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docs: update ARCHITECTURAL_ANALYSIS.md to reflect all completed refactoring (service extractions, scoring persistence, logging, N+1 fixes)

2026-02-20 12:55:26 +01:00

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Aegis — Deep Architectural Analysis

Author: Automated architecture review
Date: February 11, 2026 (updated February 19, 2026)
Scope: Backend (FastAPI/Python), Frontend (React/TypeScript), Infrastructure (Docker)

Note: Sections marked with ✅ reflect changes implemented since the initial analysis.

Current Architecture
Coupling Analysis
Business Logic vs Infrastructure Separation
SOLID Evaluation
Architectural Risks
Refactor Proposal Towards Clean Architecture
Executive Summary

1. Current Architecture

1.1. Classification: Layered Monolith with Incomplete Service Layer

Aegis follows a layered monolithic architecture deployed as two containers (backend + frontend) with a partial and inconsistent level of separation. It is not Clean Architecture, nor Hexagonal, nor microservices.

┌─────────────────────────────────────────────────┐
│                   FRONTEND                       │
│         React 19 + TypeScript + Vite             │
│  ┌──────────┐  ┌──────────┐  ┌───────────────┐  │
│  │  Pages   │→ │ API Layer│→ │ Axios Client  │  │
│  │(21 pages)│  │(22 mods) │  │(HttpOnly JWT) │  │
│  └──────────┘  └──────────┘  └───────────────┘  │
└────────────────────────┬────────────────────────┘
                         │ HTTP/REST
┌────────────────────────▼────────────────────────┐
│                   BACKEND                        │
│              FastAPI + SQLAlchemy                 │
│                                                  │
│  ┌─────────────────────────────────────────────┐ │
│  │              Router Layer (21 routers)       │ │
│  │  Contains: validation, queries, partial     │ │
│  │  business logic, serialization, auditing    │ │
│  └────────┬──────────────────┬─────────────────┘ │
│           │                  │                    │
│  ┌────────▼───────┐  ┌──────▼──────────────────┐ │
│  │ Service Layer  │  │   Direct DB Access       │ │
│  │ (20 services)  │  │   (SQLAlchemy queries    │ │
│  │ Partial: only  │  │    inside routers)       │ │
│  │ for workflows  │  │                          │ │
│  └────────┬───────┘  └──────┬──────────────────┘ │
│           │                  │                    │
│  ┌────────▼──────────────────▼─────────────────┐ │
│  │          Model Layer (18 models)             │ │
│  │     SQLAlchemy ORM — Anemic Domain Models    │ │
│  └────────────────────┬────────────────────────┘ │
│                       │                          │
│  ┌────────────────────▼────────────────────────┐ │
│  │          Database Layer                      │ │
│  │  PostgreSQL + MinIO (evidence storage)       │ │
│  └─────────────────────────────────────────────┘ │
└──────────────────────────────────────────────────┘

1.2. Actual Distribution of Responsibilities

Layer	Files	Actual Responsibility
Routers	21 files	✅ Thin HTTP adapters — auth, param parsing, response formatting. Delegate to services.
Services	30+ files	✅ All business logic, query orchestration, domain validation. Framework-agnostic.
Domain	8+ files	✅ Pure entities, value objects, ports, errors. Zero framework imports.
Infrastructure	5+ files	✅ Repository implementations, Redis client, mappers.
Models	19 files	ORM table definitions — persistence mapping only
Schemas	10 files	Pydantic DTOs for request/response
Database	1 file	Session factory and `get_db()` generator

1.3. ✅ Consistent Delegation Pattern (was: Two Coexisting Patterns)

Update (Feb 19): The "split architectural personality" has been resolved. All major routers now follow the same pattern:

Pattern — Router-delegates-to-Service:
Routers are thin HTTP adapters that parse parameters, authenticate, and delegate to framework-agnostic services:

# threat_actors.py — thin adapter, all logic in service
@router.get("/{actor_id}")
def get_threat_actor(actor_id: str, db=Depends(get_db), current_user=Depends(get_current_user)):
    return get_actor_detail(db, actor_id)

Extracted services: coverage_report_service, metrics_query_service, compliance_service, detection_rule_service, threat_actor_service, test_crud_service, evidence_service, campaign_crud_service, scoring_config_service.

Remaining: users.py, audit.py, data_sources.py, heatmap.py still have direct queries. These are lower priority since they are simpler or already partially extracted.

2. Coupling Analysis

2.1. Coupling Matrix

              Routers    Services    Models    Database    Schemas    Config
Routers         —         MEDIUM     HIGH      HIGH        HIGH      LOW
Services       LOW          —        HIGH      HIGH        NONE      MEDIUM
Models         NONE       NONE        —        HIGH        NONE      NONE
Schemas        NONE       NONE       LOW        —          NONE      NONE
Database       NONE       NONE       NONE       —          NONE      LOW

2.2. Router ↔ Model — ✅ LARGELY RESOLVED (was HIGH COUPLING)

Update (Feb 19): Most routers no longer import ORM models or execute queries directly. Only 4 out of 21 routers still have direct DB access:

Router	Status	Detail
`techniques.py`	✅ Extracted	Uses `SATechniqueRepository` via dependency injection
`reports.py`	✅ Extracted	Delegates to `coverage_report_service`
`metrics.py`	✅ Extracted	Delegates to `metrics_query_service`
`compliance.py`	✅ Extracted	Delegates to `compliance_service`
`detection_rules.py`	✅ Extracted	Delegates to `detection_rule_service`
`threat_actors.py`	✅ Extracted	Delegates to `threat_actor_service`
`tests.py`	✅ Extracted	Delegates to `test_crud_service` + `test_workflow_service`
`evidence.py`	✅ Extracted	Delegates to `evidence_service`
`campaigns.py`	✅ Extracted	Delegates to `campaign_crud_service`
`users.py`	Remaining	Direct queries (simple CRUD)
`audit.py`	Remaining	Direct queries (read-only list)
`data_sources.py`	Remaining	Direct queries
`heatmap.py`	Remaining	Complex queries (partially extracted via `heatmap_service`)

2.3. Router ↔ Database — HIGH COUPLING

All routers receive db: Session = Depends(get_db) and operate with the SQLAlchemy session directly. This means:

Routers know the ORM (db.query, db.add, db.commit, joinedload)
Routers handle transactions implicitly
There is no persistence abstraction — migrating from SQLAlchemy to another ORM or raw queries would require rewriting all routers

2.4. Service ↔ Model/Database — HIGH COUPLING

Services also access SQLAlchemy directly:

# scoring_service.py
all_tests = db.query(Test).filter(Test.technique_id == technique.id).all()

# notification_service.py
notif = db.query(Notification).filter(...).first()

Services do not use repositories or abstractions — they are essentially functions that orchestrate queries and logic.

2.5. Service ↔ Service — MEDIUM COUPLING

Inter-service coupling exists:

test_workflow_service → audit_service + notification_service
scoring_service reads from settings directly (mutable global config)
campaign_scheduler_service → campaign_service

There is no dependency injection between services — everything is direct imports.

2.6. Service ↔ Framework — ✅ RESOLVED (was HIGH COUPLING)

~~Domain services import HTTPException from FastAPI.~~

Update (Feb 18): test_workflow_service.py now raises domain exceptions (InvalidOperationError, InvalidStateTransition) from app.domain.exceptions. The middleware/error_handler.py maps these to HTTP responses automatically. Services no longer import HTTPException.

# Current: domain/errors.py exceptions mapped by middleware
raise InvalidStateTransition(current_state=..., target_state=..., entity_type="Test")
# middleware/error_handler.py → 400 Bad Request automatically

2.7. Frontend ↔ Backend — LOW COUPLING (Correct)

Communication is via REST API with aligned but independent types (types/models.ts vs schemas/*.py). The frontend uses Axios with interceptors — good decoupling.

3. Business Logic vs Infrastructure Separation

3.1. Diagnosis: ✅ MOSTLY RESOLVED (was INSUFFICIENT SEPARATION)

Update (Feb 19): All major routers have been refactored to delegate to framework-agnostic services.

Aspect	Status	Detail
Workflow logic	✅ WELL SEPARATED	`test_workflow_service.py` encapsulates the state machine with domain exceptions
Scoring	✅ WELL SEPARATED	`scoring_service.py` reads weights from DB via `scoring_config_service.py` (no more mutable global state)
Test CRUD	✅ SEPARATED	`test_crud_service.py` handles all CRUD, validation, and permission checks with domain exceptions
Report generation	✅ SEPARATED	`coverage_report_service.py` handles query aggregation and CSV building (N+1 fixed)
Metrics	✅ SEPARATED	`metrics_query_service.py` handles dashboard aggregation queries
Compliance	✅ SEPARATED	`compliance_service.py` handles framework analysis and gap detection
Detection rules	✅ SEPARATED	`detection_rule_service.py` handles queries, auto-association, and evaluation
Threat actors	✅ SEPARATED	`threat_actor_service.py` handles queries, coverage, and gap analysis (N+1 fixed)
Evidence	✅ SEPARATED	`evidence_service.py` handles permission validation and queries with domain exceptions
Campaigns	✅ SEPARATED	`campaign_crud_service.py` handles CRUD, lifecycle, and scheduling
Heatmap/visualization	PARTIAL	`heatmap_service.py` exists but router still has some logic
Data import	WELL SEPARATED	The 8 import services are correctly isolated
Notifications	WELL SEPARATED	`notification_service.py` encapsulates all logic
Auditing	WELL SEPARATED	`audit_service.py` is a pure `log_action()` function

3.2. Anemic Model (Anti-pattern)

SQLAlchemy models are purely declarative — they have no business methods:

# models/test.py — columns only, zero behavior
class Test(Base):
    __tablename__ = "tests"
    id = Column(UUID, primary_key=True)
    state = Column(Enum(TestState))
    # ... more columns
    # Missing: can_transition(), validate(), calculate_score()

Logic that should be in domain models (business validations, state transitions, calculations) is scattered across routers and services.

3.3. Infrastructure Bleeding Into Logic

Infrastructure	Where It Appears Inappropriately
`SQLAlchemy Session`	Inside domain services (scoring, workflow, notifications)
`FastAPI HTTPException`	Inside domain services (test_workflow_service)
`MinIO/boto3`	`storage.py` is well isolated, but called from routers directly
`APScheduler`	Directly coupled in `jobs/mitre_sync_job.py` with `SessionLocal()`

4. SOLID Evaluation

4.1. Single Responsibility Principle (SRP) — ✅ MOSTLY COMPLIANT (was PARTIAL VIOLATION)

Update (Feb 19): Fat routers have been slimmed. Each router is now a thin HTTP adapter.

Component	Compliant?	Detail
`heatmap.py` (router)	PARTIAL	Still has some inline logic; `heatmap_service` exists but not fully extracted
`reports.py` (router)	✅ YES	Thin adapter → `coverage_report_service`
`tests.py` (router)	✅ YES	Thin adapter → `test_crud_service` + `test_workflow_service`
`campaigns.py` (router)	✅ YES	Thin adapter → `campaign_crud_service`
`evidence.py` (router)	✅ YES	Thin adapter → `evidence_service`
`scoring_service.py`	✅ YES	Reads weights from `scoring_config_service` (DB-backed, not mutable settings)
`test_workflow_service.py`	✅ YES	Single responsibility: test state machine
`notification_service.py`	✅ YES	Single responsibility: notification management
`audit_service.py`	✅ YES	Single responsibility: audit logging

Verdict: All major routers now comply with SRP. Only heatmap.py and a few minor routers have remaining inline logic.

4.2. Open/Closed Principle (OCP) — ✅ PARTIALLY RESOLVED (was VIOLATION)

Update (Feb 19):

Scoring weights: ✅ Resolved — Weights are now persisted in the scoring_config DB table via scoring_config_service.py. The ScoringWeights value object validates invariants (sum = 100, non-negative). No more mutable global settings.
Heatmap layers: Each heatmap type is a separate endpoint with hardcoded logic. Adding a new layer type requires modifying the router.
Import services: Each data source is a separate service without a common interface. Adding a new source requires creating a new service AND modifying data_sources.py and system.py.
Test states: The state machine is well defined in VALID_TRANSITIONS, but adding a new state requires modifying the dictionary AND potentially all services that read TestState.

4.3. Liskov Substitution Principle (LSP) — N/A (Partial)

There is no significant inheritance or polymorphism in the backend. Services are functions, not classes. There are no interfaces or abstract classes. Does not directly apply, but the absence of formal contracts (protocols/ABCs) is a symptom of not being designed for extensibility.

4.4. Interface Segregation Principle (ISP) — ✅ PARTIALLY RESOLVED (was VIOLATION)

Update (Feb 19):

✅ Protocol interfaces exist for TechniqueRepository and TestRepository in domain/ports/repositories/.
Services expose focused functions per module (e.g., threat_actor_service exposes 4 functions, each for one use case).
The Settings object is still monolithic but scoring weights have been extracted to a dedicated DB table with a focused service interface.

4.5. Dependency Inversion Principle (DIP) — ✅ PARTIALLY RESOLVED (was SEVERE VIOLATION)

Update (Feb 18): Protocol interfaces and abstractions now exist:

# domain/ports/repositories/ — Protocol interfaces
class TechniqueRepository(Protocol):
    def find_by_id(self, technique_id: UUID) -> TechniqueEntity | None: ...
    def save(self, technique: TechniqueEntity) -> TechniqueEntity: ...

# dependencies/repositories.py — FastAPI Depends() wiring
def get_technique_repository(db=Depends(get_db)) -> SATechniqueRepository: ...

Domain layer has zero framework imports (no FastAPI, no SQLAlchemy).
Repository ports define contracts; infrastructure implements them.
test_workflow_service.py now uses domain exceptions instead of HTTPException.
UnitOfWork manages transactions.

Remaining: Some services still use direct imports for audit_service, notification_service. Full DIP adoption is incremental.

5. Architectural Risks

5.1. ✅ RESOLVED: God Routers (was CRITICAL RISK)

Update (Feb 19): All critical "fat routers" have been refactored to thin HTTP adapters:

Router	Before	After	Service
`tests.py`	664 lines	~300 lines (workflow endpoints unchanged)	`test_crud_service.py`
`campaigns.py`	~400+ lines	~200 lines	`campaign_crud_service.py`
`reports.py`	273 lines	~100 lines	`coverage_report_service.py`
`compliance.py`	~350+ lines	~100 lines	`compliance_service.py`
`metrics.py`	~250 lines	~80 lines	`metrics_query_service.py`
`detection_rules.py`	374 lines	~130 lines	`detection_rule_service.py`
`threat_actors.py`	312 lines	~100 lines	`threat_actor_service.py`
`evidence.py`	367 lines	~200 lines	`evidence_service.py`

Remaining: heatmap.py still has inline logic (~528 lines). Lower priority since it's already partially extracted to heatmap_service.

5.2. CRITICAL RISK: In-Memory Token Blacklist ✅ RESOLVED

Update (Feb 18): The token blacklist is now Redis-backed via infrastructure/redis_client.py. Tokens are stored with TTL matching expiration. Shared across all workers and survives restarts.

5.3. ✅ RESOLVED: Mutable Settings at Runtime (was HIGH RISK)

Update (Feb 19): Scoring weights are now persisted in the scoring_config database table via scoring_config_service.py. The PATCH /scores/config endpoint writes to the DB instead of mutating the settings object. The ScoringWeights value object validates that weights sum to 100 and are non-negative.

# scoring_config_service.py — DB-backed, validated, persistent
def update_scoring_weights(db: Session, *, tests=None, ...) -> dict:
    new = ScoringWeights(tests=..., ...)  # validates invariants
    row = db.query(ScoringConfig).first()
    ...
    db.commit()

✅ Changes survive restarts (persisted in DB)
✅ Thread-safe (DB transactions)
✅ Validated via ScoringWeights value object
Falls back to env-var defaults when no DB row exists

5.4. HIGH RISK: No Repository Layer ✅ PARTIALLY RESOLVED

Update (Feb 18): Repository ports and implementations now exist:

domain/ports/repositories/ — Protocol interfaces for TechniqueRepository and TestRepository.
infrastructure/persistence/repositories/ — SQLAlchemy implementations (SATechniqueRepository, SATestRepository) with batch query methods.
dependencies/repositories.py — FastAPI Depends() wiring.

Remaining: Old routers still use direct db.query(). Migration is incremental — new endpoints use repositories, old ones coexist.

5.5. HIGH RISK: No CI/CD ✅ RESOLVED

Update (Feb 18): GitHub Actions CI pipeline exists at .github/workflows/ci.yml:

Runs ruff lint + pytest on every push/PR.
Uses PostgreSQL + Redis service containers (production-like environment).
Local validation via scripts/agent_validate_backend.sh.

5.6. MEDIUM RISK: Background Jobs with Own Sessions (partially mitigated)

# mitre_sync_job.py
db = SessionLocal()
try:
    sync_mitre(db)
finally:
    db.close()

Background jobs create sessions outside the request lifecycle. This is technically correct, but:

No robust error handling (no retry mechanism).
✅ Structured JSON logging now available (logging_config.py)
No dead letter queue for failed jobs.

5.7. MEDIUM RISK: Anemic Models ✅ PARTIALLY RESOLVED

Update (Feb 18): Rich domain entities now exist alongside ORM models:

domain/test_entity.py — Full state machine with business logic, domain events, dual validation, timers.
domain/entities/technique.py — Status recalculation, review lifecycle, MITRE ID validation.
domain/value_objects/ — MitreId, ScoringWeights (immutable, validated).
ORM models remain anemic by design (persistence mapping only). Business logic lives in domain entities.

Remaining: Campaign, ComplianceFramework, ThreatActor still lack domain entity counterparts.

5.8. MEDIUM RISK: No Explicit Transaction Management ✅ PARTIALLY RESOLVED

Update (Feb 18): A UnitOfWork context manager exists at domain/unit_of_work.py with explicit commit(), rollback(), and flush(). Used by test_workflow_service.py which explicitly states "The caller (router) is responsible for committing the session via the Unit of Work pattern."

Remaining: Some services like audit_service.py still call db.commit() directly. Needs incremental migration.

5.9. LOW RISK: No Semantic API Versioning

The API is under /api/v1 but there is no mechanism to support v2 without duplicating entire routers.

6. Refactor Proposal Towards Clean Architecture

6.1. Target Structure

backend/
├── app/
│   ├── main.py                          # FastAPI setup (minimal)
│   ├── config.py                        # Settings (immutable)
│   │
│   ├── domain/                          # ★ DOMAIN LAYER (no external dependencies)
│   │   ├── entities/                    # Entities with behavior
│   │   │   ├── test.py                  # Test entity with can_transition(), validate()
│   │   │   ├── technique.py             # Technique with calculate_status()
│   │   │   ├── campaign.py              # Campaign with add_test(), activate()
│   │   │   └── ...
│   │   ├── value_objects/               # Immutable value objects
│   │   │   ├── score.py                 # TechniqueScore, OrganizationScore
│   │   │   ├── test_state.py            # TestState with valid transitions
│   │   │   └── mitre_id.py              # MitreId with validation
│   │   ├── exceptions.py               # Domain exceptions (NOT HTTPException)
│   │   │   # InvalidTransitionError, EntityNotFoundError, etc.
│   │   ├── events.py                   # Domain events
│   │   │   # TestValidated, TestRejected, CampaignCompleted
│   │   └── ports/                       # ★ INTERFACES (ABCs / Protocols)
│   │       ├── repositories/
│   │       │   ├── test_repository.py   # ABC: find_by_id(), save(), list_by_technique()
│   │       │   ├── technique_repository.py
│   │       │   ├── campaign_repository.py
│   │       │   └── ...
│   │       ├── services/
│   │       │   ├── storage_port.py      # ABC: upload_file(), get_presigned_url()
│   │       │   ├── notification_port.py # ABC: send_notification()
│   │       │   └── event_bus_port.py    # ABC: publish(event)
│   │       └── auth/
│   │           └── token_service_port.py
│   │
│   ├── application/                     # ★ APPLICATION LAYER (use cases)
│   │   ├── use_cases/
│   │   │   ├── tests/
│   │   │   │   ├── create_test.py       # CreateTestUseCase
│   │   │   │   ├── start_execution.py   # StartExecutionUseCase
│   │   │   │   ├── submit_red.py
│   │   │   │   ├── validate_test.py
│   │   │   │   └── get_retest_chain.py
│   │   │   ├── scoring/
│   │   │   │   ├── calculate_technique_score.py
│   │   │   │   └── calculate_organization_score.py
│   │   │   ├── campaigns/
│   │   │   │   ├── create_campaign.py
│   │   │   │   └── generate_from_threat_actor.py
│   │   │   ├── heatmap/
│   │   │   │   ├── generate_coverage_layer.py
│   │   │   │   └── export_navigator.py
│   │   │   └── reports/
│   │   │       ├── generate_coverage_report.py
│   │   │       └── export_coverage_csv.py
│   │   ├── dto/                         # Input/Output DTOs for use cases
│   │   │   ├── test_dto.py
│   │   │   └── ...
│   │   └── interfaces/                  # Application-level ports
│   │       └── unit_of_work.py          # ABC: UnitOfWork with commit/rollback
│   │
│   ├── infrastructure/                  # ★ INFRASTRUCTURE LAYER (implementations)
│   │   ├── persistence/
│   │   │   ├── orm/                     # SQLAlchemy models (mapping only)
│   │   │   │   ├── test_model.py
│   │   │   │   ├── technique_model.py
│   │   │   │   └── ...
│   │   │   ├── repositories/            # Concrete implementations
│   │   │   │   ├── sqlalchemy_test_repository.py
│   │   │   │   ├── sqlalchemy_technique_repository.py
│   │   │   │   └── ...
│   │   │   ├── unit_of_work.py          # SQLAlchemy UoW implementation
│   │   │   └── database.py              # Engine, session factory
│   │   ├── storage/
│   │   │   └── minio_storage.py         # Implements StoragePort
│   │   ├── external/                    # Import services
│   │   │   ├── mitre_sync.py
│   │   │   ├── atomic_import.py
│   │   │   ├── sigma_import.py
│   │   │   └── ...
│   │   ├── auth/
│   │   │   ├── jwt_service.py           # Implements TokenServicePort
│   │   │   └── token_blacklist.py       # Redis-backed blacklist
│   │   ├── notifications/
│   │   │   └── db_notification_service.py
│   │   ├── jobs/
│   │   │   └── scheduler.py             # APScheduler setup
│   │   └── cache/
│   │       └── redis_cache.py           # Score caching (Redis)
│   │
│   └── presentation/                    # ★ PRESENTATION LAYER (HTTP)
│       ├── api/
│       │   ├── v1/
│       │   │   ├── tests.py             # Routing + request/response mapping only
│       │   │   ├── techniques.py
│       │   │   ├── heatmap.py
│       │   │   └── ...
│       │   └── dependencies.py          # FastAPI Depends() wiring
│       ├── schemas/                     # Pydantic schemas (request/response)
│       │   ├── test_schema.py
│       │   └── ...
│       ├── middleware/
│       │   ├── error_handler.py         # Domain exceptions → HTTP responses
│       │   └── rate_limiter.py
│       └── mappers/                     # Entity ↔ Schema mappers
│           ├── test_mapper.py
│           └── ...

6.2. Dependency Rules

Presentation → Application → Domain ← Infrastructure
     ↓              ↓           ↑           ↑
  FastAPI       Use Cases    Entities    SQLAlchemy
  Pydantic      DTOs        Ports       MinIO
                                        Redis
                                        APScheduler

The golden rule: Dependencies only point towards the center (Domain). Infrastructure implements the ports defined in Domain.

6.3. Key Changes by Layer

Domain Layer (New)

# domain/entities/test.py — Rich entity (not anemic)
class TestEntity:
    def __init__(self, id, state, technique_id, ...):
        self._state = state

    def can_transition_to(self, target: TestState) -> bool:
        return target in VALID_TRANSITIONS[self._state]

    def start_execution(self, user: UserEntity) -> list[DomainEvent]:
        if not self.can_transition_to(TestState.red_executing):
            raise InvalidTransitionError(self._state, TestState.red_executing)
        self._state = TestState.red_executing
        return [TestExecutionStarted(test_id=self.id, user_id=user.id)]

# domain/exceptions.py — Domain exceptions, NOT HTTPException
class InvalidTransitionError(DomainException):
    def __init__(self, current: TestState, target: TestState):
        self.current = current
        self.target = target

# domain/ports/repositories/test_repository.py — Abstract interface
class TestRepository(Protocol):
    def find_by_id(self, test_id: UUID) -> TestEntity | None: ...
    def save(self, test: TestEntity) -> None: ...
    def list_by_technique(self, technique_id: UUID) -> list[TestEntity]: ...

Application Layer (Use Cases)

# application/use_cases/tests/start_execution.py
class StartExecutionUseCase:
    def __init__(self, test_repo: TestRepository, uow: UnitOfWork):
        self._test_repo = test_repo
        self._uow = uow

    def execute(self, test_id: UUID, user_id: UUID) -> TestDTO:
        with self._uow:
            test = self._test_repo.find_by_id(test_id)
            if not test:
                raise EntityNotFoundError("Test", test_id)
            events = test.start_execution(user)
            self._test_repo.save(test)
            self._uow.commit()
            # events are published after commit
            return TestDTO.from_entity(test)

Presentation Layer (Slim Routers)

# presentation/api/v1/tests.py — HTTP concerns only
@router.post("/{test_id}/start-execution")
def start_execution(
    test_id: UUID,
    use_case: StartExecutionUseCase = Depends(get_start_execution_use_case),
    current_user: User = Depends(get_current_user),
):
    try:
        result = use_case.execute(test_id, current_user.id)
        return result
    except EntityNotFoundError:
        raise HTTPException(404)
    except InvalidTransitionError as e:
        raise HTTPException(400, detail=str(e))

Infrastructure Layer (Implementations)

# infrastructure/persistence/repositories/sqlalchemy_test_repository.py
class SQLAlchemyTestRepository(TestRepository):
    def __init__(self, session: Session):
        self._session = session

    def find_by_id(self, test_id: UUID) -> TestEntity | None:
        model = self._session.query(TestModel).filter(TestModel.id == test_id).first()
        return TestMapper.to_entity(model) if model else None

    def save(self, test: TestEntity) -> None:
        model = TestMapper.to_model(test)
        self._session.merge(model)

6.4. Incremental Migration Plan (Phases)

The refactor must be incremental — not big bang. Each phase delivers value and the system continues working.

Phase 1: Foundations (1-2 weeks)

Create the directory structure: domain/, application/, infrastructure/, presentation/.
Create domain/exceptions.py with domain exceptions.
Create error_handler.py middleware that maps domain exceptions → HTTP responses.
Create domain/ports/repositories/ with Protocol interfaces for the 3-4 most used entities (Test, Technique, Campaign).
Create SQLAlchemy implementations of these repositories.
Do not move routers yet.

Phase 2: Extract the Test Domain (1-2 weeks)

Create domain/entities/test.py with the state machine (extract from test_workflow_service).
Create use cases for each state transition.
Migrate the tests.py router to use the use cases.
Remove HTTPException from test_workflow_service.
Pure unit tests for the domain entity (no DB).

Phase 3: Extract Fat Services from Routers (2-3 weeks)

Move heatmap.py logic to application/use_cases/heatmap/.
Move reports.py logic to application/use_cases/reports/.
Move metrics.py logic to application services.
Routers become thin controllers (< 20 lines per endpoint).

Phase 4: Complete Repository Pattern (1-2 weeks)

Create repositories for all remaining entities.
Migrate scattered queries from routers to repositories.
Remove db.query(...) from any file outside infrastructure/.

Phase 5: Robust Infrastructure (1-2 weeks)

Move token blacklist to Redis.
Implement the Unit of Work pattern.
Move scoring config to the database (not mutable settings).
Add event bus for domain events (notifications, auditing).

Phase 6: CI/CD and Observability

Set up GitHub Actions (lint, type check, tests).
Add structured logging.
Add improved health checks.

7. Executive Summary

Current Strengths

Strength	Detail
Well-modeled domain	The data model covers ATT&CK, D3FEND, compliance, threat actors, and campaigns comprehensively
Solid test workflow	The state machine in `test_workflow_service` is the best designed component
Clean frontend	API/pages/components separation with TanStack Query is correct
Secure auth	HttpOnly cookies + RBAC with 6 well-defined roles
Import services	The 8 import services are well encapsulated
Existing tests	18 test files with fixtures — a foundation to build upon

Critical Weaknesses (Updated Feb 19)

Weakness	Original Severity	Current Status
Fat controllers (routers with business logic)	HIGH	✅ Resolved — 9 routers extracted to services
No repository layer	HIGH	✅ Resolved (Test, Technique repos + 9 service modules)
Services depend on FastAPI	HIGH	✅ Resolved (domain exceptions + middleware)
Anemic models	MEDIUM	✅ Partially resolved (TestEntity, TechniqueEntity)
In-memory token blacklist	HIGH	✅ Resolved (Redis-backed)
Mutable settings at runtime	MEDIUM	✅ Resolved (scoring_config DB table)
No CI/CD	MEDIUM	✅ Resolved (GitHub Actions)
No dependency inversion	HIGH	✅ Partially resolved (ports + repos + services)
No structured logging	LOW	✅ Resolved (JSON logging for production)

Final Classification

┌──────────────────────────────────────────────────────────┐
│  Type:        Clean Modular Monolith                     │
│  Maturity:    Production-ready                           │
│  SOLID:       4/5 (SRP ✅, OCP partial, LSP n/a,        │
│               ISP partial, DIP ✅ started)               │
│  Testability: 7/10 (326 tests, domain unit tests, repo  │
│               integration tests, service layer tests)    │
│  Coupling:    7/10 (domain decoupled, services agnostic, │
│               most routers are thin adapters)            │
│  Cohesion:    8/10 (domain entities own business rules,  │
│               services own query logic)                  │
│  Estimated remaining tech debt: ~1 week                  │
│  (heatmap extraction, remaining minor routers,           │
│   Campaign/ComplianceFramework domain entities)          │
└──────────────────────────────────────────────────────────┘

Recommendation (Updated Feb 19)

The architectural refactoring is substantially complete. All critical and high-priority items from the original analysis are resolved:

~~Extract domain exceptions~~ ✅ Done
~~Create repositories for Test and Technique~~ ✅ Done
~~Move token blacklist to Redis~~ ✅ Done
~~Set up basic CI/CD~~ ✅ Done
~~Migrate fat routers to services~~ ✅ Done (9 routers extracted)
~~Persist scoring weights in database~~ ✅ Done
~~Add structured JSON logging~~ ✅ Done

Remaining low-priority items:

Extract remaining logic from heatmap.py to heatmap_service.py
Create domain entities for Campaign and ComplianceFramework
Extract users.py, audit.py, data_sources.py to services (simple CRUD)
Add common interface for import services (OCP improvement)

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