generator

CombinationGenerator(*discrete_domains)

Bases: IncrementalEnvironment

A generator that produces tests based on combination of ranges.

This generator creates a test case for each combination of the provided value ranges. Each value range must be associated with exactly one input feature of the model that shall be tested.

Initialize the combination generator.

Parameters:

Name	Type	Description	Default
discrete_domains	Discrete	A list of discrete domains to generate test cases from. Each domain must be associated with exactly one input feature of the model that shall be tested.	()

Source code in src/flowcean/core/tool/testing/generator/combination_generator.py

def __init__(self, *discrete_domains: Discrete) -> None:
    """Initialize the combination generator.

    Args:
        discrete_domains: A list of discrete domains to generate test cases
            from. Each domain must be associated with exactly one input
            feature of the model that shall be tested.
    """
    super().__init__()

    # Check if no duplicate feature names are present
    feature_names_count: dict[str, int] = {}
    for domain in discrete_domains:
        feature_names_count[domain.feature_name] = (
            feature_names_count.get(
                domain.feature_name,
                0,
            )
            + 1
        )
    if len(feature_names_count) != len(discrete_domains):
        msg = "Duplicate features found: "
        msg += ", ".join(
            f"{feature_name}: {count}"
            for feature_name, count in feature_names_count.items()
            if count > 1
        )
        raise ValueError(msg)

    self.domains = discrete_domains

    # Calculate the number of test cases
    self.number_test_cases = functools.reduce(
        lambda n, range_: n * len(range_),
        self.domains,
        1,
    )

    # Build the product iterator
    self.product_iterator = itertools.product(
        *self.domains,
    )

    # Perform the first step
    self.step()

StochasticGenerator(domains, *, test_case_count=None, seed=0)

Bases: IncrementalEnvironment

A generator that produces random tests based on given domains.

Initialize the stochastic generator.

Parameters:

Name	Type	Description	Default
domains	list[Domain]	A list of domains to generate random values for. Each domain must be associated with exactly one input feature of the model that shall be tested.	required
test_case_count	int | None	The number of test cases to generate. If None, the generator will run indefinitely.	None
seed	int	The seed for the random number generator. The default is 0, which means a random seed will be used.	0

Source code in src/flowcean/core/tool/testing/generator/stochastic_generator.py

def __init__(
    self,
    domains: list[Domain],
    *,
    test_case_count: int | None = None,
    seed: int = 0,
) -> None:
    """Initialize the stochastic generator.

    Args:
        domains: A list of domains to generate random values for.
            Each domain must be associated with exactly one input
            feature of the model that shall be tested.
        test_case_count: The number of test cases to generate. If None,
            the generator will run indefinitely.
        seed: The seed for the random number generator. The default is 0,
            which means a random seed will be used.
    """
    super().__init__()

    # Check if no duplicate feature names are present
    feature_names_count: dict[str, int] = {}
    for domain in domains:
        feature_names_count[domain.feature_name] = (
            feature_names_count.get(
                domain.feature_name,
                0,
            )
            + 1
        )
    if len(feature_names_count) != len(domains):
        msg = "Duplicate features found: "
        msg += ", ".join(
            f"{feature_name}: {count}"
            for feature_name, count in feature_names_count.items()
            if count > 1
        )
        raise ValueError(msg)

    # Seed all domains
    rng = random.Random(seed) if seed != 0 else random.Random()
    for domain in domains:
        domain.set_seed(rng.randint(0, 2**32 - 1))

    self.domains = domains
    self.count = 0
    self.number_test_cases = test_case_count
    # Perform the first step to initialize the generator
    self.step()