are, indicating maintainability challenges.

    # Cohesion analysis
    quality_metrics['cohesion'] = {
        'module_cohesion': analyze_module_cohesion(code_graph),
        'class_cohesion': analyze_class_cohesion(code_graph),
        'functional_cohesion': assess_functional_cohesion(code_graph)
    }

Cohesion metrics evaluate how well-focused individual components are, indicating design quality.

    # Complexity indicators
    quality_metrics['complexity'] = {
        'structural_complexity': nx.density(code_graph),
        'cyclomatic_complexity': aggregate_cyclomatic_complexity(code_graph),
        'dependency_depth': calculate_max_dependency_depth(code_graph),
        'fan_out_complexity': analyze_fan_out_patterns(code_graph)
    }

    # Generate quality score
    quality_metrics['overall_score'] = calculate_composite_quality_score(
        quality_metrics
    )

    return quality_metrics

Advanced Query Patterns for Code Analysis

Multi-Hop Code Queries

GraphRAG enables sophisticated code queries that span multiple relationships:

def find_security_vulnerable_paths(code_graph, entry_points, sink_functions):
    """Find potential security vulnerability paths in code"""

    vulnerability_paths = []

    for entry_point in entry_points:
        for sink in sink_functions:
            try:
                # Find all paths from entry points to dangerous sinks
                paths = nx.all_simple_paths(
                    code_graph,
                    entry_point,
                    sink,
                    cutoff=8  # Maximum path length to prevent explosion
                )

                for path in paths:
                    path_analysis = analyze_security_path(code_graph, path)

                    if path_analysis['risk_score'] > 0.6:
                        vulnerability_paths.append({
                            'path': path,
                            'risk_score': path_analysis['risk_score'],
                            'vulnerabilities': path_analysis['vulnerabilities'],
                            'entry_point': entry_point,
                            'sink_function': sink,
                            'mitigation_suggestions': suggest_mitigations(
                                path_analysis
                            )
                        })

            except nx.NetworkXNoPath:
                continue  # No path exists

    return sorted(vulnerability_paths, key=lambda x: x['risk_score'], reverse=True)

API Usage Analysis

Track how APIs are used across the codebase through graph traversal:

def analyze_api_usage_patterns(code_graph, api_functions):
    """Analyze how specific APIs are used across the codebase"""

    usage_analysis = {}

    for api_func in api_functions:
        if api_func not in code_graph.nodes:
            continue

        # Find all callers of this API
        callers = list(code_graph.predecessors(api_func))

        usage_patterns = {
            'total_usage_count': len(callers),
            'usage_contexts': [],
            'parameter_patterns': {},
            'error_handling_analysis': {}
        }

API usage analysis helps understand how interfaces are consumed and identifies potential improvement opportunities.

        for caller in callers:
            caller_info = code_graph.nodes[caller]

            # Analyze the calling context
            context_analysis = {
                'caller_name': caller_info.get('name'),
                'caller_type': caller_info.get('type'),
                'call_frequency': estimate_call_frequency(
                    code_graph, caller, api_func
                ),
                'error_handling': has_error_handling(
                    code_graph, caller, api_func
                )
            }

            usage_patterns['usage_contexts'].append(context_analysis)

        # Aggregate pattern analysis
        usage_patterns['most_common_contexts'] = identify_common_contexts(
            usage_patterns['usage_contexts']
        )

        usage_patterns['error_handling_coverage'] = calculate_error_coverage(
            usage_patterns['usage_contexts']
        )

        usage_analysis[api_func] = usage_patterns

    return usage_analysis

Performance Optimization for Code Graphs

Incremental Graph Updates

Large codebases require efficient incremental updates rather than full rebuilds:

class IncrementalCodeGraphUpdater:
    """Efficiently update code graphs for modified files"""

    def __init__(self, base_graph):
        self.base_graph = base_graph
        self.change_tracker = {}
        self.dependency_cache = {}

Incremental updates maintain performance for large codebases by only reprocessing changed components and their direct dependencies.

    def update_for_modified_file(self, file_path, new_content):
        """Update graph for a single modified file"""

        # Identify existing nodes from this file
        existing_nodes = self.get_nodes_from_file(file_path)

        # Analyze the new file content
        new_analysis = self.analyze_file_content(file_path, new_content)

        # Remove obsolete nodes and edges
        self.remove_obsolete_elements(existing_nodes, new_analysis)

        # Add new nodes and edges
        self.add_new_elements(new_analysis)

        # Update affected dependencies
        self.update_dependent_relationships(file_path)

        # Invalidate affected caches
        self.invalidate_analysis_caches(file_path)

The incremental update process minimizes computational overhead while maintaining graph consistency and correctness.

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