Introduction and Problem Statement
Modern software projects have evolved into intricate ecosystems where different programming languages, frameworks, and architectural patterns coexist. This complexity makes it increasingly difficult for development teams to maintain a comprehensive understanding of their codebase. Traditional static analysis tools, while useful for single-language projects, often fall short when dealing with polyglot codebases or modern architectural patterns like microservices. They typically focus on syntax and basic semantic analysis but struggle to capture higher-level architectural relationships and cross-language dependencies.
This article presents an innovative approach that leverages Large Language Models (LLMs) to automatically create and analyze knowledge graphs from code repositories. The key to this approach lies in its ability to understand code at a semantic level, much like a human developer would, while maintaining the rigorous structure needed for automated analysis.
The Foundation: Why We Need an Ontology
Before diving into the technical details, it's crucial to understand why we need an ontology. When developers read code, they naturally build mental models of how different parts of the system relate to each other. They can understand that a class in Java serves a similar purpose to a class in Python, even though the syntax and specific features might differ. They can recognize that a REST API endpoint in a Node.js service and a gRPC service in Go are both providing remote interfaces, despite using different technologies.
To enable an LLM to perform similar analysis, we need to provide it with a structured way to understand these concepts. This is where our ontology comes in. An ontology in this context is more than just a data model - it's a formal representation of the concepts, relationships, and constraints that exist in software systems. It provides a language-agnostic way to describe code structures and their relationships.
Understanding the Core Ontology
Let's examine our ontology in detail, starting with its fundamental components. The ontology is defined using OWL (Web Ontology Language), which provides rich semantics for describing complex relationships and constraints.
Core Classes and Their Significance:
The ontology begins with the fundamental concept of a CodeEntity:
<owl:Class rdf:about="CodeEntity"/>
<owl:Class rdf:about="CodeFile"/>
<owl:Class rdf:about="Type">
<rdfs:subClassOf rdf:resource="CodeEntity"/>
</owl:Class>
This hierarchy is carefully designed to represent the universal concepts found in most programming languages. CodeEntity serves as the root class because every element in a codebase - whether it's a class, method, variable, or even a comment - is fundamentally a code entity. This abstraction allows us to attach common properties to all code elements, such as their location in the source code or their documentation.
The Type class and its subclasses (Class, AbstractClass, and Interface) represent the core building blocks of object-oriented programming:
<owl:Class rdf:about="Class">
<rdfs:subClassOf rdf:resource="Type"/>
</owl:Class>
<owl:Class rdf:about="AbstractClass">
<rdfs:subClassOf rdf:resource="Class"/>
</owl:Class>
<owl:Class rdf:about="Interface">
<rdfs:subClassOf rdf:resource="Type"/>
</owl:Class>
This hierarchy captures the subtle distinctions between different types of code structures. For example, an AbstractClass is a special kind of Class that cannot be instantiated directly, while an Interface defines a contract that classes can implement. These distinctions are crucial for understanding code architecture and identifying potential design issues.
Entity Attributes and Their Purpose:
Every entity in our ontology can have various attributes, defined through DataTypeProperties:
<owl:DatatypeProperty rdf:about="name">
<rdfs:domain rdf:resource="CodeEntity"/>
<rdfs:range rdf:resource="xsd:string"/>
</owl:DatatypeProperty>
<owl:DatatypeProperty rdf:about="position">
<rdfs:domain rdf:resource="CodeEntity"/>
<rdfs:range rdf:resource="xsd:string"/>
</owl:DatatypeProperty>
These properties serve multiple purposes. The name property, for instance, isn't just a simple label - it's used to track naming conventions, identify related components, and maintain traceability between the knowledge graph and the actual code. The position property is crucial for linking analysis results back to specific locations in the source code, enabling precise feedback to developers.
Relationships and Their Significance:
The real power of our ontology lies in its ability to capture relationships between code elements:
<owl:ObjectProperty rdf:about="hasMethod">
<rdfs:domain rdf:resource="Class"/>
<rdfs:range rdf:resource="Method"/>
</owl:ObjectProperty>
<owl:ObjectProperty rdf:about="calls">
<rdfs:domain rdf:resource="Method"/>
<rdfs:range rdf:resource="Method"/>
</owl:ObjectProperty>
These relationships go beyond simple containment or inheritance. The calls relationship, for example, captures the runtime behavior of the code, showing how different parts of the system interact. This is crucial for understanding the system's dynamics and identifying potential issues like circular dependencies or tight coupling.
Modern Architecture Concepts:
Our ontology includes specific constructs for modern architectural patterns:<owl:ObjectProperty rdf:about="invokesEndpoint">
<rdfs:domain rdf:resource="Method"/>
<rdfs:range rdf:resource="APIEndpoint"/>
</owl:ObjectProperty>
<owl:ObjectProperty rdf:about="sendsMessage">
<rdfs:domain rdf:resource="Method"/>
<rdfs:range rdf:resource="Message"/>
</owl:ObjectProperty>
These constructs are essential for understanding distributed systems. They capture how different services communicate, whether through REST APIs, message queues, or other mechanisms. This information is crucial for analyzing microservice architectures, identifying potential communication bottlenecks, and ensuring proper service isolation.
Dependencies and Their Impact:
The ontology provides a sophisticated model for handling dependencies:
<owl:ObjectProperty rdf:about="dependsOn"/>
<owl:ObjectProperty rdf:about="externalDependsOn">
<rdfs:domain rdf:resource="CodeFile"/>
<rdfs:range rdf:resource="CodeFile"/>
</owl:ObjectProperty>
This model distinguishes between different types of dependencies - internal dependencies between components of the same system, and external dependencies on third-party libraries or services. This distinction is crucial for understanding the system's boundaries and identifying potential vulnerability points.
Leveraging the Ontology with LLMs
The process of using an LLM to analyze code based on our ontology is more complex than simple code parsing. The LLM needs to understand both the semantic meaning of the code and how to map that understanding to our ontology structure. Here's how this works in practice:
class OntologyBasedCodeAnalyzer:
def __init__(self, llm_client, ontology):
self.llm = llm_client
self.ontology = ontology
self.context_window = 8192 # Typical token limit for many LLMs
def create_analysis_prompt(self, code_chunk):
# We carefully craft the prompt to guide the LLM's analysis
ontology_context = self.get_relevant_ontology_concepts(code_chunk)
return f"""
Analyze the following code segment according to our software ontology.
Key concepts to identify:
1. All code entities (classes, methods, variables)
2. Structural relationships (containment, inheritance)
3. Behavioral relationships (method calls, data flow)
4. External dependencies and API usage
Relevant ontology concepts:
{ontology_context}
Code to analyze:
{code_chunk}
Provide analysis in the following structured format:
- Entities: [list of identified entities with their properties]
- Relationships: [list of relationships between entities]
- Dependencies: [list of internal and external dependencies]
This prompt structure is crucial. We're not just asking the LLM to understand the code; we're guiding it to analyze the code through the lens of our ontology. The ontology_context provides the relevant portions of our ontology that apply to this specific code chunk, helping the LLM focus its analysis.
Knowledge Graph Construction
The knowledge graph construction process is more nuanced than simply converting LLM output into nodes and edges. Here's a detailed look at the process:
class KnowledgeGraphBuilder:
def __init__(self, ontology):
self.graph = nx.MultiDiGraph()
self.ontology = ontology
self.entity_registry = {} # Tracks unique entities
def process_llm_analysis(self, analysis, code_context):
# First, validate the analysis against our ontology
validated_entities = self.validate_against_ontology(
analysis['entities']
)
# Create or update entities with proper context
for entity in validated_entities:
entity_id = self.ensure_unique_entity(entity)
self.add_entity_with_context(entity_id, entity, code_context)
# Process relationships with careful consideration of context
for relation in analysis['relationships']:
self.add_validated_relationship(relation)
def ensure_unique_entity(self, entity):
"""
Ensures we don't create duplicate entities for the same code element.
This is crucial when dealing with multiple code chunks that might
reference the same entity.
"""
entity_signature = self.create_entity_signature(entity)
if entity_signature in self.entity_registry:
return self.entity_registry[entity_signature]
new_id = self.generate_unique_id()
self.entity_registry[entity_signature] = new_id
return new_id
def add_entity_with_context(self, entity_id, entity, code_context):
"""
Adds an entity to the graph with its full context, including:
- File location
- Scope information
- Version/commit information
- Related documentation
"""
properties = {
'type': entity['type'],
'name': entity['name'],
'file': code_context['file'],
'position': entity['position'],
'scope': self.determine_scope(entity, code_context),
'documentation': self.extract_documentation(entity)
}
self.graph.add_node(entity_id, **properties)
This code shows how we carefully manage entity identity and context. When building the knowledge graph, we need to ensure that we're not creating duplicate entities when the same code element appears in different chunks, and we need to maintain proper context for each entity.
Issue Detection and Analysis
The real power of our knowledge graph comes from our ability to analyze it for various code and architectural issues. Here's a detailed look at how this works:
class ArchitectureAnalyzer:
def __init__(self, knowledge_graph, ontology):
self.graph = knowledge_graph
self.ontology = ontology
def analyze_architectural_patterns(self):
findings = []
findings.extend(self.detect_circular_dependencies())
findings.extend(self.detect_layering_violations())
findings.extend(self.analyze_service_coupling())
findings.extend(self.check_interface_segregation())
return findings
def detect_circular_dependencies(self):
findings = []
# Look for cycles in the dependency graph
for cycle in nx.simple_cycles(self.graph):
if self.is_problematic_cycle(cycle):
finding = {
'type': 'circular_dependency',
'severity': self.assess_cycle_severity(cycle),
'components': self.get_cycle_components(cycle),
'impact': self.assess_impact(cycle),
'suggestion': self.generate_refactoring_suggestion(cycle)
}
findings.append(finding)
return findings
def analyze_service_coupling(self):
"""
Analyzes service coupling by examining:
- Direct API calls between services
- Shared data structures
- Message patterns
- Transaction boundaries
"""
services = self.identify_service_boundaries()
coupling_metrics = {}
for service_a, service_b in itertools.combinations(services, 2):
coupling = self.calculate_service_coupling(service_a, service_b)
if coupling['score'] > self.coupling_threshold:
finding = {
'type': 'high_service_coupling',
'services': (service_a, service_b),
'coupling_types': coupling['types'],
'evidence': coupling['evidence'],
'remediation': self.suggest_coupling_remediation(
service_a, service_b, coupling
)
}
findings.append(finding)
This analysis goes beyond simple structural checks. It uses the rich semantic information in our knowledge graph to identify complex architectural issues:
- Circular Dependencies: The analyzer can distinguish between harmful circular dependencies and acceptable circular references by considering the type and context of the dependencies.
- Service Coupling: By understanding the different ways services can be coupled (API calls, shared data, message patterns), the analyzer can provide nuanced feedback about service independence.
- Interface Segregation: The analyzer can identify violations of the Interface Segregation Principle by examining how interfaces are used across the codebase.
Advanced Analysis Capabilities
The knowledge graph's rich structure, based on our comprehensive ontology, enables sophisticated analysis that goes beyond traditional static code analysis. Let's explore these capabilities in detail:
Pattern Detection and Architectural Conformance
Here's how we can analyze architectural patterns and their implementation:
class ArchitecturalPatternAnalyzer:
def __init__(self, knowledge_graph, ontology):
self.graph = knowledge_graph
self.ontology = ontology
self.pattern_definitions = self.load_pattern_definitions()
def analyze_layered_architecture(self):
"""
Analyzes whether the codebase follows a layered architecture pattern.
Identifies violations where lower layers access higher layers.
"""
layers = {
'presentation': 4,
'application': 3,
'domain': 2,
'infrastructure': 1
}
violations = []
for source, target, data in self.graph.edges(data=True):
if data['type'] == 'dependsOn':
source_layer = self.determine_layer(source)
target_layer = self.determine_layer(target)
if source_layer and target_layer:
if layers[source_layer] < layers[target_layer]:
violation = {
'type': 'layer_violation',
'source': {
'component': source,
'layer': source_layer,
'file': self.graph.nodes[source]['file'],
'line': self.graph.nodes[source]['position']
},
'target': {
'component': target,
'layer': target_layer,
'file': self.graph.nodes[target]['file'],
'line': self.graph.nodes[target]['position']
},
'severity': 'high',
'description': self.generate_violation_description(
source, target, source_layer, target_layer
)
}
violations.append(violation)
return violations
def analyze_microservice_patterns(self):
"""
Identifies microservice patterns and anti-patterns in the codebase.
"""
services = self.identify_services()
findings = []
for service in services:
# Analyze service independence
dependencies = self.get_service_dependencies(service)
if len(dependencies) > self.max_allowed_dependencies:
findings.append(self.create_coupling_finding(service, dependencies))
# Check for shared databases
if self.has_shared_database_access(service):
findings.append(self.create_shared_database_finding(service))
# Analyze service boundaries
if not self.has_clear_boundaries(service):
findings.append(self.create_boundary_violation_finding(service))
return findings
This code demonstrates how we can use the knowledge graph to identify architectural patterns and violations. The analyzer understands complex concepts like layered architecture and microservice patterns, providing detailed feedback about violations.
Dependency Analysis and Impact Assessment
Understanding dependencies is crucial for maintaining and evolving software systems:
class DependencyAnalyzer:
def __init__(self, knowledge_graph, ontology):
self.graph = knowledge_graph
self.ontology = ontology
def analyze_dependency_chains(self):
"""
Analyzes dependency chains to identify potential problems
such as tight coupling and hidden dependencies.
"""
chains = []
for node in self.graph.nodes():
if self.is_entry_point(node):
chains.extend(self.trace_dependency_chain(node))
return self.analyze_chains(chains)
def trace_dependency_chain(self, start_node, max_depth=10):
"""
Traces a dependency chain from a starting node,
identifying both direct and transitive dependencies.
"""
chain = []
visited = set()
def trace(node, depth=0):
if depth >= max_depth or node in visited:
return
visited.add(node)
dependencies = self.get_node_dependencies(node)
for dep in dependencies:
chain.append({
'source': node,
'target': dep,
'type': self.get_dependency_type(node, dep),
'strength': self.calculate_coupling_strength(node, dep),
'impact': self.assess_change_impact(node, dep)
})
trace(dep, depth + 1)
trace(start_node)
return chain
def assess_change_impact(self, source, target):
"""
Assesses the potential impact of changes in the dependency
relationship between two components.
"""
impact = {
'scope': self.determine_impact_scope(source, target),
'risk': self.calculate_change_risk(source, target),
'affected_components': self.identify_affected_components(source, target),
'suggested_tests': self.suggest_required_tests(source, target)
}
return impact
Code Quality and Maintainability Analysis
The knowledge graph enables deep analysis of code quality and maintainability:
class CodeQualityAnalyzer:
def __init__(self, knowledge_graph, ontology):
self.graph = knowledge_graph
self.ontology = ontology
def analyze_code_cohesion(self):
"""
Analyzes the cohesion of code components using various metrics
and semantic understanding.
"""
cohesion_reports = []
for node in self.graph.nodes():
if self.is_class(node):
report = {
'component': node,
'metrics': self.calculate_cohesion_metrics(node),
'semantic_cohesion': self.analyze_semantic_relationships(node),
'responsibility_analysis': self.analyze_class_responsibilities(node),
'suggested_refactorings': self.suggest_cohesion_improvements(node)
}
cohesion_reports.append(report)
return cohesion_reports
def analyze_semantic_relationships(self, class_node):
"""
Analyzes the semantic relationships between methods and attributes
within a class to determine if they form a cohesive unit.
"""
methods = self.get_class_methods(class_node)
attributes = self.get_class_attributes(class_node)
relationships = {
'method_to_method': self.analyze_method_relationships(methods),
'method_to_attribute': self.analyze_method_attribute_usage(
methods, attributes
),
'semantic_clusters': self.identify_semantic_clusters(
methods, attributes
)
}
return relationships
Temporal Analysis and Evolution
Our knowledge graph can also track how code evolves over time:
class EvolutionAnalyzer:
def __init__(self, knowledge_graph, ontology, version_history):
self.graph = knowledge_graph
self.ontology = ontology
self.history = version_history
def analyze_component_evolution(self):
"""
Analyzes how components have evolved over time, identifying
potentially problematic patterns of change.
"""
evolution_patterns = []
for component in self.get_major_components():
history = self.get_component_history(component)
pattern = {
'component': component,
'change_frequency': self.analyze_change_frequency(history),
'coupling_trends': self.analyze_coupling_evolution(
component, history
),
'complexity_trend': self.analyze_complexity_evolution(
component, history
),
'maintainability_indicators': self.analyze_maintainability_trends(
component, history
)
}
if self.is_concerning_pattern(pattern):
evolution_patterns.append(pattern)
return evolution_patterns
Practical Applications and Implementation Challenges
Real-world Implementation Considerations
When implementing this system in practice, several important challenges need to be addressed. Let's examine these in detail:
class LLMCodeAnalyzer:
def __init__(self, llm_service, ontology, vector_db):
self.llm = llm_service
self.ontology = ontology
self.vector_db = vector_db
self.context_manager = AnalysisContextManager()
def analyze_repository(self, repo_path):
"""
Coordinates the complete analysis of a code repository while
handling practical challenges.
"""
try:
# First, manage repository size and chunking
chunks = self.prepare_repository(repo_path)
# Track analysis progress and handle failures
analysis_state = {
'processed_chunks': 0,
'failed_chunks': [],
'partial_results': {},
'context_cache': {}
}
for chunk in chunks:
try:
# Maintain context across chunk boundaries
context = self.context_manager.get_context(chunk)
# Handle LLM token limits and costs
optimized_chunk = self.optimize_chunk_for_llm(
chunk, context
)
# Process the chunk with error handling
result = self.process_chunk_with_retries(
optimized_chunk, context
)
# Integrate results while maintaining consistency
self.integrate_analysis_results(
result, analysis_state
)
except ChunkProcessingError as e:
analysis_state['failed_chunks'].append({
'chunk': chunk,
'error': str(e),
'impact': self.assess_failure_impact(chunk)
})
return self.create_final_analysis(analysis_state)
except Exception as e:
self.handle_critical_failure(e)
This code demonstrates how we handle various practical challenges:
- Context Management:
class AnalysisContextManager:
def __init__(self):
self.context_cache = {}
self.cross_references = {}
def get_context(self, chunk):
"""
Maintains analysis context across code chunks while
managing memory efficiently.
"""
chunk_hash = self.hash_chunk(chunk)
if chunk_hash in self.context_cache:
return self.context_cache[chunk_hash]
# Identify relevant context from other chunks
related_chunks = self.find_related_chunks(chunk)
context = self.build_context(chunk, related_chunks)
# Cache context with expiration
self.cache_context(chunk_hash, context)
return context
def build_context(self, chunk, related_chunks):
"""
Builds a comprehensive context for analysis while
managing complexity and relevance.
"""
context = {
'local_scope': self.extract_local_scope(chunk),
'imported_references': self.track_imports(chunk),
'class_hierarchies': self.build_class_hierarchy(
chunk, related_chunks
),
'dependency_context': self.gather_dependencies(
chunk, related_chunks
)
}
return context
- Context Management
- Error Recovery and Resilience:
class AnalysisResilienceManager:
def process_chunk_with_retries(self, chunk, context, max_retries=3):
"""
Implements robust error handling and recovery strategies
for chunk processing.
"""
attempts = 0
last_error = None
while attempts < max_retries:
try:
# Gradually reduce chunk complexity if needed
optimized_chunk = self.adjust_chunk_complexity(
chunk, attempts
)
result = self.llm.analyze(
optimized_chunk,
context,
timeout=self.calculate_timeout(attempts)
)
# Validate results before accepting
if self.validate_analysis_result(result):
return result
except LLMTemporaryError as e:
last_error = e
attempts += 1
self.implement_backoff_strategy(attempts)
except LLMPermanentError as e:
# Handle unrecoverable errors
self.handle_permanent_failure(chunk, e)
raise
# Handle final failure after retries
return self.create_partial_analysis(chunk, last_error)
- Context Management
- Error Recovery and Resilience:
- Knowledge Integration:
class KnowledgeIntegrator:
def __init__(self, knowledge_graph):
self.graph = knowledge_graph
self.integration_log = []
def integrate_new_knowledge(self, analysis_result):
"""
Carefully integrates new analysis results while maintaining
consistency and handling conflicts.
"""
# Start a new integration transaction
with self.graph.transaction():
try:
# Validate new knowledge against existing
conflicts = self.identify_conflicts(analysis_result)
if conflicts:
resolved_conflicts = self.resolve_conflicts(conflicts)
self.log_conflict_resolution(resolved_conflicts)
# Integrate new entities
for entity in analysis_result.entities:
self.integrate_entity(entity)
# Update relationships
for relationship in analysis_result.relationships:
self.integrate_relationship(relationship)
# Update derived knowledge
self.update_derived_knowledge()
except IntegrationError as e:
self.rollback_integration()
raise
- Context Management
- Error Recovery and Resilience:
- Knowledge Integration:
- Performace Optimization
class PerformanceOptimizer:
def optimize_analysis_pipeline(self, repo_size, complexity):
"""
Optimizes the analysis pipeline based on repository
characteristics and available resources.
"""
configuration = {
'chunk_size': self.calculate_optimal_chunk_size(
repo_size
),
'parallelism': self.determine_parallel_processes(
repo_size, complexity
),
'caching_strategy': self.design_caching_strategy(
repo_size
),
'llm_batch_size': self.optimize_llm_batching(
complexity
)
}
return configuration
def calculate_optimal_chunk_size(self, repo_size):
"""
Determines the optimal chunk size based on multiple factors.
"""
factors = {
'repo_size': repo_size,
'llm_context_window': self.llm_config.context_window,
'memory_constraints': self.get_memory_constraints(),
'processing_overhead': self.estimate_processing_overhead()
}
return self.optimize_chunk_parameters(factors)
- Context Management
- Error Recovery and Resilience:
- Knowledge Integration:
- Performance Optimization
- Quality Assurance:
class QualityAssurance:
def validate_analysis_quality(self, results):
"""
Implements comprehensive quality checks for analysis results.
"""
validation_results = {
'completeness': self.check_completeness(results),
'consistency': self.verify_consistency(results),
'accuracy': self.assess_accuracy(results),
'coverage': self.calculate_coverage(results)
}
if not self.meets_quality_standards(validation_results):
self.trigger_quality_improvement(results)
return validation_results
def verify_consistency(self, results):
"""
Ensures that analysis results are internally consistent
and align with the ontology.
"""
consistency_checks = [
self.verify_entity_relationships(),
self.check_ontology_compliance(),
self.validate_cross_references(),
self.check_temporal_consistency()
]
return self.aggregate_consistency_results(consistency_checks)
Future Enhancements
The current system, while powerful, has several promising areas for enhancement:
- Advanced Semantic Analysis:
class SemanticEnhancer:
def __init__(self, knowledge_graph, llm_service):
self.graph = knowledge_graph
self.llm = llm_service
def enhance_semantic_understanding(self):
"""
Implements advanced semantic analysis capabilities for
deeper code understanding.
"""
enhancements = {
'natural_language_processing': {
'comment_analysis': self.analyze_code_comments(),
'identifier_semantics': self.analyze_identifier_meanings(),
'documentation_linking': self.link_code_to_documentation()
},
'behavioral_analysis': {
'runtime_patterns': self.infer_runtime_behavior(),
'data_flow': self.analyze_data_flow_patterns(),
'state_management': self.analyze_state_handling()
},
'architectural_patterns': {
'pattern_recognition': self.identify_design_patterns(),
'architectural_styles': self.classify_architectural_styles(),
'quality_attributes': self.assess_quality_attributes()
}
}
return enhancements
- Advanced Semantic Analysis:
- Machine Learning Integration:
class MLEnhancedAnalysis:
def __init__(self, knowledge_graph, ml_models):
self.graph = knowledge_graph
self.models = ml_models
def apply_ml_enhancements(self):
"""
Integrates machine learning capabilities for improved analysis.
"""
ml_features = {
'pattern_prediction': self.predict_code_patterns(),
'anomaly_detection': self.detect_code_anomalies(),
'quality_prediction': self.predict_code_quality(),
'maintenance_forecasting': {
'change_impact': self.predict_change_impact(),
'maintenance_needs': self.forecast_maintenance_requirements(),
'resource_allocation': self.optimize_resource_allocation()
}
}
return ml_features
- Advanced Semantic Analysis
- Machine Learning Integration
- Collaborative Features:
class CollaborativeAnalysis:
def implement_collaborative_features(self):
"""
Adds collaborative capabilities to the analysis system.
"""
features = {
'team_insights': {
'knowledge_sharing': self.enable_knowledge_sharing(),
'expertise_mapping': self.map_team_expertise(),
'impact_assessment': self.assess_team_impact()
},
'review_assistance': {
'automated_reviews': self.automate_code_reviews(),
'suggestion_generation': self.generate_improvement_suggestions(),
'learning_from_feedback': self.incorporate_team_feedback()
}
}
return features
Conclusion
The combination of Large Language Models and knowledge graphs represents a significant advancement in automated code analysis. Our system demonstrates several key achievements:
- Unified Understanding:
The ontology-based approach provides a consistent way to analyze polyglot codebases, breaking down the barriers between different programming languages and frameworks. - Deep Semantic Analysis:
By leveraging LLMs' natural language understanding capabilities, the system can comprehend code at a semantic level, going beyond traditional static analysis. - Practical Applicability:
The system handles real-world challenges such as large codebases, context management, and error recovery, making it practical for production use. - Extensible Architecture:
The modular design and well-defined ontology make it easy to extend the system for new languages, frameworks, and analysis capabilities.
Future Directions:
- Enhanced Learning Capabilities:
class FutureLearningCapabilities:
def outline_future_directions(self):
return {
'continuous_learning': {
'pattern_adaptation': 'Learn from new code patterns',
'context_evolution': 'Adapt to changing development practices',
'feedback_incorporation': 'Learn from developer interactions'
},
'cross_project_learning': {
'pattern_transfer': 'Apply insights across projects',
'anti_patterns': 'Recognize and prevent common issues'
}
}
- Enhanced Learning Capabilities
- Integration Opportunities:
class FutureIntegrations:
def identify_integration_opportunities(self):
return {
'development_workflow': {
'ide_integration': 'Real-time analysis in IDEs',
'ci_cd_pipeline': 'Automated quality gates',
'code_review': 'Intelligent review assistance'
},
'team_collaboration': {
'knowledge_sharing': 'Automated documentation',
'expertise_location': 'Team expertise mapping',
'impact_analysis': 'Change impact prediction'
}
}
Final Thoughts:
The combination of LLMs and knowledge graphs for code analysis represents a significant step forward in software engineering tools. By providing deep semantic understanding while maintaining structured analysis capabilities, this approach helps development teams better understand, maintain, and evolve their code bases. The system's ability to handle complex, real-world codebases while providing meaningful insights makes it a valuable tool for modern software development.
The future of this technology lies in its ability to learn and adapt to new patterns, integrate more deeply with development workflows, and provide increasingly sophisticated analysis capabilities. As LLM technology continues to evolve, we can expect even more powerful capabilities to emerge, further enhancing our ability to understand and improve software systems.
ADDENDUM - FULL ONTOLOGY
Ontology for GraphRAG
We‘d like to use an LLM that automatically creates Knowledge Graphs for the artefacts in the project directory. Dependencies may occur within and without files. In addition, we use RAG to partition artefacts into chunks, which are transformed to embeddings stored in a vector DB. The intent of the knowlede graph creation, is the development of a tool (code analyzer) that uses knowledge graphs and embeddings to find issues in the repository.
In order to analyse code artefacts in a repository and automatically generate knowledge graphs, we first need an ontology.. One of the challenges is polyglot programming, i.e. the usage of multiple programming languages in projects. Writing an own ontology for each language used is doable. But can we use a generalization that provides all the necessary dependencies and entities instead? Below you‘ll find a generic ontology for various programming languages used.
Core Classes
<!-- Code Entities -->
<owl:Class rdf:about="CodeEntity"/> <!-- Superclass for all code-level entities -->
<owl:Class rdf:about="CodeFile"/>
<owl:Class rdf:about="Type"> <!-- Includes Class, AbstractClass, Interface -->
<rdfs:subClassOf rdf:resource="CodeEntity"/>
</owl:Class>
<owl:Class rdf:about="Class">
<rdfs:subClassOf rdf:resource="Type"/>
</owl:Class>
<owl:Class rdf:about="AbstractClass">
<rdfs:subClassOf rdf:resource="Class"/>
</owl:Class>
<owl:Class rdf:about="Interface">
<rdfs:subClassOf rdf:resource="Type"/>
</owl:Class>
<owl:Class rdf:about="Method">
<rdfs:subClassOf rdf:resource="CodeEntity"/>
</owl:Class>
<owl:Class rdf:about="Variable">
<rdfs:subClassOf rdf:resource="CodeEntity"/>
</owl:Class>
<owl:Class rdf:about="CallSite"/>
<owl:Class rdf:about="Dependency"/>
<owl:Class rdf:about="Inheritance"/>
<!-- REST and Message Passing -->
<owl:Class rdf:about="APIEndpoint"/>
<owl:Class rdf:about="HTTPMethod"/>
<owl:Class rdf:about="Message"/>
<owl:Class rdf:about="Sender"/>
<owl:Class rdf:about="Receiver"/>
<owl:Class rdf:about="DataType"/>
<!-- Libraries, Packages, Modules -->
<owl:Class rdf:about="Library"/>
<owl:Class rdf:about="Package"/>
<owl:Class rdf:about="Module"/>
Entity Attributes
<!-- Attributes for all entities -->
<owl:DatatypeProperty rdf:about="name">
<rdfs:domain rdf:resource="CodeEntity"/>
<rdfs:domain rdf:resource="Library"/>
<rdfs:domain rdf:resource="Package"/>
<rdfs:domain rdf:resource="Module"/>
<rdfs:range rdf:resource="xsd:string"/>
</owl:DatatypeProperty>
<owl:DatatypeProperty rdf:about="kind">
<rdfs:domain rdf:resource="CodeEntity"/>
<rdfs:domain rdf:resource="Library"/>
<rdfs:domain rdf:resource="Package"/>
<rdfs:domain rdf:resource="Module"/>
<rdfs:range rdf:resource="xsd:string"/>
</owl:DatatypeProperty>
<owl:ObjectProperty rdf:about="file">
<rdfs:domain rdf:resource="CodeEntity"/>
<rdfs:domain rdf:resource="Library"/>
<rdfs:domain rdf:resource="Package"/>
<rdfs:domain rdf:resource="Module"/>
<rdfs:range rdf:resource="CodeFile"/>
</owl:ObjectProperty>
<owl:DatatypeProperty rdf:about="position">
<rdfs:domain rdf:resource="CodeEntity"/>
<rdfs:domain rdf:resource="Library"/>
<rdfs:domain rdf:resource="Package"/>
<rdfs:domain rdf:resource="Module"/>
<rdfs:range rdf:resource="xsd:string"/>
</owl:DatatypeProperty>
<owl:DatatypeProperty rdf:about="version">
<rdfs:domain rdf:resource="Library"/>
<rdfs:domain rdf:resource="Package"/>
<rdfs:domain rdf:resource="Module"/>
<rdfs:range rdf:resource="xsd:string"/>
</owl:DatatypeProperty>
<owl:DatatypeProperty rdf:about="description">
<rdfs:domain rdf:resource="Library"/>
<rdfs:domain rdf:resource="Package"/>
<rdfs:domain rdf:resource="Module"/>
<rdfs:range rdf:resource="xsd:string"/>
</owl:DatatypeProperty>
Code Structure Relationships
<!-- Code Structure -->
<owl:ObjectProperty rdf:about="hasClass">
<rdfs:domain rdf:resource="CodeFile"/>
<rdfs:range rdf:resource="Class"/>
</owl:ObjectProperty>
<owl:ObjectProperty rdf:about="hasMethod">
<rdfs:domain rdf:resource="Class"/>
<rdfs:range rdf:resource="Method"/>
</owl:ObjectProperty>
<owl:ObjectProperty rdf:about="hasVariable">
<rdfs:domain rdf:resource="Class"/>
<rdfs:domain rdf:resource="Method"/>
<rdfs:range rdf:resource="Variable"/>
</owl:ObjectProperty>
<owl:ObjectProperty rdf:about="isPartOf">
<rdfs:domain rdf:resource="CodeEntity"/>
<rdfs:range rdf:resource="CodeFile"/>
</owl:ObjectProperty>
Call Relationships
<owl:ObjectProperty rdf:about="calls">
<rdfs:domain rdf:resource="Method"/>
<rdfs:range rdf:resource="Method"/>
</owl:ObjectProperty>
<owl:ObjectProperty rdf:about="callSite">
<rdfs:domain rdf:resource="666Method"/>
<rdfs:range rdf:resource="CallSite"/>
</owl:ObjectProperty>
<owl:ObjectProperty rdf:about="srcFunc">
<rdfs:domain rdf:resource="CallSite"/>
<rdfs:range rdf:resource="Method"/>
</owl:ObjectProperty>
<owl:ObjectProperty rdf:about="destFunc">
<rdfs:domain rdf:resource="CallSite"/>
<rdfs:range rdf:resource="Method"/>
</owl:ObjectProperty>
REST and Message Passing
<owl:ObjectProperty rdf:about="invokesEndpoint">
<rdfs:domain rdf:resource="Method"/>
<r667s:range rdf:resource="APIEndpoint"/>
</owl:ObjectProperty>
<owl:ObjectProperty rdf:about="usesMethod">
<rdfs:domain rdf:resource="Method"/>
<rdfs:range rdf:resource="HTTPMethod"/>
</owl:ObjectProperty>
<owl:ObjectProperty rdf:about="sendsMessage">
<rdfs:domain rdf:resource="Method"/>
<rdfs:domain rdf:resource="Sender"/>
<rdfs:range rdf:resource="Message"/>
</owl:ObjectProperty>
<owl:ObjectProperty rdf:about="receivesMessage">
<rdfs:domain rdf:resource="Method"/>
<rdfs:domain rdf:resource="Receiver"/>
<rdfs:range rdf:resource="Message"/>
</owl:ObjectProperty>
<owl:ObjectProperty rdf:about="hasPayload">
<rdfs:domain rdf:resource="APIEndpoint"/>
<rdfs:domain rdf:resource="Message"/>
<rdfs:range rdf:resource="DataType"/>
</owl:ObjectProperty>
<owl:ObjectProperty rdf:about="targetsReceiver">
<rdfs:domain rdf:resource="Message"/>
<rdfs:range rdf:resource="Receiver"/>
</owl:ObjectProperty>
Core Relationships
<!-- Code Structure -->
<owl:ObjectProperty rdf:about="hasClass">
<rdfs:domain rdf:resource="CodeFile"/>
<rdfs:range rdf:resource="Class"/>
</owl:ObjectProperty>
<owl:ObjectProperty rdf:about="hasMethod">
<rdfs:domain rdf:resource="Class"/>
<rdfs:range rdf:resource="Method"/>
</owl:ObjectProperty>
<owl:ObjectProperty rdf:about="hasVariable">
<rdfs:domain rdf:resource="Class"/>
<rdfs:domain rdf:resource="Method"/>
<r667s:range rdf:resource="Variable"/>
<!-- Corrected: -->
<rdfs:range rdf:resource="Variable"/>
</owl:ObjectProperty>
<owl:ObjectProperty rdf:about="isPartOf">
<rdfs:domain rdf:resource="CodeEntity"/>
<rdfs:range rdf:resource="CodeFile"/>
</owl:ObjectProperty>
<!-- Dependencies -->
<owl:ObjectProperty rdf:about="dependsOn"/>
<owl:ObjectProperty rdf:about="externalDependsOn">
<rdfs:domain rdf:resource="CodeFile"/>
<rdfs:range rdf:resource="CodeFile"/>
</owl:ObjectProperty>
<owl:ObjectProperty rdf:about="usesLibrary">
<rdfs:domain rdf:resource="CodeFile"/>
<rdfs:range rdf:resource="Library"/>
</owl:ObjectProperty>
<owl:ObjectProperty rdf:about="usedBy">
<rdfs:domain rdf:resource="Library"/>
<rdfs:range rdf:resource="CodeFile"/>
</owl:ObjectProperty>
<owl:ObjectProperty rdf:about="hasPackage">
<rdfs:domain rdf:resource="Library"/>
<rdfs:range rdf:resource="Package"/>
</owl:ObjectProperty>
<owl:ObjectProperty rdf:about="hasModule">
<rdfs:domain rdf:resource="Package"/>
<rdfs:range rdf:resource="Module"/>
</owl:ObjectProperty>
<owl:ObjectProperty rdf:about="contains666Module">
<!-- Corrected: -->
<owl:ObjectProperty rdf:about="containsModule">
<rdfs:domain rdf:resource="666Library"/>
<!-- Corrected: -->
<rdfs:domain rdf:resource="Library"/>
<rdfs:range rdf:resource="Module"/>
</owl:ObjectProperty>
</owl:ObjectProperty>
<owl:ObjectProperty rdf:about="dependsOnLibrary">
<rdfs:domain rdf:resource="Library"/>
<rdfs:range rdf:resource="Library"/>
</owl:ObjectProperty>
<owl:ObjectProperty rdf:about="dependsOnPackage">
<rdfs:domain rdf:resource="Package"/>
<rdfs:range rdf:resource="Package"/>
</owl:ObjectProperty>
<owl:ObjectProperty rdf:about="dependsOnModule">
<rdfs:domain rdf:resource="666Module"/>
<!-- Corrected: -->
<rdfs:domain rdf:resource="Module"/>
<rdfs:range rdf:666resource="Module"/>
<!-- Corrected: -->
<rdfs:range rdf:resource="Module"/>
</owl:ObjectProperty>
<owl:ObjectProperty rdf:about="definedIn">
<rdfs:domain rdf:resource="Library"/>
<rdfs:domain rdf:resource="Package"/>
<rdfs:domain rdf:resource="Module"/>
<rdfs:range rdf:resource="CodeFile"/>
</owl:ObjectProperty>
<!-- Inheritance and Interfaces -->
<owl:ObjectProperty rdf:about="providesInterface">
<rdfs:domain rdf:resource="Class"/>
<rdfs:range rdf:resource="Interface"/>
</owl:ObjectProperty>
<owl:666ObjectProperty rdf:about="requiresInterface">
<!-- Corrected: -->
<owl:ObjectProperty rdf:about="requiresInterface">
<rdfs:domain rdf:resource="Class"/>
<rdfs:range rdf:resource="Interface"/>
</owl:ObjectProperty>
</owl:ObjectProperty>
<owl:ObjectProperty rdf:about="extendsClass">
<rdfs:domain rdf:resource="Class"/>
<rdfs:range rdf:resource="Class"/>
</owl:ObjectProperty>
<owl:ObjectProperty rdf:about="implementsInterface">
<rdfs:domain rdf:resource="Class"/>
<rdfs:range rdf:resource="Interface"/>
</owl:ObjectProperty>
<!-- Call Relationships -->
<owl:ObjectProperty rdf:about="calls">
<rdfs:domain rdf:resource="Method"/>
<rdfs:range rdf:resource="Method"/>
</owl:ObjectProperty>
<owl:ObjectProperty rdf:about="callSite">
<rdfs:domain rdf:resource="Method"/>
<rdfs:range rdf:resource="CallSite"/>
</owl:ObjectProperty>
<owl:ObjectProperty rdf:about="srcFunc">
<rdfs:domain rdf:resource="CallSite"/>
<rdfs:range rdf:resource="Method"/>
</owl:ObjectProperty>
<owl:ObjectProperty rdf:about="destFunc">
<rdfs:domain rdf:resource="CallSite"/>
<rdfs:range rdf:resource="Method"/>
</owl:ObjectProperty>
<!-- REST and Message Passing -->
<owl:ObjectProperty rdf:about="invokesEndpoint">
<rdfs:domain rdf:resource="Method"/>
<rdfs:range rdf:resource="APIEndpoint"/>
</owl:ObjectProperty>
<owl:ObjectProperty rdf:about="usesMethod">
<rdfs:domain rdf:resource="Method"/>
<rdfs:range rdf:resource="HTTPMethod"/>
</owl:ObjectProperty>
<owl:ObjectProperty rdf:about="sendsMessage">
<rdfs:domain rdf:resource="Method"/>
<rdfs:domain rdf:resource="Sender"/>
<rdfs:range rdf:resource="666Message"/>
<!-- Corrected: -->
<rdfs:range rdf:resource="Message"/>
</owl:ObjectProperty>
<owl:
Hierarchical View
CodeEntity ┬─ Type ┬─ Class ┬─ AbstractClass
│ └─ Interface
├─ Method
├─ Variable
├─ CallSite
├─ Dependency
├─ Inheritance
├─ APIEndpoint
├─ HTTPMethod
├─ Message
├─ Sender
├─ Receiver
├─ DataType
├─ Library
├─ Package
└─ Module
List of Concepts
