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.

See tests.md for examples, mocking.md for mocking guidelines, and ui-tests.md for end-to-end tests of user flows.

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
  ...

Anti-Pattern: Tautological Tests

The assertion recomputes the expected value the same way the code does — expect(add(a, b)).toBe(a + b), a snapshot derived by hand via the same logic, a constant asserted equal to itself. The test passes by construction and can never disagree with the code.

Expected values must come from an independent source of truth: a known-good literal, a worked example, the spec. See tests.md for an example.

Workflow

0. Discover the stack

The TDD cycle is universal; the commands are not. Before the first test, discover how this repository tests and use its commands for every RED and GREEN:

• Build system: package.json, pyproject.toml, go.mod, Cargo.toml, Makefile
• Checked-in wrappers first: ./gradlew, ./mvnw, make test, repo scripts — never a globally installed tool the repo doesn't use
• The focused-single-test command vs the full-suite command — run focused during the loop, full suite once before completion
• Where tests live, how they're named, what patterns neighboring tests follow
• CI workflows show the commands that actually gate merges

Never assume npm test.

1. Planning

When exploring the codebase, read GLOSSARY.md if it exists (else a legacy CONTEXT.md; GLOSSARY-MAP.md points to the right one in a multi-context repo) so that test names and interface vocabulary match the project's domain language, and respect ADRs in the area you're touching.

A seam is the public boundary you test at: the interface where you observe behavior without reaching inside. Before writing any test, write down the seams under test, one line each on what a test there catches and what it misses, and confirm them with the user:

Ask: "What's the public interface, and which seams should we test?"

No test is written at an unconfirmed seam.

Before writing any code:

• Confirm the seams under test with the user
• Confirm with user which behaviors to test (prioritize)
• Identify opportunities for deep modules (small interface, deep implementation) — call the Skill tool with codebase-design for the vocabulary and the testability checks
• List the behaviors to test (not implementation steps)
• Get user approval on the plan

You can't test everything. Agreeing the seams up front is how testing effort lands 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 → test fails
GREEN: Write minimal code to pass → test passes

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

3. Incremental Loop

For each remaining behavior:

RED:   Write next test → fails
GREEN: Minimal code to pass → passes

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

Only once GREEN — never while RED. Extract duplication, deepen modules (call the Skill tool with codebase-design for the vocabulary), and ask what the new code reveals about existing code. Run tests after each step.