nw-bdd-methodology
BDD patterns for acceptance test design - Given-When-Then structure, scenario writing rules, pytest-bdd implementation, anti-patterns, and living documentation
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BDD Methodology for Acceptance Test Design
Core Philosophy
Test units of behavior, not units of code. Acceptance tests validate business outcomes through public interfaces, decoupled from implementation.
Outside-In Double-Loop TDD
The acceptance-designer creates the outer loop of Outside-In TDD. Development starts from user perspective, drives inward.
Outer loop (acceptance/BDD): Hours to days | User perspective, business language | Defines "done" | Scenarios describe user goals and observable outcomes, not internals | Failing outer-loop test is the starting signal for implementation
Inner loop (unit/TDD): Minutes | Developer perspective, technical terms | Software-crafter owns this loop
Workflow:
- Write failing acceptance test from user perspective (outer loop -- outside)
- Software-crafter drops to inner loop: unit tests to implement components (inside)
- Iterate inner loop until acceptance test passes
- Passing acceptance test proves user value delivered
- Repeat for next behavior
Outer loop defines WHAT users need (outside). Inner loop drives HOW to build it (inside).
Given-When-Then Structure
Scenario: [Business-focused title describing one behavior]
Given [preconditions - system state in business terms]
When [single user action or business event]
Then [observable business outcome]
Scenario Writing Rules
Rule 1: One scenario, one behavior -- Split multi-behavior scenarios.
Rule 2: Declarative, not imperative -- Business outcomes, not UI interactions. "When I log in with valid credentials" not "When I click Login button and enter email."
Rule 3: Concrete examples, not abstractions -- "Given my account balance is $100.00" not "Given the user has sufficient funds."
Rule 4: Keep scenarios short (3-5 steps) -- Longer means testing multiple behaviors or irrelevant details.
Rule 5: Background for shared Given steps only -- Only Given steps. Actions/validations in scenarios.
Scenario Categorization
- Happy path: Primary successful workflows
- Error path: Invalid inputs, failures, unauthorized access (target 40%+ of scenarios)
- Edge case: Boundary conditions, unusual but valid behavior
- Integration: Cross-component/system interactions
Golden Path + Key Alternatives
Per capability: 1. Happy path (most common success) | 2. Alternative paths (valid but less common) | 3. Error paths (invalid inputs, constraint violations). Select representative examples revealing different business rules. Do not test every combination.
Scenario Outlines for Boundary Testing
Scenario Outline: Account minimum balance validation
Given I have an account with balance $<initial_balance>
When I attempt to withdraw $<withdrawal_amount>
Then the withdrawal is <result>
Examples: Valid withdrawals
| initial_balance | withdrawal_amount | result |
| 100.00 | 50.00 | accepted |
| 25.00 | 25.00 | accepted |
Examples: Invalid withdrawals
| initial_balance | withdrawal_amount | result |
| 100.00 | 101.00 | rejected (insufficient funds) |
Use outlines for boundary conditions and calculation variations. Avoid when scenarios diverge structurally.
pytest-bdd Implementation
Step Definitions with Fixture Injection
from pytest_bdd import scenarios, given, when, then, parsers
scenarios('../features/account.feature')
@given("I am authenticated", target_fixture="authenticated_user")
def authenticated_user(auth_service):
user = auth_service.create_and_authenticate("test@example.com")
return user
@given(parsers.parse('my account balance is ${amount:g}'),
target_fixture="account")
def account_with_balance(authenticated_user, account_service, amount):
return account_service.create_account(authenticated_user, balance=amount)
Step Organization by Domain
Organize by domain concept, not feature file:
steps/
authentication_steps.py # All auth-related steps
account_steps.py # All account-related steps
transaction_steps.py # All transaction-related steps
Fixture Scopes for Performance
Session: expensive setup (DB engine, app instance) | Module: schema creation | Function: data cleanup (autouse=True)
Production-Like Test Environment
@pytest.fixture(scope="session")
def app():
"""Application instance with production-like configuration."""
app = create_app({"environment": "test", "database": "postgresql://localhost/test_db"})
with app.app_context():
app.db.create_all()
yield app
with app.app_context():
app.db.drop_all()
Use real services (database, message queue) with test data. Avoid mocks at acceptance level.
Anti-Patterns
| Anti-Pattern | Fix |
|---|---|
| Testing through UI | Test through service/API layer |
| Multiple WHEN actions | Split into separate scenarios |
| Feature-coupled steps | Organize by domain concept |
| Conjunction steps ("Given A and B" as one step) | Break into atomic steps |
| Incidental details | Include only behavior-relevant info |
| Technical jargon in scenarios | Business domain language |
| Abstract scenarios | Concrete values, specific examples |
| Rambling scenarios (8+ steps) | Extract to 3-5 focused steps |
Living Documentation
Scenarios serve dual purpose: executable tests and living documentation. Organization: Business Goal > Capability > Feature > Scenario > Test. Each scenario traces to business capability. Stakeholders see which capabilities are implemented, tested, passing.
Documentation-Grade Scenarios
Replace HTTP verbs with business actions, JSON with domain concepts, status codes with business outcomes. Add context about WHO and WHY.