def all_conditions_failed(CG):
"""A specific data set that invalidates the criteria"""
#A requirement to pass the test is to have X=-1 as initial value
T = [{'X': -5}]
L = [[1, 2, 4, 5, "exit"], [1, 3, 4, 6, "exit"]]
all_conditions(CG, L, T)
def generate_data_set(CG):
L = [[1, 2, 4, 5, "exit"], [1, 3, 4, 6, "exit"]]
for path in L:
print(test_generation(CG, path))
if __name__ == "__main__":
print("Criteria 1 - all assigned")
print("Control Graph is CG_Project_Example")
print("\n")
CG1 = CG_Project_Example()
CG2 = CG_Project_Example()
all_conditions_passed(CG1)
print("\n")
all_conditions_failed(CG2)
print("\n")
print("Automatic generation of data set that validate the criteria:")
generate_data_set(CG1)
P = CSP(variables_domains)
# We add to the probelm binary constraint
for (x, y), constraints_x_y in constraints.items():
P.addConstraint(x, y, constraints_x_y)
# We return the solution of our problem
return P.solve()
def possible_variables(variables_domains, constraints):
"""compute the possible values of our variables in a given state and a set of constraints"""
result = []
variable_names = list(variables_domains.keys())
for possible_values in product(*(variables_domains.values())):
buffer_vars = dict()
for i, var in enumerate(variable_names):
buffer_vars[var] = possible_values[i]
if all((buffer_vars[var1], buffer_vars[var2]) in constraint
for (var1, var2), constraint in constraints.items()):
result.append(buffer_vars)
return result
if __name__ == "__main__":
CG = CG_Project_Example()
print(test_generation(CG, [1, 2, 4, 5, 'exit']))
print(test_generation(CG, [1, 2, 4, 6, 'exit']))