pb-test-driven-development

name	pb-test-driven-development
description	Use when implementing any feature or bugfix, before writing implementation code

pb-test-driven-development

Write failing tests first, then implement to make them pass. Red → Green → Refactor.

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.

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
• Don't skim it for ideas
• Delete means delete — remove the files, revert the changes, start from a clean state

When to Use

Always:

• New features
• Bug fixes
• Refactoring
• Behavior changes

Exceptions (ask user):

• Throwaway prototypes
• Generated code
• Configuration files

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

Red-Green-Refactor

digraph tdd_cycle {
    rankdir=TB;
    "RED: Write failing test" [shape=box style=filled fillcolor="#ffcccc"];
    "Verify RED: test fails?" [shape=diamond];
    "GREEN: Minimal code" [shape=box style=filled fillcolor="#ccffcc"];
    "Verify GREEN: test passes?" [shape=diamond];
    "REFACTOR: Clean up" [shape=box style=filled fillcolor="#ccccff"];
    "Next behavior" [shape=box];

    "RED: Write failing test" -> "Verify RED: test fails?";
    "Verify RED: test fails?" -> "GREEN: Minimal code" [label="yes"];
    "Verify RED: test fails?" -> "Fix test" [label="no - test passes"];
    "GREEN: Minimal code" -> "Verify GREEN: test passes?";
    "Verify GREEN: test passes?" -> "REFACTOR: Clean up" [label="yes"];
    "Verify GREEN: test passes?" -> "Fix code" [label="no"];
    "REFACTOR: Clean up" -> "Next behavior";
}

RED - Write Failing Test

Write one minimal test showing what should happen.

Good:

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

    result = retry_operation(operation)

    assert result == "success"
    assert attempts == 3

Clear name, tests real behavior, one thing.

Bad:

def test_retry_works():
    mock = Mock(side_effect=[ValueError, ValueError, "success"])
    result = retry_operation(mock)
    assert mock.call_count == 3

Vague name, tests mock not code.

Requirements:

• One behavior
• Clear name
• Real code (no mocks unless unavoidable)

Bug Fix: Reproduce First

For bugs, the RED phase doubles as a reproducer. Don't fix without confirming the bug exists.

User reports: "Sorting breaks when there are duplicate scores"

Wrong: Immediately changes sort logic.

Right:

# 1. RED: Write test that exposes the bug
def test_sort_with_duplicate_scores():
    scores = [
        {'name': 'Alice', 'score': 100},
        {'name': 'Bob', 'score': 100},
        {'name': 'Charlie', 'score': 90},
    ]
    result = sort_scores(scores)
    assert result[0]['score'] == 100
    assert result[1]['score'] == 100
    assert result[2]['score'] == 90

# Verify RED: run 10 times → inconsistent ordering → bug confirmed

# 2. GREEN: Fix with stable sort
def sort_scores(scores):
    return sorted(scores, key=lambda x: (-x['score'], x['name']))

# Verify GREEN: test passes consistently

Verify RED - Watch It Fail

MANDATORY. Never skip.

uv run pytest tests/path/test.py -v

Confirm:

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

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

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

GREEN - Minimal Code

Write simplest code to pass the test.

Good:

def retry_operation(fn):
    for i in range(3):
        try:
            return fn()
        except Exception:
            if i == 2:
                raise

Just enough to pass.

Bad:

def retry_operation(fn, max_retries=3, backoff="linear", on_retry=None):
    # YAGNI

Over-engineered.

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

Verify GREEN - Watch It Pass

MANDATORY.

uv run pytest tests/path/test.py -v

Confirm:

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

Test fails? Fix code, not test.

Other tests fail? Fix now.

REFACTOR - Clean Up

After green only:

• Remove duplication
• Improve names
• Extract helpers

Keep tests green. Don't add behavior.

Repeat

Next failing test for next feature.

Why Order Matters

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

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

• Might test 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 (X more hours, high confidence)
• Keep it and add tests after (30 min, low confidence, likely bugs)

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

"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.

30 minutes of tests after ≠ TDD. You get coverage, lose proof tests work.

"I'm following the spirit of TDD"

Violating the letter of the rules is violating the spirit of the rules. There is no "spirit" that permits skipping RED. The entire point is watching the test fail — that's not a ritual, it's the mechanism that proves your test detects the missing behavior. Skip RED, and you have zero evidence your test works.

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 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 existing code.

Red Flags - STOP and Start Over

These are specific thoughts that mean you are about to violate the Iron Law. Recognize them as danger signals:

• Code before test
• Test after implementation
• Test passes immediately
• Can't explain why test failed
• Tests added "later"
• Rationalizing "just this once"
• "I already manually tested it"
• "Tests after achieve the same purpose"
• "It's about spirit not ritual"
• "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..."
• "I'll just check if it works first"
• "The test would be trivial anyway"
• "I know what the code does, I wrote it"

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

Example: Bug Fix

Bug: Empty email accepted

RED

def test_rejects_empty_email():
    result = submit_form({"email": ""})
    assert result["error"] == "Email required"

Verify RED

$ uv run pytest tests/test_form.py::test_rejects_empty_email -v
FAIL: AssertionError: KeyError 'error'

GREEN

def submit_form(data):
    if not data.get("email", "").strip():
        return {"error": "Email required"}
    # ...

Verify GREEN

$ uv run pytest tests/test_form.py::test_rejects_empty_email -v
PASS

REFACTOR Extract validation for multiple fields if needed.

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 wished-for API. Write assertion first.
Test too complicated	Design too complicated. Simplify interface.
Must mock everything	Code too coupled. Use dependency injection.
Test setup huge	Extract helpers. Still complex? Simplify design.

Debugging Integration

Bug found? Write failing test reproducing it. Follow TDD cycle. Test proves fix and prevents regression.

Never fix bugs without a test.

Integration with pb-spec

• pb-build Generator: Every task in the BDD+TDD loop follows this cycle
• pb-build TDD-only tasks: Pure TDD without BDD outer loop
• Standalone: Use for any TDD need outside pb-spec workflow