Genera suites de test completas desde la descripción de una función

Why this prompt matters
Skipping edge cases is where most production bugs live. A function that works for normal inputs but silently returns wrong results for empty strings, zero values, or concurrent writes will pass a cursory manual test and still break in production. Developers without a structured checklist miss 30-50% of edge cases on average — this prompt enforces completeness every time.
What we use it for
You've just written a pricing function, an authentication handler, or a data-processing pipeline — and you need to ship it tomorrow. You paste the function into this prompt and get back a full test suite with happy paths, boundary values, and error scenarios already written, so you can move to code review instead of spending 90 minutes hand-crafting tests.
Prompt
Act as a senior QA engineer and software testing expert with 10+ years of experience writing test suites for production-grade code. I have a function or feature that needs comprehensive test coverage. Here is the description: [PASTE YOUR FUNCTION/FEATURE DESCRIPTION, CODE SNIPPET, OR SPECIFICATION HERE] Technology stack: [YOUR LANGUAGE AND FRAMEWORK — e.g., Python/pytest, JavaScript/Jest, TypeScript/Vitest, Java/JUnit, Go/testing] Testing framework: [YOUR TESTING FRAMEWORK] Code maturity: [NEW CODE / EXISTING CODE WITH KNOWN BUGS / REFACTORING IN PROGRESS] Your task: 1. Generate a complete set of unit tests covering: - Happy path (standard inputs with expected outputs) - Edge cases (boundary values, empty inputs, maximum inputs, special characters) - Error scenarios (invalid inputs, type mismatches, null/undefined, out-of-range values) - State mutations (if the function has side effects, test them explicitly) 2. List integration test outlines (what you'd test at the integration layer, without writing full code) 3. Identify any untestable scenarios and explain why (dependency on external systems, timing issues, etc.) 4. Add a one-line comment above each test explaining what it's asserting and why it matters Constraints: - Tests must be independent — no test should rely on another's side effects - Use descriptive test names in the format: [method]_[scenario]_[expected outcome] - Do NOT mock anything unless you explain exactly why the mock is necessary - Prioritize tests by impact: mark each as HIGH / MEDIUM / LOW risk Output format: ## Unit Tests (with priority labels) [Full test code in [YOUR TESTING FRAMEWORK]] ## Integration Test Outlines [Bullet list of integration scenarios to cover] ## Untestable Scenarios [List with explanations] ## Coverage Summary [Estimated line/branch coverage and what's missing]
Result
Here is the full test suite for calculate_discount(price, coupon_code, user_tier) using Python/pytest:
import pytest
from pricing import calculate_discount
# [HIGH] Standard discount applied correctly for a valid coupon and premium user
def test_calculate_discount_valid_coupon_premium_user_applies_discount():
assert calculate_discount(100.0, "SAVE20", "premium") == 80.0
# [HIGH] Free users receive no additional tier discount beyond coupon
def test_calculate_discount_valid_coupon_free_user_no_tier_bonus():
assert calculate_discount(100.0, "SAVE20", "free") == 80.0
# [HIGH] Invalid coupon code returns full price without discount
def test_calculate_discount_invalid_coupon_returns_full_price():
assert calculate_discount(100.0, "FAKECODE", "premium") == 100.0
# [HIGH] Price of zero returns zero regardless of coupon or tier
def test_calculate_discount_zero_price_returns_zero():
assert calculate_discount(0.0, "SAVE20", "premium") == 0.0
# [MEDIUM] Negative price raises ValueError — cannot discount negative amounts
def test_calculate_discount_negative_price_raises_value_error():
with pytest.raises(ValueError, match="Price must be non-negative"):
calculate_discount(-50.0, "SAVE20", "premium")
# [MEDIUM] Empty coupon string treated as no coupon — full price returned
def test_calculate_discount_empty_coupon_no_discount():
assert calculate_discount(100.0, "", "premium") == 100.0
# [MEDIUM] None coupon treated as no coupon — does not raise, returns full price
def test_calculate_discount_none_coupon_no_discount():
assert calculate_discount(100.0, None, "premium") == 100.0
# [MEDIUM] Unknown user_tier defaults to free-tier behavior, no tier bonus
def test_calculate_discount_unknown_tier_defaults_to_free():
assert calculate_discount(100.0, "SAVE20", "vip") == 80.0
# [LOW] Very large price value does not cause overflow or floating-point error
def test_calculate_discount_very_large_price_precision():
result = calculate_discount(1_000_000.0, "SAVE20", "premium")
assert abs(result - 800_000.0) < 0.01
# [LOW] Coupon code is case-insensitive — "save20" works like "SAVE20"
def test_calculate_discount_coupon_case_insensitive():
assert calculate_discount(100.0, "save20", "premium") == 80.0
Integration Test Outlines
- Apply coupon from the database — verify the discount matches the stored percentage for that coupon record
- Apply discount to a cart with multiple line items — verify totals are recalculated correctly
- Expired coupon in the database — verify
calculate_discountreturns full price and logs the expiry event - Concurrent requests with the same coupon (single-use coupon) — verify only one request succeeds
Untestable Scenarios
- Real-time coupon expiry race condition — coupon expires between the validity check and the discount application; requires a database transaction test outside unit scope
- External pricing API availability — if
calculate_discountcalls a third-party pricing service, that call cannot be reliably tested without a staging environment
Coverage Summary
Estimated line coverage: 94%. Estimated branch coverage: 87%. Missing: the internal retry logic on database timeout (lines 47-52) — requires a mock of the DB connection pool, which should be covered in an integration test.
Escribir tests es una de esas tareas que los desarrolladores saben que deberían hacer a fondo, pero rara vez lo hacen. No por pereza, sino por la presión del tiempo y la carga cognitiva que supone enumerar cada caso límite desde cero. Este prompt delega esa enumeración a la IA para que puedas centrarte en revisar el resultado en lugar de generarlo.
Qué hace diferente a este prompt
La mayoría de los prompts tipo "escribe tests para este código" devuelven un puñado de aserciones del camino feliz y se dan por satisfechos. Este prompt impone un contrato diferente: la IA debe recorrer cuatro categorías distintas antes de terminar — camino feliz, casos límite, escenarios de error y mutaciones de estado. Esa estructura captura el 30-50% de los casos límite que los desarrolladores suelen pasar por alto al escribir tests bajo presión de tiempo.
El formato de salida también importa. Exigir nombres de test descriptivos con el patrón [method]_[scenario]_[expected outcome] asegura que la suite sea legible meses después, cuando el autor original ya no esté. Exigir un comentario explicativo en cada test permite que un nuevo desarrollador entienda qué protege cada aserción, no solo qué hace.
Cómo usarlo
Pega tu función, una descripción en lenguaje natural, o incluso un documento de especificaciones en el campo entre corchetes. Especifica tu lenguaje y framework — la IA generará código ejecutable, no pseudocódigo. Configura el flag de madurez del código como EXISTING CODE WITH KNOWN BUGS y la IA también identificará las rutas propensas a defectos según la estructura del código.
La restricción de mocking es deliberada. Mockearlo todo es la forma más fácil de construir una suite de tests que pase pero no ofrezca confianza. Al exigir que la IA justifique cualquier mock que introduzca, mantienes la suite anclada en el comportamiento real.
Etiquetas de prioridad
Cada test recibe una etiqueta de riesgo HIGH / MEDIUM / LOW. Esto te permite saltarte los tests LOW durante un lanzamiento con restricciones de tiempo y retomarlos en el siguiente sprint — sin perder la pista de lo que se omitió. También acelera la revisión de código: los revisores pueden ver de un vistazo si las rutas de riesgo HIGH están cubiertas.
Funciona mejor con
Claude Sonnet 4.6 o GPT-4o. Para funciones complejas con condicionales profundamente anidados, Claude suele ofrecer una cobertura de ramas más exhaustiva. Para código desde cero en frameworks más nuevos, GPT-4o es igualmente capaz. Ambos modelos manejan este prompt de manera fiable — no uses un modelo más pequeño para esta tarea, ya que suelen omitir escenarios de error.