Derivar casos de prueba de una tabla de verdad sin probar todas las filas
Una tabla de verdad es una lista completa de entradas de prueba con resultados esperados; la cuestión es qué filas conservar. Para la regla de préstamo de abajo, cuatro condiciones y dieciséis filas, los pares de independencia del motor eligen cinco filas que muestran el efecto de cada condición, y la exportación a Python convierte cualquier fila en una comprobación ejecutable.
La regla es income_ok AND (credit_ok OR guarantor) AND NOT bankrupt. La tabla completa tiene dieciséis filas, cada una una prueba válida con un resultado esperado calculado a partir de la regla. Probar las dieciséis es razonable aquí e inviable con ocho condiciones, así que el método es conservar las filas que llevan información. Los pares de independencia son: income_ok, de sin ingresos con avalista (no elegible) a ingresos con avalista (elegible); credit_ok, de ingresos sin crédito ni avalista (no elegible) a con crédito (elegible); guarantor, de la misma base a con avalista (elegible); bankrupt, de elegible con avalista a en quiebra (no elegible).
Cinco filas distintas cubren los cuatro pares. Cada fila es una asignación completa y un resultado esperado, así que cada una es un caso de prueba; la exportación CSV las escribe con el resto de la tabla, y la exportación a Python da una función que evalúa la regla y rechaza cualquier entrada que no sea exactamente True o False, que es lo que una prueba parametrizada puede llamar.
Las filas que añadir además de los pares son las que codifican un requisito y no una condición: la fila todo FALSO, la fila todo VERDADERO (la quiebra gana), y cualquier contraejemplo que haya producido una versión anterior de la regla. La página de regresión y la de contraejemplos como pruebas cubren esas dos fuentes.
Ejemplo
eligible := income_ok AND (credit_ok OR guarantor) AND NOT bankrupt · salida
Comprobaciones exactas
Tabla de verdad (16 filas)
| # | income_ok | credit_ok | guarantor | bankrupt | eligible |
|---|---|---|---|---|---|
| 0 | FALSO | FALSO | FALSO | FALSO | FALSO |
| 1 | FALSO | FALSO | FALSO | VERDADERO | FALSO |
| 2 | FALSO | FALSO | VERDADERO | FALSO | FALSO |
| 3 | FALSO | FALSO | VERDADERO | VERDADERO | FALSO |
| 4 | FALSO | VERDADERO | FALSO | FALSO | FALSO |
| 5 | FALSO | VERDADERO | FALSO | VERDADERO | FALSO |
| 6 | FALSO | VERDADERO | VERDADERO | FALSO | FALSO |
| 7 | FALSO | VERDADERO | VERDADERO | VERDADERO | FALSO |
| 8 | VERDADERO | FALSO | FALSO | FALSO | FALSO |
| 9 | VERDADERO | FALSO | FALSO | VERDADERO | FALSO |
| 10 | VERDADERO | FALSO | VERDADERO | FALSO | VERDADERO |
| 11 | VERDADERO | FALSO | VERDADERO | VERDADERO | FALSO |
| 12 | VERDADERO | VERDADERO | FALSO | FALSO | VERDADERO |
| 13 | VERDADERO | VERDADERO | FALSO | VERDADERO | FALSO |
| 14 | VERDADERO | VERDADERO | VERDADERO | FALSO | VERDADERO |
| 15 | VERDADERO | VERDADERO | VERDADERO | VERDADERO | FALSO |
Lectura en lenguaje claro
Principianteeligible es verdadero exactamente cuando se cumplen todas las condiciones siguientes:
1. income_ok es verdadero
2. al menos uno entre credit_ok y guarantor es verdadero (pueden serlo ambos)
3. bankrupt es falso
En todos los demás casos, eligible es falso.
Sobre los operadores:
- AND (Y) solo es verdadero cuando todas sus partes son verdaderas.
- OR (O) es inclusivo: es verdadero en cuanto al menos una parte es verdadera, también cuando lo son varias.
- NOT (NO) invierte un valor: lo verdadero pasa a ser falso y lo falso, verdadero.
Intermedioeligible es verdadero exactamente cuando se cumplen todas las condiciones siguientes:
1. income_ok es verdadero
2. al menos uno entre credit_ok y guarantor es verdadero (pueden serlo ambos)
3. bankrupt es falso
En todos los demás casos, eligible es falso.
Avanzadoeligible es verdadero exactamente cuando se cumplen todas las condiciones siguientes [conjunción ∧]:
1. income_ok es verdadero
2. al menos uno entre credit_ok y guarantor es verdadero (pueden serlo ambos) [disyunción inclusiva ∨]
3. bankrupt es falso [negación ¬]
En todos los demás casos, eligible es falso.
Notación formal: income_ok ∧ (credit_ok ∨ guarantor) ∧ ¬bankrupt
Diagrama lógico
MC/DC
| Entrada | Estado | Par de independencia (solo esa entrada cambia) |
|---|---|---|
| income_ok | found | income_ok=F, credit_ok=F, guarantor=T, bankrupt=F → F | income_ok=T, credit_ok=F, guarantor=T, bankrupt=F → T |
| credit_ok | found | income_ok=T, credit_ok=F, guarantor=F, bankrupt=F → F | income_ok=T, credit_ok=T, guarantor=F, bankrupt=F → T |
| guarantor | found | income_ok=T, credit_ok=F, guarantor=F, bankrupt=F → F | income_ok=T, credit_ok=F, guarantor=T, bankrupt=F → T |
| bankrupt | found | income_ok=T, credit_ok=F, guarantor=T, bankrupt=F → T | income_ok=T, credit_ok=F, guarantor=T, bankrupt=T → F |
La misma regla como código
Python
# -*- coding: utf-8 -*-
# Generated by Tangibool (tangibool.python/1). Do not edit by hand; regenerate from the source revision.
# Model: not stated
# Source revision: reference
# Interpretation status: not stated
"""Tangibool evaluator (decision model).
Generated deterministically from the validated Boolean model; it evaluates exactly that
model. It does not establish that the model is a complete, legal, or safe statement of a
real-world rule, and evaluating examples does not verify the rule.
Source revision: reference
Interpretation status: not stated
Exporter: 1.0.0 (format tangibool.python/1; engine 1.0.0; semantics tangibool-semantics/1)
Logic digest: 40aeadff132d113e9694381e8ed2530396d9cd296f720e1c4c08565e8f3b69f4
Assumptions digest: d58ee1ad38e23e5093e4baa1047b5ae70d6972e08f555e34ac3fa16df63c4a78
Inputs (dict keys, declared order) and generated parameter names:
'income_ok' -> input_income_ok
'credit_ok' -> input_credit_ok
'guarantor' -> input_guarantor
'bankrupt' -> input_bankrupt
Outputs (declared order) and their functions:
'eligible' -> output_eligible(inputs)
Assumptions (the declared scope, checked by in_scope(inputs)):
none; every complete assignment is in scope.
Input contract:
Every required input must be exactly True or False (type(value) is bool). Missing
inputs, None, strings, integers (including 0 and 1), and keys that are not declared
inputs raise InputError before anything is evaluated, including inputs that a
short-circuiting operator would never read. Results are always bool.
Scope:
Decision functions raise OutOfScopeError when the inputs violate an assumption; an
out-of-scope assignment is never turned into a False result. Pass check_scope=False
to evaluate without the scope check, and use in_scope(inputs) to test it separately.
Usage:
import tangibool_model as model
model.evaluate({'income_ok': False, 'credit_ok': False, 'guarantor': False, 'bankrupt': False})
# -> {'eligible': False}
model.output_eligible({'income_ok': False, 'credit_ok': False, 'guarantor': False, 'bankrupt': False})
# -> False
model.evaluate({'income_ok': 1, 'credit_ok': False, 'guarantor': False, 'bankrupt': False})
# raises InputError: 1 is an int, not a bool
"""
import collections.abc as _abc
from typing import Dict, Mapping, Tuple
__all__ = [
"MODEL_INFO",
"IDENTIFIER_MAP",
"INPUT_NAMES",
"OUTPUT_NAMES",
"ASSUMPTIONS",
"InputError",
"OutOfScopeError",
"minterm_index",
"in_scope",
"evaluate",
"output_eligible",
]
MODEL_INFO = {
"format": "tangibool.python/1",
"mode": "decision",
"model": None,
"revision": "reference",
"interpretation": None,
"exporter": "1.0.0",
"engine_version": "1.0.0",
"semantics_version": "tangibool-semantics/1",
"logic_digest": "40aeadff132d113e9694381e8ed2530396d9cd296f720e1c4c08565e8f3b69f4",
"assumptions_digest": "d58ee1ad38e23e5093e4baa1047b5ae70d6972e08f555e34ac3fa16df63c4a78",
"primary_output": "eligible",
"unused_inputs": (),
"subcircuits": (),
}
# Stable ID -> generated identifier (reversible mapping; names are exact, escaped strings).
IDENTIFIER_MAP = {
"v_6cab253eb6ee": {"kind": "input", "name": "income_ok", "identifier": "input_income_ok"},
"v_d61ccf3c5e24": {"kind": "input", "name": "credit_ok", "identifier": "input_credit_ok"},
"v_a90721d191bc": {"kind": "input", "name": "guarantor", "identifier": "input_guarantor"},
"v_c9e9282e85b4": {"kind": "input", "name": "bankrupt", "identifier": "input_bankrupt"},
"f_a3631a5ff6db": {"kind": "output", "name": "eligible", "identifier": "output_eligible"},
}
INPUT_NAMES = ("income_ok", "credit_ok", "guarantor", "bankrupt")
OUTPUT_NAMES = ("eligible",)
ASSUMPTIONS = ()
_DECLARED_INPUTS = frozenset(INPUT_NAMES)
_SCOPE_INPUTS = ()
_ASSUMPTION_FORMULAS = {entry["name"]: entry["formula"] for entry in ASSUMPTIONS}
class InputError(ValueError):
"""Raised when inputs are missing, undeclared, or not exactly True/False.
Every required input must satisfy type(value) is bool. Missing inputs, None, strings,
integers (including 0 and 1), and keys that are not declared inputs are rejected before
anything is evaluated. A missing input is never treated as False. The individual problems
are listed in the problems attribute.
"""
def __init__(self, problems):
self.problems = tuple(problems)
super().__init__("Invalid inputs: " + "; ".join(self.problems))
class OutOfScopeError(ValueError):
"""Raised when the inputs violate the model's assumptions (its declared scope).
Outside its assumptions the rule has no decision: this error is never a False result.
The names of the violated assumptions are in the violated attribute.
"""
def __init__(self, violated):
self.violated = tuple(violated)
details = ", ".join(name + " (" + _ASSUMPTION_FORMULAS.get(name, "?") + ")" for name in self.violated)
super().__init__(
"Out of scope: the inputs violate "
+ ("assumption " if len(self.violated) == 1 else "assumptions ")
+ details
+ ". The rule has no decision outside its assumptions; this is not a False result."
)
def _short_repr(value: object) -> str:
text = repr(value)
return text if len(text) <= 60 else text[:57] + "..."
def _checked_inputs(inputs: Mapping[str, bool], required: Tuple[str, ...], check_scope: bool) -> Dict[str, bool]:
"""Validate inputs strictly before anything is evaluated and return a plain copy."""
if type(check_scope) is not bool:
raise TypeError("check_scope must be True or False")
if not isinstance(inputs, _abc.Mapping):
raise InputError(["inputs must be a mapping from input names to True/False, got " + type(inputs).__name__])
problems = []
for key in inputs:
if type(key) is not str or key not in _DECLARED_INPUTS:
declared = ", ".join(repr(name) for name in INPUT_NAMES) or "none"
problems.append("undeclared input " + _short_repr(key) + " (declared inputs: " + declared + ")")
needed = tuple(required) + (_SCOPE_INPUTS if check_scope else ())
for name in needed:
if name not in inputs:
problems.append("missing required input " + repr(name))
for key, value in inputs.items():
if type(key) is str and key in _DECLARED_INPUTS and type(value) is not bool:
problems.append(
"input " + repr(key) + " must be True or False (type bool), got "
+ type(value).__name__ + " " + _short_repr(value)
)
if problems:
raise InputError(list(dict.fromkeys(problems)))
return dict(inputs.items())
def _require_bool_args(function: str, pairs: Tuple[Tuple[str, object], ...]) -> None:
problems = [
"argument " + repr(name) + " of " + function + " must be True or False (type bool), got "
+ type(value).__name__ + " " + _short_repr(value)
for name, value in pairs
if type(value) is not bool
]
if problems:
raise InputError(problems)
def minterm_index(*values: bool) -> int:
"""Row index of an assignment over a declared variable order.
The first value is the most significant bit, so all-False is row 0. Used for indexed
minterm (sum) and maxterm (product) forms.
"""
index = 0
for value in values:
if type(value) is not bool:
raise InputError(["minterm_index() takes True/False values, got " + type(value).__name__ + " " + _short_repr(value)])
index = index * 2 + (1 if value else 0)
return index
def _scope_values(values: Dict[str, bool]) -> Tuple[Tuple[str, bool], ...]:
"""Value of every enabled, confirmed assumption for validated inputs, in declared order."""
return ()
def _require_in_scope(values: Dict[str, bool]) -> None:
violated = tuple(name for name, holds in _scope_values(values) if not holds)
if violated:
raise OutOfScopeError(violated)
def in_scope(inputs: Mapping[str, bool]) -> bool:
"""True when the inputs satisfy every assumption (the model's declared scope).
Requires the inputs the assumptions read; raises InputError for invalid inputs.
"""
values = _checked_inputs(inputs, (), True)
return all(holds for _, holds in _scope_values(values))
# ---- Formula bodies (inputs are validated by the public functions below) ----
def _compute_output_eligible(input_income_ok: bool, input_credit_ok: bool, input_guarantor: bool, input_bankrupt: bool) -> bool:
"""Decision output 'eligible': income_ok AND (credit_ok OR guarantor) AND NOT bankrupt"""
return input_income_ok and (input_credit_ok or input_guarantor) and (not input_bankrupt)
# ---- Public API ----
def output_eligible(inputs: Mapping[str, bool], *, check_scope: bool = True) -> bool:
"""Decision output 'eligible' (stable ID f_a3631a5ff6db).
'eligible' := income_ok AND (credit_ok OR guarantor) AND NOT bankrupt
Required inputs: 'income_ok', 'credit_ok', 'guarantor', 'bankrupt'.
Raises InputError for missing, undeclared, or non-bool inputs, and OutOfScopeError
when check_scope is True and the inputs violate an assumption.
"""
values = _checked_inputs(inputs, ("income_ok", "credit_ok", "guarantor", "bankrupt"), check_scope)
if check_scope:
_require_in_scope(values)
output_eligible_value = _compute_output_eligible(values["income_ok"], values["credit_ok"], values["guarantor"], values["bankrupt"])
return output_eligible_value
def evaluate(inputs: Mapping[str, bool], *, check_scope: bool = True) -> Dict[str, bool]:
"""Evaluate every output (declared order); each output is computed once.
Required inputs: 'income_ok', 'credit_ok', 'guarantor', 'bankrupt'.
Raises InputError for missing, undeclared, or non-bool inputs, and OutOfScopeError
when check_scope is True and the inputs violate an assumption.
"""
values = _checked_inputs(inputs, ("income_ok", "credit_ok", "guarantor", "bankrupt"), check_scope)
if check_scope:
_require_in_scope(values)
output_eligible_value = _compute_output_eligible(values["income_ok"], values["credit_ok"], values["guarantor"], values["bankrupt"])
return {
"eligible": output_eligible_value,
}
PostgreSQL
-- Tangibool PostgreSQL export (tangibool.postgresql/1)
-- Target: PostgreSQL 16 or later with typed boolean inputs; tested with PostgreSQL 18.3 (PGlite 0.5.8).
-- Not a claim of compatibility with other SQL dialects.
-- Model: not stated
-- Source revision: reference
-- Interpretation status: not stated
-- Exporter: 1.0.0 (engine 1.0.0; semantics tangibool-semantics/1)
-- Logic digest: 40aeadff132d113e9694381e8ed2530396d9cd296f720e1c4c08565e8f3b69f4
-- Assumptions digest: d58ee1ad38e23e5093e4baa1047b5ae70d6972e08f555e34ac3fa16df63c4a78
-- Mode: decision
--
-- Read-only: this file contains only WITH / VALUES / SELECT statements. No DDL, DML,
-- procedures, credentials, network access, or database changes. The expressions are pure
-- Boolean expressions, so their result does not depend on SQL evaluation order.
--
-- NON-NULL INPUT PRECONDITION: NULL is not a truth value of this rule. Every fragment below
-- is defined only when every input column it reads is a non-NULL boolean. The guard template
-- (section 2) marks any NULL input as 'invalid_input' and any row that violates the
-- assumptions as 'out_of_scope', and fills output columns only for 'ok' rows; a NULL in an
-- output column of another row is an envelope marker, not a third truth value. Never wrap
-- the fragments in COALESCE(..., FALSE) or otherwise choose a meaning for unknown inputs.
-- This evaluates the supplied model; it does not show that the model is a complete, legal,
-- or safe statement of a real-world rule.
--
-- Input columns (stable ID: name -> column):
-- v_6cab253eb6ee: income_ok -> "in_income_ok"
-- v_d61ccf3c5e24: credit_ok -> "in_credit_ok"
-- v_a90721d191bc: guarantor -> "in_guarantor"
-- v_c9e9282e85b4: bankrupt -> "in_bankrupt"
-- Output columns:
-- f_a3631a5ff6db: eligible -> "out_eligible"
-- Assumptions (scope K; exported separately and never folded into an output):
-- none; every complete input row is in scope.
-- ----------------------------------------------------------------------------------------------
-- 1. Expression fragments (parenthesised; non-NULL boolean inputs required)
-- ----------------------------------------------------------------------------------------------
--
-- [out_eligible] output eligible (stable ID f_a3631a5ff6db)
-- eligible := income_ok AND (credit_ok OR guarantor) AND NOT bankrupt
-- PRECONDITION: "in_income_ok", "in_credit_ok", "in_guarantor", "in_bankrupt" are non-NULL booleans.
-- ("in_income_ok" AND ("in_credit_ok" OR "in_guarantor") AND (NOT "in_bankrupt"))
--
-- ----------------------------------------------------------------------------------------------
-- 2. Guard template (read-only SELECT over an inputs list)
-- ----------------------------------------------------------------------------------------------
-- Replace the example rows between the markers with your own rows, or replace the inputs
-- CTE with a SELECT that provides the boolean columns listed in the header. status is
-- 'invalid_input' when any input is NULL, 'out_of_scope' when the assumptions are false, and
-- 'ok' otherwise; output columns are filled only for 'ok' rows (NULL elsewhere is an
-- envelope marker). Assumption columns are shown for rows with complete inputs.
WITH inputs ("row_id", "in_income_ok", "in_credit_ok", "in_guarantor", "in_bankrupt") AS (
-- tangibool:example-rows:begin
VALUES
(1, FALSE, FALSE, FALSE, FALSE),
(2, FALSE, FALSE, FALSE, TRUE),
(3, FALSE, FALSE, TRUE, FALSE),
(4, FALSE, FALSE, TRUE, TRUE),
(5, FALSE, TRUE, FALSE, FALSE),
(6, FALSE, TRUE, FALSE, TRUE),
(7, FALSE, TRUE, TRUE, FALSE),
(8, FALSE, TRUE, TRUE, TRUE),
(9, TRUE, FALSE, FALSE, FALSE),
(10, TRUE, FALSE, FALSE, TRUE),
(11, TRUE, FALSE, TRUE, FALSE),
(12, TRUE, FALSE, TRUE, TRUE),
(13, TRUE, TRUE, FALSE, FALSE),
(14, TRUE, TRUE, FALSE, TRUE),
(15, TRUE, TRUE, TRUE, FALSE),
(16, TRUE, TRUE, TRUE, TRUE),
(17, NULL::boolean, FALSE, FALSE, FALSE)
-- tangibool:example-rows:end
),
checked AS (
SELECT
inputs.*,
CASE
WHEN "in_income_ok" IS NULL OR "in_credit_ok" IS NULL OR "in_guarantor" IS NULL OR "in_bankrupt" IS NULL THEN 'invalid_input'
ELSE 'ok'
END AS "status"
FROM inputs
)
SELECT
"row_id",
"in_income_ok",
"in_credit_ok",
"in_guarantor",
"in_bankrupt",
"status",
CASE WHEN "status" = 'ok' THEN ("in_income_ok" AND ("in_credit_ok" OR "in_guarantor") AND (NOT "in_bankrupt")) END AS "out_eligible"
FROM checked
ORDER BY "row_id";
SMT-LIB
; Tangibool SMT-LIB export (tangibool.smtlib/1) ; Profile: SMT-LIB 2.6 Boolean subset (QF_UF): Bool constants, define-fun, let, push/pop, assert, check-sat, echo. ; Tested with Z3 5.1.0 (z3-solver 5.2.0, WebAssembly build). ; Model: not stated ; Source revision: reference ; Interpretation status: not stated ; Exporter: 1.0.0 (engine 1.0.0; semantics tangibool-semantics/1) ; Logic digest: 40aeadff132d113e9694381e8ed2530396d9cd296f720e1c4c08565e8f3b69f4 ; Assumptions digest: d58ee1ad38e23e5093e4baa1047b5ae70d6972e08f555e34ac3fa16df63c4a78 ; Mode: decision ; ; Reading results: each query block states what sat and unsat mean for its question. A sat ; answer comes with a witness or counterexample (ask for it with (get-model) after sat only); ; unsat settles that scoped query and is solver-checked, not an independently checked proof ; certificate; unknown settles nothing. Blocks are scoped with push/pop, so assertions never ; accumulate across questions. This checks the supplied model, not whether it is a complete, ; legal, or safe statement of a real-world rule. ; ; Symbols (stable ID: name -> symbol): ; v_6cab253eb6ee: income_ok -> in_income_ok ; v_d61ccf3c5e24: credit_ok -> in_credit_ok ; v_a90721d191bc: guarantor -> in_guarantor ; v_c9e9282e85b4: bankrupt -> in_bankrupt ; f_a3631a5ff6db: eligible -> out_eligible (set-option :produce-models true) (set-info :smt-lib-version 2.6) (set-logic QF_UF) ; ---- Inputs (declared order) (declare-const in_income_ok Bool) ; income_ok (declare-const in_credit_ok Bool) ; credit_ok (declare-const in_guarantor Bool) ; guarantor (declare-const in_bankrupt Bool) ; bankrupt ; ---- Outputs (dependencies first) ; output eligible (stable ID f_a3631a5ff6db): income_ok AND (credit_ok OR guarantor) AND NOT bankrupt (define-fun out_eligible () Bool (and in_income_ok (or in_credit_ok in_guarantor) (not in_bankrupt))) ; ---- Assumptions (the scope K; never folded into an output) ; K: conjunction of the enabled, confirmed assumptions (true when there are none) (define-fun K () Bool true) ; ---- Definitions only: nothing is asserted and no check is run. ; Add (assert ...) and (check-sat) inside (push 1) ... (pop 1) to ask a question.
LaTeX
eligible = \mathit{income\_ok} \cdot \left(\mathit{credit\_ok} + \mathit{guarantor}\right) \cdot \overline{\mathit{bankrupt}}El error habitual
Conservar solo las filas en las que la regla es VERDADERA. Un conjunto de pruebas con solicitantes elegibles demuestra que la regla admite; nunca comprueba que rechaza, y el par de bankrupt, de elegible a no elegible por una condición, es la fila que más importa.
Preguntas
¿Por qué no probar todas las filas?
Con cuatro condiciones puedes, y la exportación lo hace barato. El método es para las reglas en las que no puedes, y los pares son las filas que lamentarías descartar: cada una es la única evidencia de que alguna condición hace algo.
¿Cómo llegan los resultados esperados a las pruebas?
De la tabla de verdad, que es la regla hecha exhaustiva, y la regla de su requisito. La exportación a Python evalúa el modelo; no decide lo que el modelo debería decir.
Relacionado
Véase también
- Pares de independencia MC/DC, ejemplo resuelto con el informe de cobertura del motor
- Pruebas de regresión para reglas de decisión que cambian
- Convertir un contraejemplo en una prueba de regresión
- De expresión booleana a Python, SQL, SMT-LIB y LaTeX
- Generador de tablas de verdad: cualquier expresión booleana, hasta 12 variables, con columnas intermedias
- Elegibilidad de un préstamo: ingresos_ok AND (credito_ok OR avalista) AND NOT quiebra
- Operadores booleanos en Python: and, or, not, precedencia y errores comunes
- Lección 12: Diseñar pruebas que encuentren errores
Pruebas y reglas
- Tabla de decisión frente a tabla de verdad: cuándo usar cada una
- Prueba con tabla de decisión, ejemplo resuelto (estilo ISTQB Foundation 4.2.3)
- MC/DC explicado: cobertura de condición/decisión modificada para reglas booleanas
- Pares de independencia MC/DC, ejemplo resuelto con el informe de cobertura del motor
Generado a partir del motor de Tangibool, semántica tangibool-semantics/1, motor 1.0.0, compilación . Cada tabla, mapa, simplificación y veredicto de estas páginas lo calculó el motor de Tangibool al compilar el sitio, y cada afirmación la volvió a comprobar el mismo verificador exacto que usa la app. Los límites se indican donde aplican. © 2026 Foreranger LTD.