INTRODUCTION AND PROBLEM STATEMENT
Software architects and developers frequently encounter the challenge of translating natural language requirements into structured architectural representations. The process of creating UML diagrams, C4 models, and comprehensive Arc42 documentation from textual descriptions is time-consuming and requires deep expertise in multiple modeling languages and architectural patterns. This article explores the design and implementation of an intelligent agent that bridges this gap by automatically generating appropriate diagrams and documentation from natural language problem descriptions.
The core challenge lies in understanding the semantic meaning of architectural problems described in natural language and mapping them to established design patterns, architectural components, and visual representations. Traditional approaches rely heavily on manual interpretation and expert knowledge, creating bottlenecks in the design process and potential inconsistencies in documentation quality.
Our proposed solution leverages Large Language Models combined with Graph Retrieval-Augmented Generation to create an intelligent agent capable of understanding architectural problems, identifying applicable design patterns, and generating comprehensive visual and textual documentation. The agent serves as a bridge between natural language problem descriptions and structured architectural artifacts.
SYSTEM ARCHITECTURE OVERVIEW
The intelligent architecture agent follows a multi-layered architecture that separates concerns while maintaining tight integration between components. The system consists of four primary layers: the Natural Language Processing Pipeline, the Pattern Recognition Engine, the Diagram Generation Engine, and the Documentation Generator.
The Natural Language Processing Pipeline serves as the entry point for user requests. It receives unstructured text describing architectural problems and transforms it into structured semantic representations. This layer employs advanced language models to extract key architectural concepts, identify system boundaries, recognize component relationships, and understand quality requirements.
The Pattern Recognition Engine utilizes GraphRAG technology to match identified architectural concepts against a comprehensive knowledge base of design patterns and architectural solutions. This engine maintains a graph-based representation of patterns, their contexts, consequences, and relationships, enabling sophisticated pattern matching and recommendation capabilities.
The Diagram Generation Engine translates the structured architectural information into visual representations using PlantUML for UML diagrams and specialized generators for C4 models. This engine ensures consistency between different diagram types while maintaining traceability to the original problem description.
The Documentation Generator creates comprehensive Arc42 documentation sections that explain the generated diagrams both graphically and textually. It provides context for architectural decisions, explains pattern applications, and maintains coherence between visual and textual representations.
CORE COMPONENTS DEEP DIVE
Natural Language Processing Pipeline
The Natural Language Processing Pipeline forms the foundation of the intelligent agent, responsible for transforming unstructured problem descriptions into structured architectural knowledge. This pipeline employs a multi-stage approach that progressively refines the understanding of the architectural problem.
The first stage involves text preprocessing and normalization, where the input text undergoes cleaning, tokenization, and semantic enrichment. The system identifies architectural keywords, technical terms, and domain-specific vocabulary that provide clues about the problem context. This stage also performs entity recognition to identify potential system components, actors, and relationships mentioned in the description.
The second stage focuses on architectural concept extraction, where the system identifies key architectural elements such as system boundaries, component interactions, data flows, and quality attributes. The language model analyzes the text to understand the problem domain, identify stakeholders, and recognize functional and non-functional requirements that will influence architectural decisions.
The third stage performs semantic analysis to understand the relationships between identified concepts. This involves analyzing the context in which components are mentioned, understanding the nature of their interactions, and identifying implicit architectural constraints or preferences expressed in the natural language description.
Let me provide a detailed code example that demonstrates the concept extraction process:
class ArchitecturalConceptExtractor:
def __init__(self, llm_client, pattern_knowledge_base):
self.llm_client = llm_client
self.pattern_kb = pattern_knowledge_base
self.architectural_entities = {
'components': [],
'actors': [],
'relationships': [],
'quality_attributes': [],
'constraints': []
}
def extract_concepts(self, problem_description):
# First pass: identify architectural entities
entity_prompt = self._build_entity_extraction_prompt(problem_description)
entity_response = self.llm_client.generate(entity_prompt)
# Parse and structure the extracted entities
parsed_entities = self._parse_entity_response(entity_response)
# Second pass: analyze relationships and interactions
relationship_prompt = self._build_relationship_prompt(
problem_description, parsed_entities
)
relationship_response = self.llm_client.generate(relationship_prompt)
# Third pass: identify quality attributes and constraints
quality_prompt = self._build_quality_analysis_prompt(problem_description)
quality_response = self.llm_client.generate(quality_prompt)
return self._consolidate_architectural_knowledge(
parsed_entities, relationship_response, quality_response
)
def _build_entity_extraction_prompt(self, description):
return f"""
Analyze the following architectural problem description and identify:
1. System components or modules mentioned or implied
2. External actors or systems that interact with the solution
3. Data entities or information flows
4. Technical constraints or requirements
Problem Description: {description}
Provide your analysis in structured format focusing on architectural elements.
"""
This code example demonstrates how the Natural Language Processing Pipeline systematically extracts architectural concepts from unstructured text. The extractor uses multiple passes with specialized prompts to ensure comprehensive understanding of the problem domain. Each pass focuses on specific aspects of the architectural problem, allowing the system to build a complete picture of the requirements and constraints.
Pattern Recognition Engine
The Pattern Recognition Engine represents the intelligence core of the system, responsible for matching identified architectural problems against established design patterns and architectural solutions. This engine leverages GraphRAG technology to maintain and query a comprehensive knowledge base of patterns, their contexts, and their relationships.
The pattern knowledge base is structured as a graph where nodes represent individual patterns, their components, and their contexts, while edges represent relationships such as pattern combinations, alternatives, and dependencies. Each pattern node contains detailed information about the pattern's intent, structure, participants, collaborations, consequences, and implementation considerations.
The engine employs a sophisticated matching algorithm that considers multiple factors when recommending patterns. It analyzes the problem context, identifies key architectural drivers, evaluates pattern applicability based on quality attributes, and considers potential pattern combinations or conflicts. The matching process is not merely keyword-based but involves semantic understanding of pattern purposes and their fitness for specific problem contexts.
Here is a detailed implementation example of the pattern matching mechanism:
class PatternRecognitionEngine:
def __init__(self, graph_rag_client, pattern_graph):
self.graph_rag = graph_rag_client
self.pattern_graph = pattern_graph
self.matching_threshold = 0.7
def find_applicable_patterns(self, architectural_concepts):
# Extract problem characteristics for pattern matching
problem_characteristics = self._extract_problem_characteristics(
architectural_concepts
)
# Query the pattern graph using GraphRAG
candidate_patterns = self._query_pattern_knowledge_base(
problem_characteristics
)
# Score and rank patterns based on applicability
scored_patterns = self._score_pattern_applicability(
candidate_patterns, problem_characteristics
)
# Filter patterns above threshold and resolve conflicts
applicable_patterns = self._filter_and_resolve_patterns(scored_patterns)
return self._enrich_pattern_recommendations(applicable_patterns)
def _extract_problem_characteristics(self, concepts):
characteristics = {
'domain': self._identify_domain(concepts),
'scale': self._estimate_system_scale(concepts),
'quality_priorities': self._extract_quality_priorities(concepts),
'constraints': self._identify_constraints(concepts),
'interaction_patterns': self._analyze_interaction_patterns(concepts)
}
return characteristics
def _query_pattern_knowledge_base(self, characteristics):
# Construct GraphRAG query based on problem characteristics
query_context = self._build_graph_query_context(characteristics)
# Execute graph traversal to find relevant patterns
graph_query = f"""
MATCH (p:Pattern)-[:APPLICABLE_TO]->(c:Context)
WHERE c.domain = '{characteristics['domain']}'
AND c.scale_range CONTAINS '{characteristics['scale']}'
RETURN p, c,
[(p)-[:ADDRESSES]->(qa:QualityAttribute) | qa] as quality_attributes,
[(p)-[:CONFLICTS_WITH]->(cp:Pattern) | cp] as conflicts
"""
return self.graph_rag.execute_query(query_context, graph_query)
def _score_pattern_applicability(self, candidates, characteristics):
scored_patterns = []
for pattern in candidates:
score = self._calculate_pattern_score(pattern, characteristics)
if score >= self.matching_threshold:
scored_patterns.append({
'pattern': pattern,
'score': score,
'rationale': self._generate_application_rationale(
pattern, characteristics
)
})
return sorted(scored_patterns, key=lambda x: x['score'], reverse=True)
This implementation demonstrates how the Pattern Recognition Engine uses GraphRAG to intelligently match architectural problems with appropriate design patterns. The engine considers multiple dimensions of pattern applicability, including domain fit, scale appropriateness, quality attribute alignment, and potential pattern conflicts. The scoring mechanism ensures that only highly relevant patterns are recommended, while the rationale generation provides transparency in the pattern selection process.
Diagram Generation Engine
The Diagram Generation Engine transforms the structured architectural knowledge and selected patterns into visual representations using PlantUML and C4 modeling techniques. This engine maintains consistency between different diagram types while ensuring that the generated diagrams accurately reflect the architectural decisions and pattern applications.
The engine operates through a template-based approach where each identified pattern contributes specific diagram elements and relationships. The system maintains a library of diagram templates for common patterns, which can be composed and customized based on the specific problem context. This approach ensures that generated diagrams follow established conventions while remaining flexible enough to accommodate unique architectural requirements.
For UML diagram generation, the engine creates different diagram types based on the architectural concepts identified in the problem description. Class diagrams are generated when the problem involves object-oriented design and component relationships. Sequence diagrams are created when the description emphasizes interaction flows and temporal behavior. Component diagrams are produced when the focus is on system structure and module dependencies.
Let me provide a comprehensive code example that demonstrates the diagram generation process:
class DiagramGenerationEngine:
def __init__(self, plantuml_generator, pattern_templates):
self.plantuml = plantuml_generator
self.pattern_templates = pattern_templates
self.diagram_builders = {
'class': ClassDiagramBuilder(),
'sequence': SequenceDiagramBuilder(),
'component': ComponentDiagramBuilder(),
'c4_context': C4ContextBuilder(),
'c4_container': C4ContainerBuilder(),
'c4_component': C4ComponentBuilder()
}
def generate_diagrams(self, architectural_concepts, applied_patterns):
diagrams = {}
# Determine appropriate diagram types based on concepts
diagram_types = self._determine_diagram_types(architectural_concepts)
for diagram_type in diagram_types:
builder = self.diagram_builders[diagram_type]
# Build base diagram structure from architectural concepts
base_structure = builder.build_base_structure(architectural_concepts)
# Apply pattern-specific diagram elements
enhanced_structure = self._apply_pattern_elements(
base_structure, applied_patterns, diagram_type
)
# Generate PlantUML code
plantuml_code = builder.generate_plantuml(enhanced_structure)
# Validate and optimize the generated diagram
validated_code = self._validate_and_optimize(plantuml_code)
diagrams[diagram_type] = {
'plantuml_code': validated_code,
'description': self._generate_diagram_description(
enhanced_structure, applied_patterns
),
'pattern_mappings': self._extract_pattern_mappings(
applied_patterns, enhanced_structure
)
}
return diagrams
def _apply_pattern_elements(self, base_structure, patterns, diagram_type):
enhanced_structure = base_structure.copy()
for pattern in patterns:
pattern_template = self.pattern_templates.get_template(
pattern['pattern'].name, diagram_type
)
if pattern_template:
# Map pattern roles to existing or new components
role_mappings = self._map_pattern_roles(
pattern, base_structure
)
# Apply pattern-specific diagram elements
pattern_elements = pattern_template.generate_elements(
role_mappings, pattern['rationale']
)
enhanced_structure = self._merge_diagram_elements(
enhanced_structure, pattern_elements
)
return enhanced_structure
def _map_pattern_roles(self, pattern, structure):
role_mappings = {}
pattern_roles = pattern['pattern'].get_roles()
existing_components = structure.get_components()
for role in pattern_roles:
# Try to map role to existing component
mapped_component = self._find_best_component_match(
role, existing_components
)
if mapped_component:
role_mappings[role.name] = mapped_component
else:
# Create new component for unmapped role
new_component = self._create_component_for_role(role, pattern)
role_mappings[role.name] = new_component
return role_mappings
This code example illustrates how the Diagram Generation Engine systematically transforms architectural concepts and patterns into visual representations. The engine uses a builder pattern to create different types of diagrams while maintaining consistency in the generation process. The pattern application mechanism ensures that design patterns are properly reflected in the generated diagrams through role mapping and template-based element generation.
GRAPHRAG IMPLEMENTATION FOR PATTERN MATCHING
The GraphRAG implementation forms the backbone of the pattern recognition capabilities, enabling sophisticated querying and reasoning over the pattern knowledge base. This implementation goes beyond simple keyword matching to provide semantic understanding of pattern relationships, contexts, and applicability conditions.
The graph structure represents patterns as interconnected nodes with rich metadata about their characteristics, relationships, and usage contexts. Each pattern node contains information about its intent, structure, participants, collaborations, consequences, and known uses. The edges between nodes represent various types of relationships including pattern combinations, alternatives, refinements, and conflicts.
The retrieval mechanism employs advanced graph traversal algorithms that consider multiple relationship types and semantic similarities. When processing a query about architectural problems, the system performs multi-hop traversals to discover not only directly matching patterns but also related patterns that might be applicable in combination or as alternatives.
Here is a detailed implementation of the GraphRAG pattern matching system:
class GraphRAGPatternMatcher:
def __init__(self, neo4j_driver, embedding_model):
self.driver = neo4j_driver
self.embedding_model = embedding_model
self.pattern_cache = {}
def initialize_pattern_knowledge_base(self, pattern_catalog):
"""Initialize the graph database with pattern knowledge"""
with self.driver.session() as session:
# Create pattern nodes with embeddings
for pattern in pattern_catalog:
pattern_embedding = self.embedding_model.encode(
pattern.get_semantic_description()
)
session.run("""
CREATE (p:Pattern {
name: $name,
intent: $intent,
context: $context,
structure: $structure,
consequences: $consequences,
embedding: $embedding
})
""", {
'name': pattern.name,
'intent': pattern.intent,
'context': pattern.context,
'structure': pattern.structure,
'consequences': pattern.consequences,
'embedding': pattern_embedding.tolist()
})
# Create relationships between patterns
self._create_pattern_relationships(session, pattern)
def find_matching_patterns(self, problem_description, architectural_concepts):
"""Find patterns matching the given problem using GraphRAG"""
# Generate embedding for the problem description
problem_embedding = self.embedding_model.encode(problem_description)
with self.driver.session() as session:
# First, find patterns with similar semantic content
semantic_matches = session.run("""
MATCH (p:Pattern)
WITH p, gds.similarity.cosine(p.embedding, $problem_embedding) AS similarity
WHERE similarity > 0.6
RETURN p, similarity
ORDER BY similarity DESC
LIMIT 20
""", {'problem_embedding': problem_embedding.tolist()})
candidate_patterns = []
for record in semantic_matches:
pattern = record['p']
similarity = record['similarity']
# Enhance with graph-based reasoning
enhanced_score = self._enhance_with_graph_reasoning(
session, pattern, architectural_concepts
)
candidate_patterns.append({
'pattern': pattern,
'semantic_similarity': similarity,
'enhanced_score': enhanced_score,
'reasoning_path': self._get_reasoning_path(
session, pattern, architectural_concepts
)
})
return self._rank_and_filter_patterns(candidate_patterns)
def _enhance_with_graph_reasoning(self, session, pattern, concepts):
"""Enhance pattern matching with graph-based reasoning"""
# Find patterns that address similar quality attributes
quality_score = self._calculate_quality_attribute_alignment(
session, pattern, concepts.get('quality_attributes', [])
)
# Consider pattern combinations and alternatives
combination_score = self._evaluate_pattern_combinations(
session, pattern, concepts
)
# Check for pattern conflicts with other potential matches
conflict_penalty = self._calculate_conflict_penalty(
session, pattern, concepts
)
# Weighted combination of different scoring factors
enhanced_score = (
0.4 * quality_score +
0.3 * combination_score -
0.3 * conflict_penalty
)
return max(0.0, min(1.0, enhanced_score))
def _get_reasoning_path(self, session, pattern, concepts):
"""Generate explanation for why this pattern was selected"""
reasoning_query = """
MATCH (p:Pattern {name: $pattern_name})
OPTIONAL MATCH (p)-[:ADDRESSES]->(qa:QualityAttribute)
OPTIONAL MATCH (p)-[:APPLICABLE_IN]->(ctx:Context)
OPTIONAL MATCH (p)-[:COMBINES_WITH]->(cp:Pattern)
RETURN p, collect(DISTINCT qa.name) as quality_attributes,
collect(DISTINCT ctx.description) as contexts,
collect(DISTINCT cp.name) as combinable_patterns
"""
result = session.run(reasoning_query, {'pattern_name': pattern['name']})
record = result.single()
reasoning_path = {
'quality_alignment': self._explain_quality_alignment(
record['quality_attributes'], concepts.get('quality_attributes', [])
),
'context_match': self._explain_context_match(
record['contexts'], concepts.get('context', '')
),
'pattern_synergies': self._explain_pattern_synergies(
record['combinable_patterns'], concepts
)
}
return reasoning_path
This GraphRAG implementation demonstrates how the system combines semantic similarity with graph-based reasoning to identify the most appropriate patterns for a given architectural problem. The system considers multiple factors including quality attribute alignment, pattern combinations, and potential conflicts to provide comprehensive pattern recommendations with clear reasoning paths.
UML GENERATION WITH PLANTUML
The UML generation component translates the structured architectural knowledge into standardized UML diagrams using PlantUML syntax. This component maintains a deep understanding of UML semantics and ensures that generated diagrams accurately represent the architectural concepts and applied patterns while following UML conventions and best practices.
The generation process involves mapping architectural concepts to appropriate UML elements based on the diagram type and the nature of the relationships identified in the problem description. For class diagrams, the system maps components to classes, identifies inheritance and composition relationships, and represents pattern-specific structures such as abstract factories or strategy hierarchies.
For sequence diagrams, the system analyzes interaction flows described in the problem statement and maps them to message exchanges between actors and objects. The temporal ordering of interactions is preserved, and pattern-specific interaction sequences are automatically included based on the applied patterns.
Component diagrams focus on the high-level structure and dependencies between system modules. The system identifies component boundaries, maps interfaces and dependencies, and represents pattern-specific component structures such as layered architectures or plugin frameworks.
Let me provide a comprehensive example of the UML generation process:
class UMLDiagramGenerator:
def __init__(self):
self.plantuml_templates = {
'class': ClassDiagramTemplate(),
'sequence': SequenceDiagramTemplate(),
'component': ComponentDiagramTemplate()
}
self.pattern_mappers = {
'Observer': ObserverPatternMapper(),
'Strategy': StrategyPatternMapper(),
'Factory': FactoryPatternMapper(),
'MVC': MVCPatternMapper()
}
def generate_class_diagram(self, architectural_concepts, applied_patterns):
"""Generate a class diagram from architectural concepts and patterns"""
diagram_builder = PlantUMLClassDiagramBuilder()
# Start with basic class structure from components
for component in architectural_concepts.get('components', []):
class_definition = self._map_component_to_class(component)
diagram_builder.add_class(class_definition)
# Add relationships identified in the architectural analysis
for relationship in architectural_concepts.get('relationships', []):
uml_relationship = self._map_to_uml_relationship(relationship)
diagram_builder.add_relationship(uml_relationship)
# Apply pattern-specific class structures
for pattern in applied_patterns:
pattern_mapper = self.pattern_mappers.get(pattern['pattern'].name)
if pattern_mapper:
pattern_classes = pattern_mapper.generate_class_structure(
pattern, architectural_concepts
)
diagram_builder.integrate_pattern_structure(pattern_classes)
# Generate the final PlantUML code
plantuml_code = diagram_builder.build()
return {
'plantuml_code': plantuml_code,
'diagram_type': 'class',
'elements': diagram_builder.get_elements(),
'pattern_annotations': self._generate_pattern_annotations(applied_patterns)
}
def _map_component_to_class(self, component):
"""Map an architectural component to a UML class definition"""
class_definition = {
'name': component.name,
'stereotype': self._determine_class_stereotype(component),
'attributes': self._extract_attributes(component),
'methods': self._extract_methods(component),
'visibility': component.get('visibility', 'public'),
'abstract': component.get('abstract', False)
}
return class_definition
def _determine_class_stereotype(self, component):
"""Determine appropriate UML stereotype for component"""
component_type = component.get('type', '').lower()
if 'interface' in component_type:
return 'interface'
elif 'abstract' in component_type:
return 'abstract'
elif 'controller' in component_type:
return 'controller'
elif 'service' in component_type:
return 'service'
elif 'repository' in component_type:
return 'repository'
elif 'entity' in component_type:
return 'entity'
else:
return None
class ObserverPatternMapper:
"""Maps Observer pattern to UML class diagram elements"""
def generate_class_structure(self, pattern_application, concepts):
"""Generate Observer pattern class structure"""
# Identify or create Subject and Observer classes
subject_class = self._find_or_create_subject(
pattern_application, concepts
)
observer_interface = self._create_observer_interface(
pattern_application
)
concrete_observers = self._create_concrete_observers(
pattern_application, concepts
)
pattern_structure = {
'classes': [subject_class, observer_interface] + concrete_observers,
'relationships': self._create_observer_relationships(
subject_class, observer_interface, concrete_observers
),
'notes': self._create_pattern_notes()
}
return pattern_structure
def _find_or_create_subject(self, pattern_application, concepts):
"""Find existing component to serve as Subject or create new one"""
# Look for component that matches Subject role characteristics
for component in concepts.get('components', []):
if self._matches_subject_characteristics(component):
return self._enhance_as_subject(component)
# Create new Subject class if no suitable component found
return {
'name': 'Subject',
'stereotype': 'abstract',
'attributes': [
{'name': 'observers', 'type': 'List<Observer>', 'visibility': 'private'}
],
'methods': [
{'name': 'attach', 'parameters': ['observer: Observer'], 'visibility': 'public'},
{'name': 'detach', 'parameters': ['observer: Observer'], 'visibility': 'public'},
{'name': 'notify', 'parameters': [], 'visibility': 'protected'}
]
}
def _create_observer_relationships(self, subject, observer_interface, concrete_observers):
"""Create relationships for Observer pattern"""
relationships = []
# Subject aggregates Observers
relationships.append({
'type': 'aggregation',
'from': subject['name'],
'to': observer_interface['name'],
'multiplicity': '1..*',
'label': 'observers'
})
# Concrete Observers implement Observer interface
for concrete_observer in concrete_observers:
relationships.append({
'type': 'realization',
'from': concrete_observer['name'],
'to': observer_interface['name']
})
return relationships
class PlantUMLClassDiagramBuilder:
"""Builds PlantUML code for class diagrams"""
def __init__(self):
self.classes = []
self.relationships = []
self.notes = []
self.packages = []
def add_class(self, class_definition):
"""Add a class to the diagram"""
self.classes.append(class_definition)
def add_relationship(self, relationship):
"""Add a relationship to the diagram"""
self.relationships.append(relationship)
def integrate_pattern_structure(self, pattern_structure):
"""Integrate pattern-specific structure into the diagram"""
self.classes.extend(pattern_structure.get('classes', []))
self.relationships.extend(pattern_structure.get('relationships', []))
self.notes.extend(pattern_structure.get('notes', []))
def build(self):
"""Generate the complete PlantUML code"""
plantuml_code = "@startuml\n"
plantuml_code += "!theme plain\n"
plantuml_code += "skinparam classAttributeIconSize 0\n\n"
# Add packages if any
for package in self.packages:
plantuml_code += f"package \"{package['name']}\" {{\n"
plantuml_code += self._generate_package_content(package)
plantuml_code += "}\n\n"
# Add classes
for class_def in self.classes:
plantuml_code += self._generate_class_code(class_def)
plantuml_code += "\n"
# Add relationships
for relationship in self.relationships:
plantuml_code += self._generate_relationship_code(relationship)
plantuml_code += "\n"
# Add notes
for note in self.notes:
plantuml_code += self._generate_note_code(note)
plantuml_code += "\n"
plantuml_code += "@enduml"
return plantuml_code
def _generate_class_code(self, class_def):
"""Generate PlantUML code for a single class"""
class_code = ""
# Handle stereotypes
if class_def.get('stereotype'):
if class_def['stereotype'] == 'interface':
class_code += f"interface {class_def['name']} {{\n"
elif class_def['stereotype'] == 'abstract':
class_code += f"abstract class {class_def['name']} {{\n"
else:
class_code += f"class {class_def['name']} <<{class_def['stereotype']}>> {{\n"
else:
class_code += f"class {class_def['name']} {{\n"
# Add attributes
for attr in class_def.get('attributes', []):
visibility = self._map_visibility_symbol(attr.get('visibility', 'private'))
class_code += f" {visibility}{attr['name']}: {attr['type']}\n"
# Add separator between attributes and methods
if class_def.get('attributes') and class_def.get('methods'):
class_code += " --\n"
# Add methods
for method in class_def.get('methods', []):
visibility = self._map_visibility_symbol(method.get('visibility', 'public'))
params = ', '.join(method.get('parameters', []))
return_type = method.get('return_type', 'void')
class_code += f" {visibility}{method['name']}({params}): {return_type}\n"
class_code += "}"
return class_code
def _map_visibility_symbol(self, visibility):
"""Map visibility keywords to PlantUML symbols"""
visibility_map = {
'public': '+',
'private': '-',
'protected': '#',
'package': '~'
}
return visibility_map.get(visibility.lower(), '+')
This comprehensive UML generation implementation demonstrates how the system translates architectural concepts and design patterns into proper UML representations. The pattern mappers ensure that specific design patterns are correctly represented in the generated diagrams, while the PlantUML builder creates syntactically correct and well-formatted diagram code.
C4 MODEL INTEGRATION
The C4 model integration provides a hierarchical approach to architectural documentation, creating, container, component, and code-level diagrams that complement the UML representations. The C4 model focuses on different levels of abstraction, making it particularly valuable for communicating architectural decisions to different stakeholder groups.
The Context level diagram shows the system boundary and its relationships with external actors and systems. This level is generated by analyzing the problem description for mentions of external systems, user types, and system boundaries. The system identifies the primary system being designed and maps all external interactions mentioned in the problem description.
The Container level diagram breaks down the system into major containers such as web applications, databases, microservices, and external systems. The system analyzes the architectural concepts to identify logical containers based on technology boundaries, deployment units, and functional groupings mentioned in the problem description.
The Component level diagram shows the internal structure of individual containers, identifying major components and their relationships. This level leverages the applied design patterns to ensure that pattern-specific component structures are properly represented in the C4 model.
Here is a detailed implementation of the C4 model generation:
class C4ModelGenerator:
def __init__(self):
self.diagram_generators = {
'context': C4ContextDiagramGenerator(),
'container': C4ContainerDiagramGenerator(),
'component': C4ComponentDiagramGenerator()
}
self.c4_stdlib_import = "!include https://raw.githubusercontent.com/plantuml-stdlib/C4-PlantUML/master/C4_Context.puml"
def generate_c4_diagrams(self, architectural_concepts, applied_patterns):
"""Generate complete C4 model diagrams"""
c4_diagrams = {}
# Extract system boundary and external entities
system_boundary = self._identify_system_boundary(architectural_concepts)
external_entities = self._identify_external_entities(architectural_concepts)
# Generate Context diagram
c4_diagrams['context'] = self._generate_context_diagram(
system_boundary, external_entities, architectural_concepts
)
# Generate Container diagram
containers = self._identify_containers(architectural_concepts, applied_patterns)
c4_diagrams['container'] = self._generate_container_diagram(
system_boundary, containers, external_entities
)
# Generate Component diagrams for each major container
for container in containers:
if container.get('show_components', False):
component_diagram = self._generate_component_diagram(
container, architectural_concepts, applied_patterns
)
c4_diagrams[f"component_{container['name']}"] = component_diagram
return c4_diagrams
def _identify_system_boundary(self, concepts):
"""Identify the primary system being designed"""
# Look for explicit system name in concepts
system_name = concepts.get('system_name')
if not system_name:
# Infer system name from problem description
system_name = self._infer_system_name(concepts)
system_boundary = {
'name': system_name,
'description': concepts.get('system_description', ''),
'type': 'SoftwareSystem',
'technology': concepts.get('primary_technology', ''),
'responsibilities': concepts.get('system_responsibilities', [])
}
return system_boundary
def _identify_containers(self, concepts, patterns):
"""Identify logical containers within the system"""
containers = []
# Analyze components to group them into containers
components = concepts.get('components', [])
container_groups = self._group_components_into_containers(components)
for group_name, group_components in container_groups.items():
container = {
'name': group_name,
'type': self._determine_container_type(group_components),
'technology': self._determine_container_technology(group_components),
'description': self._generate_container_description(group_components),
'components': group_components,
'show_components': len(group_components) > 1
}
containers.append(container)
# Apply pattern-specific container structures
for pattern in patterns:
pattern_containers = self._get_pattern_containers(pattern, containers)
containers.extend(pattern_containers)
return containers
def _generate_context_diagram(self, system_boundary, external_entities, concepts):
"""Generate C4 Context diagram"""
generator = self.diagram_generators['context']
plantuml_code = f"{self.c4_stdlib_import}\n\n"
plantuml_code += "LAYOUT_WITH_LEGEND()\n\n"
plantuml_code += f"title System Context diagram for {system_boundary['name']}\n\n"
# Add the main system
plantuml_code += generator.add_system(
system_boundary['name'],
system_boundary['description'],
system_boundary.get('technology', '')
)
# Add external entities (persons and external systems)
for entity in external_entities:
if entity['type'] == 'person':
plantuml_code += generator.add_person(
entity['name'],
entity['description']
)
else:
plantuml_code += generator.add_external_system(
entity['name'],
entity['description'],
entity.get('technology', '')
)
# Add relationships
relationships = concepts.get('external_relationships', [])
for relationship in relationships:
plantuml_code += generator.add_relationship(
relationship['from'],
relationship['to'],
relationship['description'],
relationship.get('technology', ''),
relationship.get('direction', 'Rel')
)
plantuml_code += "\n@enduml"
return {
'plantuml_code': plantuml_code,
'diagram_type': 'c4_context',
'system_boundary': system_boundary,
'external_entities': external_entities
}
class C4ContainerDiagramGenerator:
"""Generates C4 Container level diagrams"""
def add_container(self, name, description, technology, container_type="Container"):
"""Add a container to the diagram"""
# Sanitize name for PlantUML
sanitized_name = self._sanitize_name(name)
if container_type.lower() == 'database':
return f"ContainerDb({sanitized_name}, \"{name}\", \"{technology}\", \"{description}\")\n"
elif container_type.lower() == 'queue':
return f"ContainerQueue({sanitized_name}, \"{name}\", \"{technology}\", \"{description}\")\n"
else:
return f"Container({sanitized_name}, \"{name}\", \"{technology}\", \"{description}\")\n"
def add_container_relationship(self, from_container, to_container, description, technology="", direction="Rel"):
"""Add relationship between containers"""
from_sanitized = self._sanitize_name(from_container)
to_sanitized = self._sanitize_name(to_container)
if technology:
return f"{direction}({from_sanitized}, {to_sanitized}, \"{description}\", \"{technology}\")\n"
else:
return f"{direction}({from_sanitized}, {to_sanitized}, \"{description}\")\n"
def _sanitize_name(self, name):
"""Sanitize names for PlantUML compatibility"""
import re
# Remove special characters and spaces, replace with underscores
sanitized = re.sub(r'[^a-zA-Z0-9_]', '_', name)
# Ensure it starts with a letter
if sanitized and not sanitized[0].isalpha():
sanitized = 'C_' + sanitized
return sanitized or 'Container'
class C4ComponentDiagramGenerator:
"""Generates C4 Component level diagrams with pattern integration"""
def generate_component_diagram_with_patterns(self, container, concepts, patterns):
"""Generate component diagram integrating applied patterns"""
plantuml_code = "!include https://raw.githubusercontent.com/plantuml-stdlib/C4-PlantUML/master/C4_Component.puml\n\n"
plantuml_code += f"title Component diagram for {container['name']}\n\n"
# Add container boundary
plantuml_code += f"Container_Boundary(c1, \"{container['name']}\") {{\n"
# Add components from architectural analysis
components = container.get('components', [])
for component in components:
plantuml_code += self._add_component_with_stereotype(component)
# Add pattern-specific components
for pattern in patterns:
pattern_components = self._generate_pattern_components(pattern, container)
for pattern_component in pattern_components:
plantuml_code += self._add_component_with_stereotype(pattern_component)
plantuml_code += "}\n\n"
# Add external dependencies
external_deps = self._identify_external_dependencies(container, concepts)
for dep in external_deps:
plantuml_code += self._add_external_dependency(dep)
# Add relationships
relationships = self._generate_component_relationships(
components, patterns, external_deps
)
for relationship in relationships:
plantuml_code += self._add_component_relationship(relationship)
plantuml_code += "\n@enduml"
return {
'plantuml_code': plantuml_code,
'diagram_type': 'c4_component',
'container': container,
'patterns_applied': [p['pattern'].name for p in patterns]
}
def _add_component_with_stereotype(self, component):
"""Add component with appropriate stereotype based on pattern role"""
component_name = self._sanitize_name(component['name'])
stereotype = component.get('pattern_role', component.get('stereotype', ''))
if stereotype:
return f"Component({component_name}, \"{component['name']}\", \"{stereotype}\", \"{component.get('description', '')}\")\n"
else:
return f"Component({component_name}, \"{component['name']}\", \"{component.get('description', '')}\")\n"
def _generate_pattern_components(self, pattern, container):
"""Generate components specific to the applied pattern"""
pattern_name = pattern['pattern'].name
pattern_components = []
if pattern_name == 'MVC':
pattern_components.extend(self._generate_mvc_components(pattern, container))
elif pattern_name == 'Observer':
pattern_components.extend(self._generate_observer_components(pattern, container))
elif pattern_name == 'Strategy':
pattern_components.extend(self._generate_strategy_components(pattern, container))
return pattern_components
def _generate_mvc_components(self, pattern, container):
"""Generate MVC pattern specific components"""
mvc_components = []
# Model components
mvc_components.append({
'name': f"{container['name']}_Model",
'description': "Manages data and business logic",
'pattern_role': 'Model',
'stereotype': 'Model'
})
# View components
mvc_components.append({
'name': f"{container['name']}_View",
'description': "Handles presentation logic",
'pattern_role': 'View',
'stereotype': 'View'
})
# Controller components
mvc_components.append({
'name': f"{container['name']}_Controller",
'description': "Coordinates between Model and View",
'pattern_role': 'Controller',
'stereotype': 'Controller'
})
return mvc_components
This C4 model implementation demonstrates how the system creates hierarchical architectural views that complement the UML diagrams. The integration with design patterns ensures that pattern-specific structures are properly represented at the appropriate abstraction levels, providing comprehensive architectural documentation.
ARC42 TEMPLATE INTEGRATION
The Arc42 template integration provides structured documentation that explains the generated diagrams both graphically and textually. Arc42 is a comprehensive template for software architecture documentation that covers all relevant aspects of software architecture in a structured and standardized way.
The system generates specific sections of the Arc42 template that are most relevant to the architectural problem and the applied patterns. The primary focus is on the solution strategy section, the building block view, and the runtime view, as these sections directly correspond to the generated UML and C4 diagrams.
The solution strategy section explains the fundamental decisions and solution approaches that address the architectural problem. This section integrates the rationale for pattern selection, explains how the chosen patterns address the identified quality attributes, and provides justification for the overall architectural approach.
The building block view section provides detailed explanations of the generated diagrams, describing the purpose and responsibility of each component, the relationships between components, and how the applied patterns influence the overall structure. This section maintains traceability between the natural language problem description and the visual representations.
Here is a comprehensive implementation of the Arc42 documentation generator:
class Arc42DocumentationGenerator:
def __init__(self, template_engine):
self.template_engine = template_engine
self.section_generators = {
'solution_strategy': SolutionStrategyGenerator(),
'building_blocks': BuildingBlocksGenerator(),
'runtime_view': RuntimeViewGenerator(),
'deployment_view': DeploymentViewGenerator(),
'concepts': ConceptsGenerator()
}
def generate_arc42_documentation(self, problem_description, architectural_concepts,
applied_patterns, generated_diagrams):
"""Generate comprehensive Arc42 documentation"""
documentation = {
'introduction_goals': self._generate_introduction_and_goals(
problem_description, architectural_concepts
),
'constraints': self._generate_constraints_section(architectural_concepts),
'solution_strategy': self._generate_solution_strategy(
applied_patterns, architectural_concepts
),
'building_blocks': self._generate_building_blocks_view(
generated_diagrams, applied_patterns, architectural_concepts
),
'runtime_view': self._generate_runtime_view(
generated_diagrams, applied_patterns
),
'concepts': self._generate_crosscutting_concepts(
applied_patterns, architectural_concepts
)
}
return self._compile_documentation(documentation)
def _generate_solution_strategy(self, applied_patterns, concepts):
"""Generate Section 4: Solution Strategy"""
strategy_generator = self.section_generators['solution_strategy']
solution_strategy = {
'overview': strategy_generator.generate_strategy_overview(
applied_patterns, concepts
),
'quality_goals': strategy_generator.explain_quality_goal_achievement(
applied_patterns, concepts.get('quality_attributes', [])
),
'pattern_rationale': strategy_generator.generate_pattern_rationale(
applied_patterns
),
'technology_decisions': strategy_generator.explain_technology_decisions(
concepts, applied_patterns
)
}
return solution_strategy
def _generate_building_blocks_view(self, diagrams, patterns, concepts):
"""Generate Section 5: Building Blocks View"""
building_blocks_generator = self.section_generators['building_blocks']
building_blocks = {}
# Level 1: System Context
if 'c4_context' in diagrams:
building_blocks['level_1'] = building_blocks_generator.generate_level_1_view(
diagrams['c4_context'], concepts
)
# Level 2: Container View
if 'c4_container' in diagrams:
building_blocks['level_2'] = building_blocks_generator.generate_level_2_view(
diagrams['c4_container'], patterns, concepts
)
# Level 3: Component Views
component_diagrams = {k: v for k, v in diagrams.items() if k.startswith('component_')}
if component_diagrams:
building_blocks['level_3'] = building_blocks_generator.generate_level_3_views(
component_diagrams, patterns, concepts
)
return building_blocks
class SolutionStrategyGenerator:
"""Generates Arc42 Section 4: Solution Strategy"""
def generate_strategy_overview(self, applied_patterns, concepts):
"""Generate high-level solution strategy overview"""
strategy_overview = {
'architectural_approach': self._describe_architectural_approach(
applied_patterns, concepts
),
'key_decisions': self._identify_key_architectural_decisions(
applied_patterns, concepts
),
'quality_attribute_scenarios': self._generate_quality_scenarios(
concepts.get('quality_attributes', [])
)
}
return strategy_overview
def _describe_architectural_approach(self, patterns, concepts):
"""Describe the overall architectural approach"""
approach_description = []
# Analyze dominant architectural styles from patterns
architectural_styles = self._identify_architectural_styles(patterns)
if 'layered' in architectural_styles:
approach_description.append(
"The solution employs a layered architectural style to achieve separation of concerns "
"and maintainability. This approach isolates different aspects of the system into "
"distinct layers, each with specific responsibilities and well-defined interfaces."
)
if 'mvc' in architectural_styles:
approach_description.append(
"The Model-View-Controller pattern is applied to separate presentation logic from "
"business logic, enabling independent evolution of user interface and core functionality. "
"This separation facilitates testing, maintenance, and potential future changes to "
"either the user interface or business rules."
)
if 'observer' in architectural_styles:
approach_description.append(
"The Observer pattern is utilized to implement loose coupling between components "
"that need to react to state changes. This approach enables the system to maintain "
"consistency across multiple components while avoiding tight dependencies that would "
"reduce flexibility and testability."
)
# Add domain-specific considerations
domain_considerations = self._analyze_domain_specific_approach(concepts)
approach_description.extend(domain_considerations)
return ' '.join(approach_description)
def generate_pattern_rationale(self, applied_patterns):
"""Generate detailed rationale for pattern selection"""
pattern_rationales = []
for pattern in applied_patterns:
rationale = {
'pattern_name': pattern['pattern'].name,
'selection_reason': self._explain_pattern_selection(pattern),
'benefits': self._describe_pattern_benefits(pattern),
'trade_offs': self._describe_pattern_trade_offs(pattern),
'implementation_considerations': self._describe_implementation_considerations(pattern)
}
pattern_rationales.append(rationale)
return pattern_rationales
def _explain_pattern_selection(self, pattern):
"""Explain why this specific pattern was selected"""
pattern_name = pattern['pattern'].name
reasoning_path = pattern.get('reasoning_path', {})
explanation = f"The {pattern_name} pattern was selected because "
# Add quality attribute alignment explanation
quality_alignment = reasoning_path.get('quality_alignment', {})
if quality_alignment:
aligned_qualities = quality_alignment.get('aligned_attributes', [])
if aligned_qualities:
explanation += f"it directly addresses the required quality attributes of {', '.join(aligned_qualities)}. "
# Add context match explanation
context_match = reasoning_path.get('context_match', {})
if context_match and context_match.get('match_score', 0) > 0.7:
explanation += f"The pattern's typical usage context aligns well with the problem domain. "
# Add pattern synergy explanation
pattern_synergies = reasoning_path.get('pattern_synergies', {})
if pattern_synergies:
synergistic_patterns = pattern_synergies.get('complementary_patterns', [])
if synergistic_patterns:
explanation += f"Additionally, this pattern works synergistically with {', '.join(synergistic_patterns)} to provide a comprehensive solution. "
return explanation
class BuildingBlocksGenerator:
"""Generates Arc42 Section 5: Building Blocks View"""
def generate_level_1_view(self, context_diagram, concepts):
"""Generate Level 1 building blocks documentation"""
level_1_doc = {
'overview': self._generate_context_overview(context_diagram, concepts),
'system_responsibility': self._describe_system_responsibility(concepts),
'external_interfaces': self._document_external_interfaces(context_diagram),
'diagram_explanation': self._explain_context_diagram(context_diagram)
}
return level_1_doc
def _generate_context_overview(self, context_diagram, concepts):
"""Generate overview of the system context"""
system_boundary = context_diagram.get('system_boundary', {})
external_entities = context_diagram.get('external_entities', [])
overview = f"The {system_boundary.get('name', 'system')} operates within an ecosystem of "
# Count and categorize external entities
persons = [e for e in external_entities if e.get('type') == 'person']
external_systems = [e for e in external_entities if e.get('type') == 'system']
if persons:
overview += f"{len(persons)} user type(s) "
if external_systems:
overview += "and "
if external_systems:
overview += f"{len(external_systems)} external system(s). "
overview += f"The primary purpose of the {system_boundary.get('name', 'system')} is to "
overview += system_boundary.get('description', 'provide the core functionality described in the requirements.')
return overview
def generate_level_2_view(self, container_diagram, patterns, concepts):
"""Generate Level 2 building blocks documentation"""
level_2_doc = {
'container_overview': self._generate_container_overview(container_diagram),
'container_responsibilities': self._document_container_responsibilities(
container_diagram, patterns
),
'technology_mapping': self._document_technology_mapping(container_diagram),
'pattern_influence': self._explain_pattern_influence_on_containers(
patterns, container_diagram
)
}
return level_2_doc
def _document_container_responsibilities(self, container_diagram, patterns):
"""Document the responsibilities of each container"""
responsibilities = {}
containers = container_diagram.get('containers', [])
for container in containers:
container_name = container['name']
# Base responsibilities from container definition
base_responsibilities = [container.get('description', '')]
# Add pattern-influenced responsibilities
pattern_responsibilities = self._derive_pattern_responsibilities(
container, patterns
)
responsibilities[container_name] = {
'primary_responsibility': container.get('description', ''),
'detailed_responsibilities': base_responsibilities + pattern_responsibilities,
'technology_stack': container.get('technology', ''),
'interfaces': container.get('interfaces', [])
}
return responsibilities
def _explain_pattern_influence_on_containers(self, patterns, container_diagram):
"""Explain how patterns influence container structure"""
pattern_influences = []
for pattern in patterns:
pattern_name = pattern['pattern'].name
if pattern_name == 'MVC':
pattern_influences.append({
'pattern': pattern_name,
'influence': "The MVC pattern influences the container structure by promoting "
"separation between presentation, business logic, and data management concerns. "
"This may result in separate containers for web presentation, application services, "
"and data persistence, each optimized for their specific responsibilities."
})
elif pattern_name == 'Microservices':
pattern_influences.append({
'pattern': pattern_name,
'influence': "The Microservices pattern drives the decomposition of functionality "
"into independently deployable containers, each representing a bounded context "
"with its own data storage and business capabilities. This influences both "
"the number and the granularity of containers in the architecture."
})
return pattern_influences
This Arc42 documentation generator demonstrates how the system creates comprehensive textual documentation that explains the generated diagrams and architectural decisions. The documentation maintains traceability between the original problem description, the applied patterns, and the resulting architectural artifacts, providing stakeholders with clear understanding of the solution rationale.
PATTERN APPLICATION STRATEGY
The pattern application strategy represents the core intelligence of the system, determining how to effectively apply design patterns to solve the identified architectural problems. This strategy goes beyond simple pattern matching to consider pattern combinations, conflicts, and the specific context of the problem domain.
The strategy employs a multi-dimensional analysis approach that considers the problem characteristics, quality attribute priorities, system constraints, and potential pattern interactions. The system maintains knowledge about pattern relationships, including which patterns work well together, which patterns conflict with each other, and which patterns can be layered or composed to address complex architectural challenges.
The pattern application process involves several stages of analysis and refinement. The initial stage identifies candidate patterns based on semantic similarity and context matching. The refinement stage evaluates pattern combinations and resolves potential conflicts. The final stage determines the specific roles and responsibilities within each pattern and maps them to existing or new architectural components.
The system also considers the evolutionary aspects of pattern application, ensuring that the selected patterns support future extensibility and modification. This involves analyzing the long-term implications of pattern choices and their impact on system maintainability, testability, and scalability.
Here is a detailed implementation of the pattern application strategy:
class PatternApplicationStrategy:
def __init__(self, pattern_knowledge_base, conflict_resolver):
self.pattern_kb = pattern_knowledge_base
self.conflict_resolver = conflict_resolver
self.application_rules = PatternApplicationRules()
self.combination_analyzer = PatternCombinationAnalyzer()
def apply_patterns_to_architecture(self, candidate_patterns, architectural_concepts):
"""Apply selected patterns to the architectural solution"""
# Stage 1: Evaluate pattern combinations and conflicts
pattern_combinations = self._evaluate_pattern_combinations(candidate_patterns)
# Stage 2: Resolve conflicts and select optimal pattern set
optimal_patterns = self._resolve_conflicts_and_optimize(
pattern_combinations, architectural_concepts
)
# Stage 3: Map pattern roles to architectural components
pattern_applications = []
for pattern in optimal_patterns:
application = self._apply_single_pattern(pattern, architectural_concepts)
pattern_applications.append(application)
# Stage 4: Validate and refine pattern applications
validated_applications = self._validate_pattern_applications(
pattern_applications, architectural_concepts
)
return validated_applications
def _evaluate_pattern_combinations(self, candidate_patterns):
"""Evaluate potential combinations of patterns"""
combinations = []
# Evaluate single patterns
for pattern in candidate_patterns:
combinations.append({
'patterns': [pattern],
'combination_score': pattern['enhanced_score'],
'synergy_bonus': 0.0,
'conflict_penalty': 0.0
})
# Evaluate pattern pairs
for i in range(len(candidate_patterns)):
for j in range(i + 1, len(candidate_patterns)):
pattern1 = candidate_patterns[i]
pattern2 = candidate_patterns[j]
combination_analysis = self.combination_analyzer.analyze_combination(
pattern1, pattern2
)
if combination_analysis['compatible']:
combinations.append({
'patterns': [pattern1, pattern2],
'combination_score': (pattern1['enhanced_score'] + pattern2['enhanced_score']) / 2,
'synergy_bonus': combination_analysis['synergy_score'],
'conflict_penalty': combination_analysis['conflict_penalty']
})
# Evaluate larger combinations if beneficial
promising_combinations = [c for c in combinations if c['synergy_bonus'] > 0.2]
for combination in promising_combinations:
if len(combination['patterns']) == 2:
extended_combinations = self._explore_extended_combinations(
combination, candidate_patterns
)
combinations.extend(extended_combinations)
return combinations
def _apply_single_pattern(self, pattern, architectural_concepts):
"""Apply a single pattern to the architectural solution"""
pattern_definition = pattern['pattern']
# Extract pattern roles and responsibilities
pattern_roles = pattern_definition.get_roles()
# Map roles to existing components or create new ones
role_mappings = self._map_pattern_roles(pattern_roles, architectural_concepts)
# Generate pattern-specific architectural elements
pattern_elements = self._generate_pattern_elements(
pattern_definition, role_mappings, architectural_concepts
)
# Create application documentation
application_doc = self._create_pattern_application_documentation(
pattern, role_mappings, pattern_elements
)
pattern_application = {
'pattern': pattern_definition,
'role_mappings': role_mappings,
'architectural_elements': pattern_elements,
'application_rationale': pattern.get('rationale', ''),
'documentation': application_doc,
'quality_impact': self._analyze_quality_impact(pattern, role_mappings)
}
return pattern_application
def _map_pattern_roles(self, pattern_roles, architectural_concepts):
"""Map pattern roles to architectural components"""
role_mappings = {}
existing_components = architectural_concepts.get('components', [])
for role in pattern_roles:
# Try to find existing component that fits the role
best_match = self._find_best_component_for_role(role, existing_components)
if best_match and best_match['compatibility_score'] > 0.7:
# Map to existing component
role_mappings[role.name] = {
'type': 'existing_component',
'component': best_match['component'],
'adaptations_needed': best_match['adaptations'],
'compatibility_score': best_match['compatibility_score']
}
else:
# Create new component for the role
new_component = self._create_component_for_role(role, architectural_concepts)
role_mappings[role.name] = {
'type': 'new_component',
'component': new_component,
'creation_rationale': self._explain_component_creation(role, existing_components)
}
return role_mappings
def _find_best_component_for_role(self, role, components):
"""Find the best existing component to fulfill a pattern role"""
best_match = None
highest_score = 0.0
for component in components:
compatibility_score = self._calculate_role_compatibility(role, component)
if compatibility_score > highest_score:
highest_score = compatibility_score
adaptations = self._identify_required_adaptations(role, component)
best_match = {
'component': component,
'compatibility_score': compatibility_score,
'adaptations': adaptations
}
return best_match
def _calculate_role_compatibility(self, role, component):
"""Calculate how well a component fits a pattern role"""
compatibility_factors = []
# Responsibility alignment
role_responsibilities = set(role.get_responsibilities())
component_responsibilities = set(component.get('responsibilities', []))
responsibility_overlap = len(role_responsibilities.intersection(component_responsibilities))
responsibility_score = responsibility_overlap / len(role_responsibilities) if role_responsibilities else 0.0
compatibility_factors.append(('responsibility', responsibility_score, 0.4))
# Interface compatibility
role_interfaces = role.get_required_interfaces()
component_interfaces = component.get('interfaces', [])
interface_score = self._calculate_interface_compatibility(role_interfaces, component_interfaces)
compatibility_factors.append(('interface', interface_score, 0.3))
# Behavioral compatibility
role_behavior = role.get_behavioral_requirements()
component_behavior = component.get('behavior', {})
behavior_score = self._calculate_behavioral_compatibility(role_behavior, component_behavior)
compatibility_factors.append(('behavior', behavior_score, 0.3))
# Calculate weighted compatibility score
total_score = sum(score * weight for _, score, weight in compatibility_factors)
return total_score
def _create_component_for_role(self, role, architectural_concepts):
"""Create a new component to fulfill a pattern role"""
component_name = self._generate_component_name(role, architectural_concepts)
new_component = {
'name': component_name,
'type': role.get_component_type(),
'responsibilities': role.get_responsibilities(),
'interfaces': role.get_required_interfaces(),
'behavior': role.get_behavioral_requirements(),
'pattern_role': role.name,
'creation_reason': 'pattern_application',
'quality_attributes': role.get_quality_requirements()
}
return new_component
class PatternCombinationAnalyzer:
"""Analyzes compatibility and synergy between patterns"""
def __init__(self):
self.synergy_rules = self._load_synergy_rules()
self.conflict_rules = self._load_conflict_rules()
def analyze_combination(self, pattern1, pattern2):
"""Analyze the combination of two patterns"""
# Check for known synergies
synergy_score = self._calculate_synergy_score(pattern1, pattern2)
# Check for conflicts
conflict_penalty = self._calculate_conflict_penalty(pattern1, pattern2)
# Determine overall compatibility
compatibility = (synergy_score - conflict_penalty) > 0.0
analysis = {
'compatible': compatibility,
'synergy_score': synergy_score,
'conflict_penalty': conflict_penalty,
'combination_rationale': self._generate_combination_rationale(
pattern1, pattern2, synergy_score, conflict_penalty
)
}
return analysis
def _calculate_synergy_score(self, pattern1, pattern2):
"""Calculate synergy score between two patterns"""
pattern1_name = pattern1['pattern'].name
pattern2_name = pattern2['pattern'].name
# Check predefined synergy rules
synergy_key = tuple(sorted([pattern1_name, pattern2_name]))
predefined_synergy = self.synergy_rules.get(synergy_key, 0.0)
# Calculate dynamic synergy based on complementary characteristics
dynamic_synergy = self._calculate_dynamic_synergy(pattern1, pattern2)
return max(predefined_synergy, dynamic_synergy)
def _calculate_dynamic_synergy(self, pattern1, pattern2):
"""Calculate synergy based on pattern characteristics"""
# Analyze quality attribute complementarity
qa1 = set(pattern1.get('quality_attributes', []))
qa2 = set(pattern2.get('quality_attributes', []))
# Patterns are synergistic if they address different quality attributes
qa_complementarity = len(qa1.symmetric_difference(qa2)) / (len(qa1.union(qa2)) or 1)
# Analyze structural complementarity
roles1 = set(pattern1['pattern'].get_role_names())
roles2 = set(pattern2['pattern'].get_role_names())
# Patterns are synergistic if they have minimal role overlap
role_complementarity = 1.0 - (len(roles1.intersection(roles2)) / (len(roles1.union(roles2)) or 1))
# Analyze scope complementarity
scope1 = pattern1['pattern'].get_scope()
scope2 = pattern2['pattern'].get_scope()
scope_complementarity = self._calculate_scope_complementarity(scope1, scope2)
# Weighted combination of complementarity factors
dynamic_synergy = (
0.4 * qa_complementarity +
0.3 * role_complementarity +
0.3 * scope_complementarity
)
return dynamic_synergy
def _load_synergy_rules(self):
"""Load predefined pattern synergy rules"""
synergy_rules = {
('MVC', 'Observer'): 0.8, # Observer often used within MVC
('Strategy', 'Factory'): 0.7, # Factory can create Strategy instances
('Command', 'Observer'): 0.6, # Commands can notify observers
('Decorator', 'Strategy'): 0.5, # Can be combined for flexible behavior
('Facade', 'Adapter'): 0.6, # Often used together for interface management
}
return synergy_rules
def _load_conflict_rules(self):
"""Load predefined pattern conflict rules"""
conflict_rules = {
('Singleton', 'Strategy'): 0.4, # Singleton can limit Strategy flexibility
('Observer', 'Mediator'): 0.3, # Can create competing communication mechanisms
}
return conflict_rules
This pattern application strategy demonstrates how the system intelligently combines and applies design patterns to create comprehensive architectural solutions. The strategy considers pattern compatibility, role mapping, and the specific context of the architectural problem to ensure that the applied patterns work together effectively and address the identified requirements.
IMPLEMENTATION DETAILS WITH CODE EXAMPLES
The implementation of the intelligent architecture agent requires careful orchestration of multiple components and technologies. The system architecture follows a microservices approach where each major component operates as an independent service with well-defined interfaces and responsibilities.
The core orchestration engine coordinates the interaction between the Natural Language Processing Pipeline, Pattern Recognition Engine, Diagram Generation Engine, and Documentation Generator. This engine maintains the overall workflow state and ensures that information flows correctly between components while handling error conditions and providing feedback to users.
The system employs a plugin architecture for pattern definitions and diagram generators, allowing for easy extension with new patterns and diagram types. Each pattern is defined as a structured object that encapsulates the pattern's intent, structure, participants, collaborations, and consequences, along with generation templates for different diagram types.
Let me provide a comprehensive implementation example that demonstrates the complete system integration:
class ArchitectureAgentOrchestrator:
"""Main orchestrator for the intelligent architecture agent"""
def __init__(self, config):
self.config = config
self.nlp_pipeline = self._initialize_nlp_pipeline()
self.pattern_engine = self._initialize_pattern_engine()
self.diagram_generator = self._initialize_diagram_generator()
self.doc_generator = self._initialize_documentation_generator()
self.workflow_state = WorkflowState()
def process_architecture_request(self, problem_description, user_preferences=None):
"""Process a complete architecture generation request"""
try:
# Stage 1: Natural Language Processing
self.workflow_state.update_stage('nlp_processing')
architectural_concepts = self.nlp_pipeline.extract_concepts(problem_description)
# Stage 2: Pattern Recognition and Selection
self.workflow_state.update_stage('pattern_recognition')
candidate_patterns = self.pattern_engine.find_applicable_patterns(
architectural_concepts
)
# Apply user preferences if provided
if user_preferences:
candidate_patterns = self._apply_user_preferences(
candidate_patterns, user_preferences
)
# Stage 3: Pattern Application
self.workflow_state.update_stage('pattern_application')
applied_patterns = self.pattern_engine.apply_patterns_to_architecture(
candidate_patterns, architectural_concepts
)
# Stage 4: Diagram Generation
self.workflow_state.update_stage('diagram_generation')
generated_diagrams = self.diagram_generator.generate_diagrams(
architectural_concepts, applied_patterns
)
# Stage 5: Documentation Generation
self.workflow_state.update_stage('documentation_generation')
arc42_documentation = self.doc_generator.generate_arc42_documentation(
problem_description, architectural_concepts, applied_patterns, generated_diagrams
)
# Stage 6: Result Compilation
self.workflow_state.update_stage('result_compilation')
final_result = self._compile_final_result(
problem_description, architectural_concepts, applied_patterns,
generated_diagrams, arc42_documentation
)
self.workflow_state.mark_completed()
return final_result
except Exception as e:
self.workflow_state.mark_failed(str(e))
return self._handle_processing_error(e)
def _initialize_nlp_pipeline(self):
"""Initialize the Natural Language Processing Pipeline"""
llm_client = LLMClient(
model_name=self.config.get('llm_model', 'gpt-4'),
api_key=self.config.get('llm_api_key'),
temperature=self.config.get('llm_temperature', 0.3)
)
pattern_knowledge_base = PatternKnowledgeBase(
database_url=self.config.get('pattern_db_url')
)
return ArchitecturalConceptExtractor(llm_client, pattern_knowledge_base)
def _initialize_pattern_engine(self):
"""Initialize the Pattern Recognition Engine"""
graph_rag_client = GraphRAGClient(
neo4j_url=self.config.get('neo4j_url'),
neo4j_user=self.config.get('neo4j_user'),
neo4j_password=self.config.get('neo4j_password')
)
embedding_model = EmbeddingModel(
model_name=self.config.get('embedding_model', 'sentence-transformers/all-MiniLM-L6-v2')
)
pattern_matcher = GraphRAGPatternMatcher(graph_rag_client, embedding_model)
pattern_strategy = PatternApplicationStrategy(pattern_matcher, ConflictResolver())
return PatternRecognitionEngine(pattern_matcher, pattern_strategy)
def _compile_final_result(self, problem_description, concepts, patterns, diagrams, documentation):
"""Compile the final result with all generated artifacts"""
final_result = {
'request_summary': {
'original_problem': problem_description,
'processing_timestamp': self.workflow_state.get_timestamp(),
'processing_duration': self.workflow_state.get_duration()
},
'architectural_analysis': {
'extracted_concepts': concepts,
'identified_components': concepts.get('components', []),
'quality_attributes': concepts.get('quality_attributes', []),
'constraints': concepts.get('constraints', [])
},
'pattern_applications': {
'applied_patterns': [p['pattern'].name for p in patterns],
'pattern_details': patterns,
'pattern_rationale': self._extract_pattern_rationale(patterns)
},
'generated_diagrams': {
'diagram_types': list(diagrams.keys()),
'diagrams': diagrams,
'diagram_relationships': self._analyze_diagram_relationships(diagrams)
},
'documentation': {
'arc42_sections': documentation,
'traceability_matrix': self._generate_traceability_matrix(
concepts, patterns, diagrams
)
},
'recommendations': {
'implementation_guidance': self._generate_implementation_guidance(patterns),
'evolution_considerations': self._generate_evolution_considerations(patterns),
'alternative_approaches': self._suggest_alternative_approaches(concepts, patterns)
}
}
return final_result
class WorkflowState:
"""Manages the state of the architecture generation workflow"""
def __init__(self):
self.current_stage = None
self.start_time = None
self.stage_history = []
self.errors = []
self.completed = False
def update_stage(self, stage_name):
"""Update the current processing stage"""
import time
current_time = time.time()
if self.current_stage:
# Record completion of previous stage
self.stage_history.append({
'stage': self.current_stage,
'start_time': self.start_time,
'end_time': current_time,
'duration': current_time - self.start_time
})
self.current_stage = stage_name
self.start_time = current_time
if not self.stage_history: # First stage
self.workflow_start_time = current_time
def mark_completed(self):
"""Mark the workflow as completed"""
import time
current_time = time.time()
if self.current_stage:
self.stage_history.append({
'stage': self.current_stage,
'start_time': self.start_time,
'end_time': current_time,
'duration': current_time - self.start_time
})
self.completed = True
self.completion_time = current_time
def get_duration(self):
"""Get total processing duration"""
if hasattr(self, 'completion_time'):
return self.completion_time - self.workflow_start_time
return None
class LLMClient:
"""Client for interacting with Large Language Models"""
def __init__(self, model_name, api_key, temperature=0.3):
self.model_name = model_name
self.api_key = api_key
self.temperature = temperature
self.client = self._initialize_client()
def generate(self, prompt, max_tokens=2000):
"""Generate response from the language model"""
try:
response = self.client.chat.completions.create(
model=self.model_name,
messages=[
{"role": "system", "content": "You are an expert software architect."},
{"role": "user", "content": prompt}
],
temperature=self.temperature,
max_tokens=max_tokens
)
return response.choices[0].message.content
except Exception as e:
raise LLMProcessingError(f"Failed to generate response: {str(e)}")
def _initialize_client(self):
"""Initialize the LLM client based on model type"""
if 'gpt' in self.model_name.lower():
import openai
return openai.OpenAI(api_key=self.api_key)
elif 'claude' in self.model_name.lower():
import anthropic
return anthropic.Anthropic(api_key=self.api_key)
else:
raise ValueError(f"Unsupported model: {self.model_name}")
class PatternKnowledgeBase:
"""Knowledge base for design patterns and architectural solutions"""
def __init__(self, database_url):
self.database_url = database_url
self.patterns = self._load_patterns()
self.pattern_relationships = self._load_pattern_relationships()
def _load_patterns(self):
"""Load pattern definitions from the knowledge base"""
patterns = {}
# Observer Pattern Definition
observer_pattern = DesignPattern(
name='Observer',
intent='Define a one-to-many dependency between objects so that when one object changes state, all its dependents are notified and updated automatically.',
context='When multiple objects need to be notified of state changes in another object',
structure={
'Subject': 'Maintains list of observers and notifies them of state changes',
'Observer': 'Interface for objects that should be notified of changes',
'ConcreteSubject': 'Stores state and notifies observers when state changes',
'ConcreteObserver': 'Implements Observer interface and maintains reference to ConcreteSubject'
},
consequences={
'benefits': ['Loose coupling between subject and observers', 'Dynamic relationships'],
'drawbacks': ['Potential for unexpected updates', 'Memory leaks if observers not properly removed']
},
quality_attributes=['maintainability', 'flexibility', 'extensibility']
)
patterns['Observer'] = observer_pattern
# Strategy Pattern Definition
strategy_pattern = DesignPattern(
name='Strategy',
intent='Define a family of algorithms, encapsulate each one, and make them interchangeable.',
context='When you need to use different variants of an algorithm within an object',
structure={
'Strategy': 'Interface for all concrete strategies',
'ConcreteStrategy': 'Implements specific algorithm using Strategy interface',
'Context': 'Maintains reference to Strategy object and delegates algorithm execution'
},
consequences={
'benefits': ['Algorithms can be switched at runtime', 'Easy to add new algorithms'],
'drawbacks': ['Increased number of classes', 'Clients must be aware of different strategies']
},
quality_attributes=['flexibility', 'extensibility', 'maintainability']
)
patterns['Strategy'] = strategy_pattern
# MVC Pattern Definition
mvc_pattern = DesignPattern(
name='MVC',
intent='Separate the representation of information from the user interaction with it.',
context='When building user interfaces that need to separate concerns',
structure={
'Model': 'Manages data and business logic',
'View': 'Handles presentation and user interface',
'Controller': 'Manages user input and coordinates between Model and View'
},
consequences={
'benefits': ['Separation of concerns', 'Multiple views of same data', 'Easier testing'],
'drawbacks': ['Increased complexity for simple applications', 'Potential for tight coupling']
},
quality_attributes=['maintainability', 'testability', 'reusability']
)
patterns['MVC'] = mvc_pattern
return patterns
def get_pattern(self, pattern_name):
"""Retrieve a specific pattern by name"""
return self.patterns.get(pattern_name)
def get_all_patterns(self):
"""Retrieve all patterns in the knowledge base"""
return list(self.patterns.values())
class DesignPattern:
"""Represents a design pattern with all its characteristics"""
def __init__(self, name, intent, context, structure, consequences, quality_attributes):
self.name = name
self.intent = intent
self.context = context
self.structure = structure
self.consequences = consequences
self.quality_attributes = quality_attributes
self.roles = self._extract_roles_from_structure()
def _extract_roles_from_structure(self):
"""Extract pattern roles from structure definition"""
roles = []
for role_name, role_description in self.structure.items():
role = PatternRole(
name=role_name,
description=role_description,
pattern_name=self.name
)
roles.append(role)
return roles
def get_roles(self):
"""Get all roles defined in this pattern"""
return self.roles
def get_role_names(self):
"""Get names of all roles in this pattern"""
return [role.name for role in self.roles]
def get_scope(self):
"""Get the scope of this pattern (class, object, architectural)"""
# This would be determined based on pattern characteristics
if self.name in ['Observer', 'Strategy', 'Command']:
return 'object'
elif self.name in ['MVC', 'Layered', 'Microservices']:
return 'architectural'
else:
return 'class'
class PatternRole:
"""Represents a role within a design pattern"""
def __init__(self, name, description, pattern_name):
self.name = name
self.description = description
self.pattern_name = pattern_name
self.responsibilities = self._extract_responsibilities()
self.interfaces = self._extract_interfaces()
self.behavioral_requirements = self._extract_behavioral_requirements()
def _extract_responsibilities(self):
"""Extract responsibilities from role description"""
# This would use NLP to extract key responsibilities
# For now, using simple keyword-based extraction
responsibilities = []
description_lower = self.description.lower()
if 'maintain' in description_lower:
responsibilities.append('state_management')
if 'notify' in description_lower:
responsibilities.append('notification')
if 'implement' in description_lower:
responsibilities.append('algorithm_implementation')
if 'coordinate' in description_lower:
responsibilities.append('coordination')
return responsibilities
def get_responsibilities(self):
"""Get the responsibilities of this role"""
return self.responsibilities
def get_required_interfaces(self):
"""Get the interfaces required by this role"""
return self.interfaces
def get_behavioral_requirements(self):
"""Get the behavioral requirements for this role"""
return self.behavioral_requirements
def get_component_type(self):
"""Determine the appropriate component type for this role"""
if 'interface' in self.name.lower():
return 'interface'
elif 'abstract' in self.description.lower():
return 'abstract_class'
else:
return 'class'
This comprehensive implementation demonstrates how all the components of the intelligent architecture agent work together to process natural language problem descriptions and generate complete architectural solutions. The system maintains modularity and extensibility while providing robust error handling and workflow management.
INTEGRATION AND WORKFLOW
The integration and workflow management represents the orchestration layer that coordinates all components of the intelligent architecture agent. This layer ensures that information flows correctly between components, maintains consistency across different artifacts, and provides a seamless user experience.
The workflow follows a pipeline architecture where each stage processes the output of the previous stage and provides input to the next stage. The system maintains intermediate results at each stage, allowing for iterative refinement and enabling users to provide feedback or modifications at specific points in the process.
The integration layer also handles cross-cutting concerns such as logging, monitoring, error handling, and performance optimization. It maintains audit trails of all processing steps, enabling traceability from the final architectural artifacts back to the original problem description and the reasoning behind specific design decisions.
The system provides multiple integration points for external tools and systems, including APIs for programmatic access, webhook integrations for continuous integration pipelines, and export capabilities for popular architectural modeling tools.
Here is a detailed implementation of the integration and workflow management:
class ArchitectureAgentWorkflow:
"""Manages the complete workflow for architecture generation"""
def __init__(self, orchestrator, persistence_layer, notification_service):
self.orchestrator = orchestrator
self.persistence = persistence_layer
self.notifications = notification_service
self.workflow_plugins = []
self.validation_rules = ValidationRuleEngine()
def execute_workflow(self, request):
"""Execute the complete architecture generation workflow"""
workflow_id = self._generate_workflow_id()
try:
# Initialize workflow context
context = WorkflowContext(
workflow_id=workflow_id,
request=request,
user_id=request.get('user_id'),
preferences=request.get('preferences', {})
)
# Stage 1: Request Validation and Preprocessing
validated_request = self._validate_and_preprocess_request(context)
# Stage 2: Core Processing
processing_result = self.orchestrator.process_architecture_request(
validated_request['problem_description'],
validated_request.get('user_preferences')
)
# Stage 3: Result Validation and Enhancement
validated_result = self._validate_and_enhance_result(
processing_result, context
)
# Stage 4: Artifact Generation and Export
final_artifacts = self._generate_final_artifacts(
validated_result, context
)
# Stage 5: Persistence and Notification
self._persist_workflow_result(workflow_id, final_artifacts, context)
self._send_completion_notification(workflow_id, final_artifacts, context)
return {
'workflow_id': workflow_id,
'status': 'completed',
'artifacts': final_artifacts,
'metadata': context.get_metadata()
}
except Exception as e:
error_result = self._handle_workflow_error(workflow_id, e, context)
return error_result
def _validate_and_preprocess_request(self, context):
"""Validate and preprocess the incoming request"""
request = context.request
# Validate required fields
if not request.get('problem_description'):
raise ValidationError("Problem description is required")
# Preprocess problem description
preprocessed_description = self._preprocess_problem_description(
request['problem_description']
)
# Apply user preferences and constraints
processed_preferences = self._process_user_preferences(
request.get('preferences', {}), context
)
# Validate against business rules
self.validation_rules.validate_request(request, context)
validated_request = {
'problem_description': preprocessed_description,
'user_preferences': processed_preferences,
'processing_options': request.get('processing_options', {}),
'output_formats': request.get('output_formats', ['plantuml', 'arc42'])
}
# Log validation completion
context.log_stage_completion('request_validation', validated_request)
return validated_request
def _validate_and_enhance_result(self, processing_result, context):
"""Validate and enhance the processing result"""
# Validate diagram consistency
diagram_validation = self._validate_diagram_consistency(
processing_result['generated_diagrams']
)
if not diagram_validation['valid']:
# Attempt automatic correction
corrected_diagrams = self._correct_diagram_issues(
processing_result['generated_diagrams'],
diagram_validation['issues']
)
processing_result['generated_diagrams'] = corrected_diagrams
# Validate pattern applications
pattern_validation = self._validate_pattern_applications(
processing_result['pattern_applications']
)
# Enhance with additional metadata
enhanced_result = self._enhance_with_metadata(processing_result, context)
# Generate quality metrics
quality_metrics = self._calculate_quality_metrics(enhanced_result)
enhanced_result['quality_metrics'] = quality_metrics
context.log_stage_completion('result_validation', enhanced_result)
return enhanced_result
def _generate_final_artifacts(self, validated_result, context):
"""Generate final artifacts in requested formats"""
artifacts = {}
output_formats = context.request.get('output_formats', ['plantuml', 'arc42'])
# Generate PlantUML artifacts
if 'plantuml' in output_formats:
plantuml_artifacts = self._generate_plantuml_artifacts(validated_result)
artifacts['plantuml'] = plantuml_artifacts
# Generate Arc42 documentation
if 'arc42' in output_formats:
arc42_artifacts = self._generate_arc42_artifacts(validated_result)
artifacts['arc42'] = arc42_artifacts
# Generate additional formats if requested
if 'json' in output_formats:
json_artifacts = self._generate_json_artifacts(validated_result)
artifacts['json'] = json_artifacts
if 'pdf' in output_formats:
pdf_artifacts = self._generate_pdf_artifacts(validated_result, artifacts)
artifacts['pdf'] = pdf_artifacts
# Generate summary report
summary_report = self._generate_summary_report(validated_result, context)
artifacts['summary'] = summary_report
context.log_stage_completion('artifact_generation', artifacts)
return artifacts
class WorkflowContext:
"""Maintains context and state throughout the workflow"""
def __init__(self, workflow_id, request, user_id=None, preferences=None):
self.workflow_id = workflow_id
self.request = request
self.user_id = user_id
self.preferences = preferences or {}
self.stage_logs = []
self.metadata = {}
self.start_time = time.time()
def log_stage_completion(self, stage_name, stage_result):
"""Log completion of a workflow stage"""
stage_log = {
'stage': stage_name,
'timestamp': time.time(),
'duration': time.time() - self.start_time,
'result_summary': self._summarize_stage_result(stage_result)
}
self.stage_logs.append(stage_log)
def get_metadata(self):
"""Get workflow metadata"""
total_duration = time.time() - self.start_time
metadata = {
'workflow_id': self.workflow_id,
'user_id': self.user_id,
'start_time': self.start_time,
'total_duration': total_duration,
'stage_count': len(self.stage_logs),
'stages': self.stage_logs
}
return metadata
def _summarize_stage_result(self, stage_result):
"""Create a summary of stage result for logging"""
if isinstance(stage_result, dict):
return {
'keys': list(stage_result.keys()),
'size': len(stage_result)
}
else:
return {
'type': type(stage_result).__name__,
'size': len(str(stage_result))
}
class ValidationRuleEngine:
"""Validates requests and results against business rules"""
def __init__(self):
self.rules = self._load_validation_rules()
def validate_request(self, request, context):
"""Validate incoming request against business rules"""
errors = []
# Rule 1: Problem description length
problem_desc = request.get('problem_description', '')
if len(problem_desc) < 50:
errors.append("Problem description too short (minimum 50 characters)")
elif len(problem_desc) > 10000:
errors.append("Problem description too long (maximum 10000 characters)")
# Rule 2: User preferences validation
preferences = request.get('preferences', {})
if preferences:
pref_errors = self._validate_preferences(preferences)
errors.extend(pref_errors)
# Rule 3: Output format validation
output_formats = request.get('output_formats', [])
valid_formats = ['plantuml', 'arc42', 'json', 'pdf']
invalid_formats = [f for f in output_formats if f not in valid_formats]
if invalid_formats:
errors.append(f"Invalid output formats: {invalid_formats}")
if errors:
raise ValidationError(f"Request validation failed: {'; '.join(errors)}")
def _validate_preferences(self, preferences):
"""Validate user preferences"""
errors = []
# Validate pattern preferences
if 'preferred_patterns' in preferences:
preferred_patterns = preferences['preferred_patterns']
if not isinstance(preferred_patterns, list):
errors.append("Preferred patterns must be a list")
else:
valid_patterns = ['Observer', 'Strategy', 'Factory', 'MVC', 'Command']
invalid_patterns = [p for p in preferred_patterns if p not in valid_patterns]
if invalid_patterns:
errors.append(f"Invalid pattern preferences: {invalid_patterns}")
# Validate quality attribute priorities
if 'quality_priorities' in preferences:
quality_priorities = preferences['quality_priorities']
if not isinstance(quality_priorities, dict):
errors.append("Quality priorities must be a dictionary")
return errors
def _load_validation_rules(self):
"""Load validation rules configuration"""
rules = {
'min_description_length': 50,
'max_description_length': 10000,
'valid_output_formats': ['plantuml', 'arc42', 'json', 'pdf'],
'valid_patterns': ['Observer', 'Strategy', 'Factory', 'MVC', 'Command'],
'max_preferred_patterns': 5
}
return rules
class ArtifactGenerator:
"""Generates various output artifacts from processing results"""
def __init__(self):
self.generators = {
'plantuml': PlantUMLArtifactGenerator(),
'arc42': Arc42ArtifactGenerator(),
'json': JSONArtifactGenerator(),
'pdf': PDFArtifactGenerator()
}
def generate_plantuml_artifacts(self, processing_result):
"""Generate PlantUML artifacts"""
plantuml_generator = self.generators['plantuml']
artifacts = {}
diagrams = processing_result['generated_diagrams']['diagrams']
for diagram_type, diagram_data in diagrams.items():
artifact = plantuml_generator.generate_artifact(diagram_type, diagram_data)
artifacts[diagram_type] = artifact
# Generate combined artifact with all diagrams
combined_artifact = plantuml_generator.generate_combined_artifact(diagrams)
artifacts['combined'] = combined_artifact
return artifacts
def generate_arc42_artifacts(self, processing_result):
"""Generate Arc42 documentation artifacts"""
arc42_generator = self.generators['arc42']
documentation = processing_result['documentation']['arc42_sections']
artifacts = {}
# Generate individual sections
for section_name, section_content in documentation.items():
artifact = arc42_generator.generate_section_artifact(section_name, section_content)
artifacts[section_name] = artifact
# Generate complete document
complete_document = arc42_generator.generate_complete_document(documentation)
artifacts['complete_document'] = complete_document
return artifacts
def generate_summary_report(self, processing_result, context):
"""Generate executive summary report"""
summary = {
'workflow_summary': {
'workflow_id': context.workflow_id,
'processing_time': context.get_metadata()['total_duration'],
'user_id': context.user_id
},
'problem_analysis': {
'original_problem': context.request['problem_description'][:200] + '...',
'identified_components': len(processing_result['architectural_analysis']['identified_components']),
'quality_attributes': processing_result['architectural_analysis']['quality_attributes']
},
'solution_overview': {
'applied_patterns': processing_result['pattern_applications']['applied_patterns'],
'generated_diagrams': list(processing_result['generated_diagrams']['diagrams'].keys()),
'documentation_sections': list(processing_result['documentation']['arc42_sections'].keys())
},
'quality_assessment': processing_result.get('quality_metrics', {}),
'recommendations': processing_result.get('recommendations', {})
}
return summary
class NotificationService:
"""Handles notifications for workflow completion and errors"""
def __init__(self, config):
self.config = config
self.notification_channels = self._initialize_channels()
def send_completion_notification(self, workflow_id, artifacts, context):
"""Send notification when workflow completes successfully"""
notification = {
'type': 'workflow_completion',
'workflow_id': workflow_id,
'user_id': context.user_id,
'completion_time': time.time(),
'artifacts_generated': list(artifacts.keys()),
'processing_duration': context.get_metadata()['total_duration']
}
self._send_notification(notification, context)
def send_error_notification(self, workflow_id, error, context):
"""Send notification when workflow encounters an error"""
notification = {
'type': 'workflow_error',
'workflow_id': workflow_id,
'user_id': context.user_id,
'error_time': time.time(),
'error_message': str(error),
'stage_completed': len(context.stage_logs)
}
self._send_notification(notification, context)
def _send_notification(self, notification, context):
"""Send notification through configured channels"""
user_preferences = context.preferences.get('notifications', {})
# Email notification
if user_preferences.get('email', True):
self._send_email_notification(notification, context)
# Webhook notification
if user_preferences.get('webhook'):
self._send_webhook_notification(notification, context)
# In-app notification
if user_preferences.get('in_app', True):
self._send_in_app_notification(notification, context)
def _initialize_channels(self):
"""Initialize notification channels"""
channels = {}
if self.config.get('email_service'):
channels['email'] = EmailNotificationChannel(self.config['email_service'])
if self.config.get('webhook_service'):
channels['webhook'] = WebhookNotificationChannel(self.config['webhook_service'])
channels['in_app'] = InAppNotificationChannel()
return channels
This comprehensive integration and workflow implementation demonstrates how the intelligent architecture agent orchestrates all components to provide a seamless and robust architecture generation experience. The system handles validation, error management, artifact generation, and notifications while maintaining full traceability and auditability of the process.
CHALLENGES AND SOLUTIONS
The development and deployment of an intelligent architecture agent presents several significant challenges that require careful consideration and innovative solutions. These challenges span multiple domains including natural language understanding, pattern recognition accuracy, diagram quality assurance, and system scalability.
One of the primary challenges is the ambiguity inherent in natural language descriptions of architectural problems. Users often describe problems using domain-specific terminology, incomplete information, or implicit assumptions that are difficult for automated systems to interpret correctly. The solution involves implementing a multi-stage clarification process where the system identifies ambiguous or incomplete aspects of the problem description and generates targeted questions to gather additional information.
Another significant challenge is ensuring the accuracy and relevance of pattern recommendations. The system must avoid both false positives (recommending inappropriate patterns) and false negatives (missing applicable patterns). This requires sophisticated semantic understanding and context analysis that goes beyond simple keyword matching. The solution employs a combination of semantic similarity analysis, graph-based reasoning, and validation mechanisms that consider multiple factors including quality attributes, system constraints, and pattern interactions.
The quality and consistency of generated diagrams presents another complex challenge. Automated diagram generation must produce visually clear, semantically correct, and professionally formatted diagrams that accurately represent the architectural concepts and applied patterns. The solution involves implementing comprehensive validation rules, layout optimization algorithms, and pattern-specific diagram templates that ensure consistency and quality across different diagram types.
Here is a detailed implementation of the challenge mitigation strategies:
class AmbiguityResolver:
"""Resolves ambiguities in natural language problem descriptions"""
def __init__(self, llm_client, clarification_engine):
self.llm_client = llm_client
self.clarification_engine = clarification_engine
self.ambiguity_patterns = self._load_ambiguity_patterns()
def resolve_ambiguities(self, problem_description, architectural_concepts):
"""Identify and resolve ambiguities in the problem description"""
# Stage 1: Identify potential ambiguities
identified_ambiguities = self._identify_ambiguities(
problem_description, architectural_concepts
)
# Stage 2: Prioritize ambiguities by impact
prioritized_ambiguities = self._prioritize_ambiguities(identified_ambiguities)
# Stage 3: Generate clarification questions
clarification_questions = self._generate_clarification_questions(
prioritized_ambiguities
)
# Stage 4: Attempt automatic resolution where possible
auto_resolved = self._attempt_automatic_resolution(
prioritized_ambiguities, architectural_concepts
)
resolution_result = {
'identified_ambiguities': identified_ambiguities,
'clarification_questions': clarification_questions,
'auto_resolved': auto_resolved,
'requires_user_input': len(clarification_questions) > 0
}
return resolution_result
def _identify_ambiguities(self, description, concepts):
"""Identify potential ambiguities in the problem description"""
ambiguities = []
# Check for vague quantifiers
vague_quantifiers = ['many', 'several', 'some', 'few', 'large', 'small']
for quantifier in vague_quantifiers:
if quantifier in description.lower():
ambiguities.append({
'type': 'vague_quantifier',
'text': quantifier,
'context': self._extract_context(description, quantifier),
'severity': 'medium'
})
# Check for undefined technical terms
technical_terms = self._extract_technical_terms(description)
undefined_terms = self._identify_undefined_terms(technical_terms, concepts)
for term in undefined_terms:
ambiguities.append({
'type': 'undefined_term',
'text': term,
'context': self._extract_context(description, term),
'severity': 'high'
})
# Check for missing quality attributes
if not concepts.get('quality_attributes'):
ambiguities.append({
'type': 'missing_quality_attributes',
'text': 'No explicit quality attributes mentioned',
'context': 'Overall system requirements',
'severity': 'high'
})
# Check for incomplete component relationships
components = concepts.get('components', [])
relationships = concepts.get('relationships', [])
if len(components) > 1 and len(relationships) == 0:
ambiguities.append({
'type': 'missing_relationships',
'text': 'Component relationships not specified',
'context': 'System structure',
'severity': 'medium'
})
return ambiguities
def _generate_clarification_questions(self, ambiguities):
"""Generate targeted questions to resolve ambiguities"""
questions = []
for ambiguity in ambiguities:
if ambiguity['type'] == 'vague_quantifier':
question = self._generate_quantifier_question(ambiguity)
elif ambiguity['type'] == 'undefined_term':
question = self._generate_term_definition_question(ambiguity)
elif ambiguity['type'] == 'missing_quality_attributes':
question = self._generate_quality_attributes_question(ambiguity)
elif ambiguity['type'] == 'missing_relationships':
question = self._generate_relationships_question(ambiguity)
else:
question = self._generate_generic_question(ambiguity)
questions.append(question)
return questions
def _generate_quantifier_question(self, ambiguity):
"""Generate question to clarify vague quantifiers"""
context = ambiguity['context']
quantifier = ambiguity['text']
question = {
'id': f"quantifier_{quantifier}_{hash(context)}",
'type': 'quantifier_clarification',
'question': f"You mentioned '{quantifier}' in the context of '{context}'. "
f"Could you provide a more specific number or range?",
'expected_answer_type': 'numeric_range',
'priority': 'medium'
}
return question
def _attempt_automatic_resolution(self, ambiguities, concepts):
"""Attempt to resolve ambiguities automatically using context and heuristics"""
auto_resolved = []
for ambiguity in ambiguities:
resolution = None
if ambiguity['type'] == 'missing_quality_attributes':
# Infer quality attributes from problem context
inferred_qa = self._infer_quality_attributes(concepts)
if inferred_qa:
resolution = {
'ambiguity': ambiguity,
'resolution': inferred_qa,
'confidence': 0.7,
'method': 'context_inference'
}
elif ambiguity['type'] == 'vague_quantifier':
# Use domain-specific defaults for common quantifiers
default_values = self._get_default_quantifier_values(ambiguity)
if default_values:
resolution = {
'ambiguity': ambiguity,
'resolution': default_values,
'confidence': 0.6,
'method': 'domain_defaults'
}
if resolution:
auto_resolved.append(resolution)
return auto_resolved
class PatternValidationEngine:
"""Validates pattern recommendations for accuracy and relevance"""
def __init__(self, pattern_knowledge_base, validation_rules):
self.pattern_kb = pattern_knowledge_base
self.validation_rules = validation_rules
self.false_positive_detector = FalsePositiveDetector()
self.false_negative_detector = FalseNegativeDetector()
def validate_pattern_recommendations(self, recommended_patterns, architectural_concepts):
"""Validate the accuracy and relevance of pattern recommendations"""
validation_result = {
'validated_patterns': [],
'rejected_patterns': [],
'missing_patterns': [],
'validation_warnings': []
}
# Check for false positives
for pattern in recommended_patterns:
fp_analysis = self.false_positive_detector.analyze_pattern(
pattern, architectural_concepts
)
if fp_analysis['is_false_positive']:
validation_result['rejected_patterns'].append({
'pattern': pattern,
'rejection_reason': fp_analysis['reason'],
'confidence': fp_analysis['confidence']
})
else:
validation_result['validated_patterns'].append(pattern)
# Check for false negatives
fn_analysis = self.false_negative_detector.analyze_missing_patterns(
recommended_patterns, architectural_concepts
)
validation_result['missing_patterns'] = fn_analysis['potentially_missing']
# Generate validation warnings
warnings = self._generate_validation_warnings(
validation_result['validated_patterns'], architectural_concepts
)
validation_result['validation_warnings'] = warnings
return validation_result
def _generate_validation_warnings(self, patterns, concepts):
"""Generate warnings about potential issues with pattern selection"""
warnings = []
# Check for pattern overuse
if len(patterns) > 5:
warnings.append({
'type': 'pattern_overuse',
'message': f"High number of patterns ({len(patterns)}) may increase complexity",
'severity': 'medium',
'recommendation': 'Consider if all patterns are necessary'
})
# Check for conflicting patterns
conflicts = self._detect_pattern_conflicts(patterns)
for conflict in conflicts:
warnings.append({
'type': 'pattern_conflict',
'message': f"Potential conflict between {conflict['pattern1']} and {conflict['pattern2']}",
'severity': 'high',
'recommendation': conflict['resolution_suggestion']
})
# Check for missing complementary patterns
missing_complements = self._detect_missing_complements(patterns, concepts)
for complement in missing_complements:
warnings.append({
'type': 'missing_complement',
'message': f"Consider adding {complement['pattern']} to complement {complement['existing_pattern']}",
'severity': 'low',
'recommendation': complement['rationale']
})
return warnings
class DiagramQualityAssurance:
"""Ensures quality and consistency of generated diagrams"""
def __init__(self):
self.quality_rules = self._load_quality_rules()
self.layout_optimizer = LayoutOptimizer()
self.consistency_checker = ConsistencyChecker()
def validate_diagram_quality(self, diagrams, architectural_concepts):
"""Validate the quality of generated diagrams"""
quality_report = {
'overall_score': 0.0,
'diagram_scores': {},
'quality_issues': [],
'improvement_suggestions': []
}
total_score = 0.0
for diagram_type, diagram_data in diagrams.items():
diagram_quality = self._validate_single_diagram(
diagram_type, diagram_data, architectural_concepts
)
quality_report['diagram_scores'][diagram_type] = diagram_quality
total_score += diagram_quality['score']
if diagram_quality['issues']:
quality_report['quality_issues'].extend(diagram_quality['issues'])
if diagram_quality['suggestions']:
quality_report['improvement_suggestions'].extend(diagram_quality['suggestions'])
quality_report['overall_score'] = total_score / len(diagrams) if diagrams else 0.0
return quality_report
def _validate_single_diagram(self, diagram_type, diagram_data, concepts):
"""Validate quality of a single diagram"""
validation_result = {
'score': 0.0,
'issues': [],
'suggestions': []
}
# Check PlantUML syntax validity
syntax_validation = self._validate_plantuml_syntax(diagram_data['plantuml_code'])
if not syntax_validation['valid']:
validation_result['issues'].append({
'type': 'syntax_error',
'severity': 'high',
'message': syntax_validation['error_message']
})
return validation_result # Cannot proceed with invalid syntax
# Check diagram completeness
completeness_score = self._check_diagram_completeness(diagram_data, concepts)
# Check layout quality
layout_score = self._check_layout_quality(diagram_data)
# Check consistency with other diagrams
consistency_score = self._check_diagram_consistency(diagram_type, diagram_data)
# Check adherence to conventions
convention_score = self._check_convention_adherence(diagram_type, diagram_data)
# Calculate overall score
validation_result['score'] = (
0.3 * completeness_score +
0.25 * layout_score +
0.25 * consistency_score +
0.2 * convention_score
)
# Generate suggestions for improvement
if completeness_score < 0.8:
validation_result['suggestions'].append({
'type': 'completeness',
'message': 'Consider adding missing architectural elements',
'priority': 'medium'
})
if layout_score < 0.7:
validation_result['suggestions'].append({
'type': 'layout',
'message': 'Diagram layout could be improved for better readability',
'priority': 'low'
})
return validation_result
def optimize_diagram_layout(self, diagram_data):
"""Optimize diagram layout for better readability"""
plantuml_code = diagram_data['plantuml_code']
# Apply layout optimization rules
optimized_code = self.layout_optimizer.optimize_layout(plantuml_code)
# Add layout directives if needed
if self._needs_layout_directives(optimized_code):
optimized_code = self._add_layout_directives(optimized_code)
# Optimize element positioning
optimized_code = self._optimize_element_positioning(optimized_code)
return {
'plantuml_code': optimized_code,
'optimization_applied': True,
'optimization_notes': self._generate_optimization_notes(plantuml_code, optimized_code)
}
class ScalabilityManager:
"""Manages system scalability and performance optimization"""
def __init__(self, config):
self.config = config
self.cache_manager = CacheManager(config.get('cache_config', {}))
self.load_balancer = LoadBalancer(config.get('load_balancer_config', {}))
self.resource_monitor = ResourceMonitor()
def handle_high_load_scenario(self, request_queue):
"""Handle high load scenarios with appropriate scaling strategies"""
current_load = self.resource_monitor.get_current_load()
if current_load > 0.8: # High load threshold
# Apply load reduction strategies
self._apply_load_reduction_strategies(request_queue)
# Scale resources if possible
scaling_result = self._attempt_resource_scaling()
# Implement request prioritization
prioritized_queue = self._prioritize_requests(request_queue)
return {
'load_reduction_applied': True,
'scaling_result': scaling_result,
'prioritized_queue': prioritized_queue
}
return {'load_reduction_applied': False}
def _apply_load_reduction_strategies(self, request_queue):
"""Apply strategies to reduce system load"""
# Enable aggressive caching
self.cache_manager.enable_aggressive_caching()
# Reduce processing complexity for non-critical requests
for request in request_queue:
if request.get('priority', 'normal') == 'low':
request['processing_options'] = {
'simplified_analysis': True,
'reduced_pattern_search': True,
'basic_diagrams_only': True
}
# Implement request batching where possible
batched_requests = self._batch_similar_requests(request_queue)
return batched_requests
def optimize_for_performance(self, processing_pipeline):
"""Optimize processing pipeline for better performance"""
optimizations = []
# Implement parallel processing where possible
if self._can_parallelize(processing_pipeline):
parallel_pipeline = self._create_parallel_pipeline(processing_pipeline)
optimizations.append('parallel_processing')
# Add caching layers
cached_pipeline = self._add_caching_layers(processing_pipeline)
optimizations.append('caching_layers')
# Optimize database queries
optimized_queries = self._optimize_database_queries(processing_pipeline)
optimizations.append('query_optimization')
return {
'optimized_pipeline': cached_pipeline,
'optimizations_applied': optimizations,
'expected_performance_gain': self._estimate_performance_gain(optimizations)
}
These powerful challenge mitigation strategies demonstrate how the intelligent architecture agent addresses the key technical and operational challenges inherent in automated architecture generation. The solutions provide robust error handling, quality assurance, and scalability while maintaining the system's core functionality and user experience.
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