Acceptance Test Design Mandates

Four mandates enforced during peer review. All must pass before handoff to software-crafter.

Mandate 1: Hexagonal Boundary Enforcement

Tests invoke through driving ports (entry points), never internal components.

Driving Ports (Test Through These)

Application services/orchestrators | API controllers/CLI handlers | Message consumers/event handlers | Public API facade classes

Not Entry Points (Never Test Directly)

Internal validators, parsers, formatters | Domain entities/value objects | Repository implementations | Internal service components

Correct Pattern

# Invoke through system entry point (driving port)
from myapp.orchestrator import AppOrchestrator

def when_user_performs_action(self):
    orchestrator = AppOrchestrator()
    self.result = orchestrator.perform_action(
        context=self.context
    )

Violation Pattern

# Invoking internal component directly
from myapp.validator import InputValidator  # INTERNAL

def when_user_validates_input(self):
    validator = InputValidator()  # WRONG BOUNDARY
    self.result = validator.validate(self.input)

Testing internal components creates Testing Theater: tests pass but users cannot access feature through actual entry point. Integration wiring bugs remain hidden.

Mandate 2: Business Language Abstraction

Step methods speak business language, abstract all technical details.

Three Abstraction Layers

Layer 1 - Gherkin: Pure business language, all stakeholders. Domain terms from ubiquitous language | Zero technical jargon | Describe WHAT user does, not HOW system does it

Scenario: Customer places order for available product
  Given customer has items in shopping cart
  When customer submits order
  Then order is confirmed
  And customer receives confirmation email

Layer 2 - Step Methods: Business service delegation. Method names use domain terms | Delegate to business service layer (OrderService, not HTTP client) | Assert business outcomes (order.is_confirmed()), not technical state (status_code == 201)

def when_customer_submits_order(self):
    self.result = self.order_service.place_order(
        customer=self.customer, items=self.cart_items
    )

def then_order_is_confirmed(self):
    assert self.result.is_confirmed()
    assert self.result.has_order_number()

Layer 3 - Business Services: Production services handle technical implementation. HTTP calls, DB transactions, SMTP hidden inside service layer.

Test Smell Indicators

requests.post() in step method | db.execute() in step method | assert response.status_code | Technical terms in Gherkin

Mandate 3: User Journey Completeness

Tests validate complete user journeys with business value, not isolated technical operations.

Complete Journey Structure

Every scenario includes: User trigger (Given/When) | Business logic (When - system processes rules) | Observable outcome (Then - user sees result) | Business value (Then - value delivered)

Correct Example

Scenario: Customer successfully completes purchase
  Given customer has selected products worth $150
  And customer has valid payment method
  When customer submits order
  Then order is confirmed with order number
  And customer receives email confirmation
  And order appears in customer's order history

Violation Example

Scenario: Order validator accepts valid order data
  Given valid order JSON exists
  When validator.validate() is called
  Then validation passes
# Tests isolated validation, not user journey

Scenario Name Test

Does name express user value or technical operation? "Customer completes purchase" = correct. "Validator accepts JSON" = violation.

Walking Skeleton Strategy

Balance user-centric E2E integration tests with focused boundary tests.

Walking Skeletons (2-5 per feature)

Trace thin vertical slice delivering observable user value E2E | Each answers: "Can a user accomplish this goal and see the result?" | Express simplest complete user journey | Validate system delivers demo-able stakeholder value | Touch all layers as consequence of journey, not as design goal

Walking Skeleton Litmus Test

1. Title describes user goal ("Customer purchases a product") not technical flow ("Order passes through all layers")
2. Given/When describe user actions/context, not system state setup
3. Then describe user observations (confirmation, email, receipt), not internal side effects (DB row, message queued)
4. Non-technical stakeholder can confirm "yes, that is what users need"

Focused Scenarios (15-20 per feature, majority)

Test specific business rules at driving port boundary | Test doubles for external dependencies (faster, isolated) | Cover business rule variations and edge cases | Invoke through entry point (OrderService, Orchestrator)

Recommended Ratio

For typical feature with 20 scenarios: 2-3 walking skeletons (user value E2E) | 17-18 focused scenarios (boundary tests with test doubles). Walking skeletons prove users achieve goals. Focused scenarios run fast, cover breadth. Both use business language and invoke through entry points.

Mandate 4: Pure Function Extraction Before Fixtures

BEFORE parametrizing any test fixture with environment variants:

1. Identify ALL business logic in the code under test
2. Extract every piece of business logic into a pure function:
   • Pure function: takes inputs, returns outputs, no side effects
   • Impure code: subprocess calls, file I/O, network, environment variables
3. Test pure functions directly — no fixtures, no mocks, no environment setup needed
4. Test impure code (subprocess, file I/O) through adapter interfaces:
   • Define a port (interface) for each impure operation
   • Create a test adapter (in-memory, fake) for each port
   • Acceptance tests use real adapters; unit tests use fakes
5. Parametrize fixtures ONLY for the thin adapter layer that connects to real environments

Rationale: Parametrizing fixtures across environments is expensive. Pure functions need zero environment setup. Extract first, parametrize the minimum.

Violation Pattern

# WRONG: parametrizing entire test across environments
@pytest.fixture(params=["clean", "with-pre-commit", "with-stale-config"])
def environment(request):
    return setup_environment(request.param)

def test_install_detects_conflicts(environment):
    result = full_install_pipeline(environment)  # Impure: touches filesystem
    assert result.conflicts == []

Correct Pattern

# Step 1: Extract pure logic
def detect_conflicts(config: Config, existing: list[str]) -> list[Conflict]:
    """Pure function — no I/O, no environment dependency."""
    return [Conflict(k) for k in existing if k in config.keys]

# Step 2: Test pure function directly (no fixture needed)
def test_detect_conflicts_with_overlapping_keys():
    conflicts = detect_conflicts(Config(keys=["a", "b"]), existing=["b", "c"])
    assert conflicts == [Conflict("b")]

# Step 3: Parametrize ONLY the adapter layer
@pytest.fixture(params=["clean", "with-pre-commit"])
def fs_adapter(request):
    return create_real_fs_adapter(request.param)

def test_adapter_reads_config_from_environment(fs_adapter):
    config = fs_adapter.read_config()  # Only I/O is parametrized
    assert config is not None

Mandate Compliance (CM-D)

• CM-D: Business logic extracted to pure functions. Impure code isolated behind adapters. Fixture parametrization applies only to adapter layer.

Mandate Compliance Verification

Handoff to software-crafter includes proof all four mandates pass:

• CM-A: All test files import entry points (driving ports), zero internal component imports
• CM-B: Gherkin uses business terms only, step methods delegate to services
• CM-C: Scenarios validate complete user journeys with business value
• CM-D: Business logic extracted to pure functions. Impure code isolated behind adapters. Fixture parametrization applies only to adapter layer.

Evidence: import listings, grep for technical terms, walking skeleton identification, focused scenario count, pure function extraction inventory (list of extracted functions + their adapter boundaries).