Tdd

Test-Driven Development

Philosophy

Core principle: Tests should verify behavior through public interfaces, not implementation details. Code can change entirely; tests shouldn't.

Good tests are integration-style: they exercise real code paths through public APIs. They describe what the system does, not how it does it. A good test reads like a specification - "user can checkout with valid cart" tells you exactly what capability exists. These tests survive refactors because they don't care about internal structure.

Bad tests are coupled to implementation. They mock internal collaborators, test private methods, or verify through external means (like querying a database directly instead of using the interface). The warning sign: your test breaks when you refactor, but behavior hasn't changed. If you rename an internal function and tests fail, those tests were testing implementation, not behavior.

Anti-Pattern: Horizontal Slices

DO NOT write all tests first, then all implementation. This is "horizontal slicing" - treating RED as "write all tests" and GREEN as "write all code."

This produces crap tests:

• Tests written in bulk test imagined behavior, not actual behavior
• You end up testing the shape of things (data structures, function signatures) rather than user-facing behavior
• Tests become insensitive to real changes - they pass when behavior breaks, fail when behavior is fine
• You outrun your headlights, committing to test structure before understanding the implementation

Correct approach: Vertical slices via tracer bullets. One test → one implementation → repeat. Each test responds to what you learned from the previous cycle. Because you just wrote the code, you know exactly what behavior matters and how to verify it.

WRONG (horizontal):
  RED:   test1, test2, test3, test4, test5
  GREEN: impl1, impl2, impl3, impl4, impl5

RIGHT (vertical):
  RED→GREEN: test1→impl1
  RED→GREEN: test2→impl2
  RED→GREEN: test3→impl3
  ...

Layer Selection

This project has three test layers. Start at the highest layer that exercises the behavior under test, then add lower-layer tests only when needed.

Layer 1 (default): RF acceptance tests — atest/
  Public interface: Robot Framework keywords as a Robot Framework user sees them
  Run: inv atest --suite <suite>
  Skill: write-robot-tests

Layer 2 (complex Python logic): Python unit tests — utest/
  Public interface: Python keyword methods
  Run: inv utest
  Skill: python-unit-test

Layer 3 (complex Node logic): Node.js unit tests — node/playwright-wrapper/__tests__/
  Public interface: TypeScript wrapper functions called over gRPC
  Run: inv utest-node
  Skill: node-unit-test

Default: start with an RF acceptance test. If the work adds or changes a keyword, the RF test is the specification. It verifies the keyword behaves correctly end-to-end through the full stack.

Drop to Python unit tests when:

• The behavior involves complex Python-side logic (e.g. argument parsing, assertion engine, type conversion)
• The behavior is difficult or slow to exercise through RF (e.g. edge cases that require precise mock control)

Drop to Node.js unit tests when:

• The behavior lives entirely in the Playwright wrapper (e.g. new interaction primitives, browser-control logic)
• The Node-side logic is complex enough that RF-level tests wouldn't isolate failures clearly

gRPC boundary note: The proto interface (protobuf/playwright.proto) is a natural seam. Behavior that crosses it often warrants tests on both sides: a Python unit test that mocks the gRPC stub, and a Node unit test that mocks Playwright. RF acceptance tests cover the full path but don't isolate which side failed.

For the mechanics of writing tests in each layer, load the corresponding skill before starting:

• RF tests → write-robot-tests skill
• Python unit tests → python-unit-test skill
• Node unit tests → node-unit-test skill

Workflow

1. Planning

When exploring the codebase, use the project's domain glossary so that test names and interface vocabulary match the project's language, and respect ADRs in the area you're touching.

Before writing any code:

• Confirm with user what interface changes are needed
• Confirm with user which behaviors to test (prioritize)
• Choose the starting layer (see Layer Selection above) — default is RF acceptance tests
• Identify opportunities for deep modules (small interface, deep implementation)
• Design interfaces for testability
• List the behaviors to test (not implementation steps)
• Get user approval on the plan

Ask: "What should the public interface look like? Which behaviors are most important to test?"

You can't test everything. Confirm with the user exactly which behaviors matter most. Focus testing effort on critical paths and complex logic, not every possible edge case.

2. Tracer Bullet

Write ONE test that confirms ONE thing about the system:

RED:   Write test for first behavior → run tests → confirm failure
         RF:     inv atest --suite <suite> --test <test>
         Python: inv utest -k <test_name>
         Node:   inv utest-node
GREEN: Write minimal code to pass → run tests → confirm pass

This is your tracer bullet - proves the path works end-to-end.

3. Incremental Loop

For each remaining behavior:

RED:   Write next test → run tests → confirm failure
GREEN: Minimal code to pass → run tests → confirm pass

Rules:

• One test at a time
• Only enough code to pass current test
• Don't anticipate future tests
• Keep tests focused on observable behavior

4. Refactor

After all tests pass, look for refactor candidates:

• Extract duplication
• Deepen modules (move complexity behind simple interfaces)
• Apply SOLID principles where natural
• Consider what new code reveals about existing code
• Run tests after each refactor step

Never refactor while RED. Get to GREEN first.

Checklist Per Cycle

[ ] Test describes behavior, not implementation
[ ] Test uses public interface only
[ ] Test would survive internal refactor
[ ] Code is minimal for this test
[ ] No speculative features added