test-driven-development

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

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 your human partner):

• 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

digraph tdd_cycle {
    rankdir=LR;
    red [label="RED\nWrite failing test", shape=box, style=filled, fillcolor="#ffcccc"];
    verify_red [label="Verify fails\ncorrectly", shape=diamond];
    green [label="GREEN\nMinimal code", shape=box, style=filled, fillcolor="#ccffcc"];
    verify_green [label="Verify passes\nAll green", shape=diamond];
    refactor [label="REFACTOR\nClean up", shape=box, style=filled, fillcolor="#ccccff"];
    next [label="Next", shape=ellipse];

    red -> verify_red;
    verify_red -> green [label="yes"];
    verify_red -> red [label="wrong\nfailure"];
    green -> verify_green;
    verify_green -> refactor [label="yes"];
    verify_green -> green [label="no"];
    refactor -> verify_green [label="stay\ngreen"];
    verify_green -> next;
    next -> red;
}

RED - Write Failing Test

Write one minimal test showing what should happen.

```rust #[test] fn detect_missing_return_type() { let db = Database::default(); let src = r#" main(void) { return 0; } "#; let file = SourceFile::new(&db, "test.c".into(), src.into());

let diags = parse::accumulated::<Diagnostics>(&db, file);

assert_eq!(diags.len(), 1);
assert_eq!(diags[0].message(), "Expected a return type for function");
assert_eq!(diags[0].code(), codes::parse::missing_token);

}

Clear name, one behaviour, tests observable output (diagnostics) through the real pipeline; the seam is the input source.
</Good>

<Bad>
```rust
#[test]
fn test_parser() {
    let db = Database::default();
    let file = SourceFile::new(&db, "test.c".into(), "main(void) { return 0; }".into());
    let tree = parse_internal(&db, file);  // internal API

    assert_eq!(tree.root_node().child_count(), 5);  // implementation detail
    assert!(some_internal_cursor_moved());          // not user-visible behaviour
}

Vague name, tests implementation detail (tree shape, internal state) instead of observable behaviour (diagnostics). One test doing multiple things.

Requirements:

• One behaviour
• Clear name
• Assert on observable behaviour; prefer seams and dependency injection so you can test real code with controlled inputs (no mock frameworks)

Verify RED - Watch It Fail

MANDATORY. Never skip.

cargo test detect_missing_return_type

(This repo uses cargo nextest run -p mcc -- detect_missing_return_type for the workspace.)

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.

```rust // One query, one diagnostic when the pattern matches. No new types. fn ensure_return_type(db: &dyn Db, lang: &Language, tree: &Tree, file: SourceFile) { let query = Query::new(lang, "(function_definition ...) @function-def").unwrap(); let mut cursor = QueryCursor::new(); for m in cursor.matches(&query, tree.root_node(), src.as_bytes()) { Diagnostic::error() .with_message("Expected a return type for function") .with_code(codes::parse::missing_token) .with_labels(vec![Label::primary(file, Span::for_node(m.captures[0].node))]) .accumulate(db); } } ``` Just enough to make the test pass; no config struct, no generic pipeline. ```rust // YAGNI: ParserConfig, validation pipeline, extra diagnostics you didn't test. struct ParserConfig { strict_return_type: bool, emit_warnings: bool, ... } fn parse_with_config(db: &dyn Db, file: SourceFile, config: &ParserConfig) -> Ast { ... } ``` Over-engineered: new types and options the test didn't ask for.

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

Verify GREEN - Watch It Pass

MANDATORY.

cargo test detect_missing_return_type

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.

Good Tests

Quality	Good	Bad
Minimal	One thing. "and" in name? Split it.	fn test_validates_and_normalises_and_checks_domain()
Clear	Name describes behaviour	fn test_parser()
Shows intent	Demonstrates desired API / observable outcome	Obscures what code should do

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. Working code without real tests is technical debt.

"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. You verify remembered edge cases, not discovered ones.

Tests-first force edge case discovery before implementing. Tests-after verify you remembered everything (you didn't).

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

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

• 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..."

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

Example: Bug Fix

Bug: Parser accepts a function without a return type; we should emit a diagnostic but don't.

RED

#[test]
fn detect_missing_return_type() {
    let db = Database::default();
    let file = SourceFile::new(&db, "test.c".into(), " main(void) { return 0; } ".into());
    let diags = parse::accumulated::<Diagnostics>(&db, file);
    assert_eq!(diags.len(), 1, "expected one diagnostic, got {}", diags.len());
    assert_eq!(diags[0].message(), "Expected a return type for function");
}

Verify RED

$ cargo test detect_missing_return_type
FAIL: assertion failed: expected one diagnostic, got 0

GREEN Add the missing check: e.g. run a tree-sitter query for the pattern, emit the diagnostic when it matches (one branch in the parser, or a new ensure_return_type helper). No new config types — just enough to get the test green.

Verify GREEN

$ cargo test detect_missing_return_type
PASS

REFACTOR Extract shared validation or diagnostic-building if duplication appears.

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 (prefer seams and DI; test behaviour, not injected doubles' call counts)
• 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. Ask your human partner.
Test too complicated	Design too complicated. Simplify interface.
Must mock everything	Code too coupled. Introduce a seam; inject the dependency so you can test real behaviour with controlled inputs or a test double.
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.

Testing Anti-Patterns

When adding test doubles or test utilities, read @testing-anti-patterns.md to avoid common pitfalls:

• Testing the double's behaviour (e.g. asserting on an injected dependency's call count) instead of the observable outcome
• Adding test-only methods to production types
• Injecting a dependency without designing for testability (no seam)

Final Rule

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

No exceptions without your human partner's permission.