test-driven-development

name	test-driven-development
description	TDD: enforce RED-GREEN-REFACTOR, tests before code.
version	1.2.0
author	Adapted from Hermes Agent (obra/superpowers)
license	MIT
tags	["testing","tdd","development","quality","red-green-refactor"]

Test-Driven Development (TDD)

Overview

Write the test first. Watch it fail. Write minimal code to pass.

Core principle: If you didn't watch the test fail, you don't know if it tests the right thing.

Violating the letter of the rules is violating the spirit of the rules.

When to Use

Always:

• New features
• Bug fixes
• Refactoring
• Behavior changes

Exceptions (ask first):

• Throwaway prototypes
• Generated code
• Configuration files

Thinking "skip TDD just this once"? Stop. That's rationalization.

The Iron Law

NO PRODUCTION CODE WITHOUT A FAILING TEST FIRST

Write code before the test? Delete it. Start over.

No exceptions:

• Don't keep it as "reference"
• Don't "adapt" it while writing tests
• Don't look at it
• Delete means delete

Implement fresh from tests. Period.

Red-Green-Refactor Cycle

RED — Write Failing Test

Write one minimal test showing what should happen.

Good test:

def test_retries_failed_operations_3_times():
    attempts = 0
    def operation():
        nonlocal attempts
        attempts += 1
        if attempts < 3:
            raise Exception('fail')
        return 'success'

    result = retry_operation(operation)

    assert result == 'success'
    assert attempts == 3

Clear name, tests real behavior, one thing.

Bad test:

def test_retry_works():
    mock = MagicMock()
    mock.side_effect = [Exception(), Exception(), 'success']
    result = retry_operation(mock)
    assert result == 'success'  # What about retry count? Timing?

Vague name, tests mock not real code.

Requirements:

• One behavior per test
• Clear descriptive name ("and" in name? Split it)
• Real code, not mocks (unless truly unavoidable)
• Name describes behavior, not implementation

Verify RED — Watch It Fail

MANDATORY. Never skip.

# Run the specific test
pytest tests/test_feature.py::test_specific_behavior -v

Confirm:

• Test fails (not errors from typos)
• Failure message is expected
• Fails because the feature is missing

Test passes immediately? You're testing existing behavior. Fix the test.

Test errors? Fix the error, re-run until it fails correctly.

GREEN — Minimal Code

Write the simplest code to pass the test. Nothing more.

Good:

def add(a, b):
    return a + b  # Nothing extra

Bad:

def add(a, b):
    result = a + b
    logging.info(f"Adding {a} + {b} = {result}")  # Extra!
    return result

Don't add features, refactor other code, or "improve" beyond the test.

Cheating is OK in GREEN:

• Hardcode return values
• Copy-paste
• Duplicate code
• Skip edge cases

We'll fix it in REFACTOR.

Verify GREEN — Watch It Pass

MANDATORY.

# Run the specific test
pytest tests/test_feature.py::test_specific_behavior -v

# Then run ALL tests to check for regressions
pytest tests/ -q

Confirm:

• Test passes
• Other tests still pass
• Output pristine (no errors, warnings)

Test fails? Fix the code, not the test.

Other tests fail? Fix regressions now.

REFACTOR — Clean Up

After green only:

• Remove duplication
• Improve names
• Extract helpers
• Simplify expressions

Keep tests green throughout. Don't add behavior.

If tests fail during refactor: Undo immediately. Take smaller steps.

Repeat

Next failing test for next behavior. One cycle at a time.

Why Order Matters

"I'll write tests after to verify it works"

Tests written after code pass immediately. Passing immediately proves nothing:

• Might test the wrong thing
• Might test implementation, not behavior
• Might miss edge cases you forgot
• You never saw it catch the bug

Test-first forces you to see the test fail, proving it actually tests something.

"I already manually tested all the edge cases"

Manual testing is ad-hoc. You think you tested everything but:

• No record of what you tested
• Can't re-run when code changes
• Easy to forget cases under pressure
• "It worked when I tried it" ≠ comprehensive

Automated tests are systematic. They run the same way every time.

"Deleting X hours of work is wasteful"

Sunk cost fallacy. The time is already gone. Your choice now:

• Delete and rewrite with TDD (high confidence)
• Keep it and add tests after (low confidence, likely bugs)

The "waste" is keeping code you can't trust.

"TDD is dogmatic, being pragmatic means adapting"

TDD IS pragmatic:

• Finds bugs before commit (faster than debugging after)
• Prevents regressions (tests catch breaks immediately)
• Documents behavior (tests show how to use code)
• Enables refactoring (change freely, tests catch breaks)

"Pragmatic" shortcuts = debugging in production = slower.

"Tests after achieve the same goals — it's spirit not ritual"

No. Tests-after answer "What does this do?" Tests-first answer "What should this do?"

Tests-after are biased by your implementation. You test what you built, not what's required. Tests-first force edge case discovery before implementing.

Common Rationalizations

Excuse	Reality
"Too simple to test"	Simple code breaks. Test takes 30 seconds.
"I'll test after"	Tests passing immediately prove nothing.
"Tests after achieve same goals"	Tests-after = "what does this do?" Tests-first = "what should this do?"
"Already manually tested"	Ad-hoc ≠ systematic. No record, can't re-run.
"Deleting X hours is wasteful"	Sunk cost fallacy. Keeping unverified code is technical debt.
"Keep as reference, write tests first"	You'll adapt it. That's testing after. Delete means delete.
"Need to explore first"	Fine. Throw away exploration, start with TDD.
"Test hard = design unclear"	Listen to the test. Hard to test = hard to use.
"TDD will slow me down"	TDD faster than debugging. Pragmatic = test-first.
"Manual test faster"	Manual doesn't prove edge cases. You'll re-test every change.
"Existing code has no tests"	You're improving it. Add tests for the code you touch.

Red Flags — STOP and Start Over

If you catch yourself doing any of these, delete the code and restart with TDD:

• Code before test
• Test after implementation
• Test passes immediately on first run
• Can't explain why test failed
• Tests added "later"
• Rationalizing "just this once"
• "I already manually tested it"
• "Tests after achieve the same purpose"
• "Keep as reference" or "adapt existing code"
• "Already spent X hours, deleting is wasteful"
• "TDD is dogmatic, I'm being pragmatic"
• "This is different because..."

All of these mean: Delete code. Start over with TDD.

Verification Checklist

Before marking work complete:

• Every new function/method has a test
• Watched each test fail before implementing
• Each test failed for expected reason (feature missing, not typo)
• Wrote minimal code to pass each test
• All tests pass
• Output pristine (no errors, warnings)
• Tests use real code (mocks only if unavoidable)
• Edge cases and errors covered

Can't check all boxes? You skipped TDD. Start over.

When Stuck

Problem	Solution
Don't know how to test	Write the wished-for API. Write the assertion first. Ask.
Test too complicated	Design too complicated. Simplify the interface.
Must mock everything	Code too coupled. Use dependency injection.
Test setup huge	Extract helpers. Still complex? Simplify the design.

Running Tests

Use the project's canonical test runner whenever possible:

# RED — verify failure
pytest tests/test_feature.py::test_name -v

# If the project has a runner script, use it:
scripts/run_tests.sh tests/test_feature.py::test_name

# GREEN — verify pass
pytest tests/test_feature.py::test_name -v

# Full suite — verify no regressions
pytest tests/ -q

If the first RED attempt fails because pytest isn't installed, install it or use the project's venv/runner. Don't count a missing-dependency error as valid RED evidence — you need the test itself to fail because the feature is missing.

Scratch / Prototype Test Fallback

When implementing a small prototype with no test framework installed:

1. Write behavior tests against the wished-for public API before creating the implementation module.
2. Run the focused test and preserve the RED evidence (e.g., import failure because the module doesn't exist yet is valid RED evidence).
3. Implement the smallest callable API that satisfies the acceptance criteria, then run the test suite.
4. Prefer dependency-free tests for small stdlib-only tools. If pytest is unavailable, use unittest rather than installing a new dependency just to validate a tiny deliverable.
5. For unittest discovery, make tests/ importable (tests/__init__.py) and use: python3 -m unittest discover -s tests -p 'test_*.py' -v
6. Clean generated local artifacts after validation (.venv, .pytest_cache, __pycache__) if not part of the deliverable.

Testing Anti-Patterns

• Testing mock behavior instead of real behavior — mocks should verify interactions, not replace the system under test
• Testing implementation details — test behavior/results, not internal method calls
• Happy path only — always test edge cases, errors, and boundaries
• Brittle tests — tests should verify behavior, not structure; refactoring shouldn't break them

Final Rule

Production code → test exists and failed first
Otherwise → not TDD

No exceptions without explicit permission.