Run tests

How to add a new test runner to the test goal.

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1. Set up a test target type

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Usually, you will want to add a "test" target type for your language, such as shell_test or python_test. A test target contrasts with a "source" target, such as shell_source. A test target is useful so that pants test :: doesn't try to run tests on non-test files.

When creating a test target, you should usually subclass SingleSourceField. You may also want to create TimeoutField (which should subclass IntField) and a SkipField (which should subclass BoolField).

See Creating new targets for a guide on how to define new target types.

from pants.engine.target import (
    COMMON_TARGET_FIELDS,
    Dependencies,
    BoolField,
    IntField,
    SingleSourceField,
    Target,
)


class ExampleTestSourceField(SingleSourceField):
    expected_file_extensions = (".example",)


class ExampleTestTimeoutField(IntField):
     alias = "timeout"
     help = "Whether to time out after a certain period of time"


class SkipExampleTestsField(BoolField):
    alias = "skip_example_tests"
    default = False
    help = "If set, don't run tests on this source"


class ExampleTestTarget(Target):
    alias = "example_tests"
    help = "Example tests run by some tool"
    core_fields = (
        *COMMON_TARGET_FIELDS,
        Dependencies,
        ExampleTestSourceField,
        ExampleTestTimeoutField,
        SkipExampleTestsField,
    )

2. Set up a subclass of TestFieldSet

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Your test-runner will need access to some / most of the fields defined on your new target to actually execute the tests within. Collect those fields into a new subclass of TestFieldSet, and mark at least your source field as required.

If you have a "skip" field, use it in an opt_out method of your subclass:

from pants.core.goals.test import TestFieldSet

@dataclass(frozen=True)
class ExampleTestFieldSet(TestFieldSet):
    required_fields = (ExamleTestSourceField,)
    sources: ExampleTestSourceField
    timeout: ExampleTestTimeoutField

    @classmethod
    def opt_out(cls, tgt: Target) -> bool:
        return tgt.get(SkipExampleTestsField).value

3. Set up a Subsystem for your test runner

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Test runners are expected to implement (at least) a skip option at a subsystem level.

from pants.option.option_types import SkipOption
from pants.option.subsystem import Subsystem

class ExampleTestSubsystem(Subsystem):
    name = "Example"
    options_scope = "example-test"
    help = "Some tool to run tests"

    skip = SkipOption("test")

See Options and subsystems for more information about defining new subsystems.

4. Set up a subclass of TestRequest

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The rules used to drive batching and executing tests come from the TestRequest class. To use it, first declare a new subclass pointing at your subclasses of TestFieldSet and Subsystem:

from pants.core.goals.test import TestRequest

@dataclass(frozen=True)
class ExampleTestRequest(TestRequest):
    field_set_type = ExampleTestFieldSet
    tool_subsystem = ExampleTestSubsystem

Then register the rules of your subclass:

def rules():
    return [
        # Add to any other existing rules here:
        *ExampleTestRequest.rules()
    ]

In addition to registering your subclass as a valid TestRequest, this will automatically register rules to handle splitting your test inputs into single-element batches. If this is the correct behavior for your test runner, you can move on and skip the following section about defining a batching/partitioning rule. On the other hand, if your test runner supports testing multiple files in a single process (i.e. to share expensive setup logic), you can override the default partitioner_type on your TestRequest subclass:

from pants.core.goals.test import PartitionerType

@dataclass(frozen=True)
class ExampleTestRequest(TestRequest):
    field_set_type = ExampleTestFieldSet
    tool_subsystem = ExampleTestSubsystem
    # Changed from the default:
    partitioner_type = PartitionerType.CUSTOM

This will prevent generation of the "default" partitioning rule, allowing you to implement a custom rule for grouping compatible tests into the same process.

5. Define a batching/partitioning @rule

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This step is optional

Defining a partitioning rule is only required if you overrode the partitioner_type field in your TestRequest subclass to be PartitionerType.CUSTOM. Skip to the next section if your subclass is using the default partitioner_type.

Pants can run tests from multiple targets/files within the same process (for example, to share expensive setup/teardown logic across multiple files). Since it's not always safe/possible to batch test files together, each plugin defining a test implementation is expected to define a @rule for splitting field-sets into appropriate batches:

from pants.core.goals.test import Partitions
from pants.engine.rules import collect_rules, rule

@rule
async def partition(
    request: ExampleTestRequest.PartitionRequest[ExampleTestFieldSet]
) -> Partitions:
    ...

def rules():
    return [
        # If it isn't already in the list:
        *collect_rules(),
    ]

The Partitions type is a custom collection of Partition objects, and a Partition is a dataclass containing:

• A tuple[TestFieldSetSubclass, ...] of partition elements
• An optional metadata field

Partition metadata can be any type implementing:

@property
def description(self) -> str:
    ...

Any metadata returned by the partitioning rule will be passed back to your test runner as an input to the test execution rule, so it can be useful to declare a custom type modeling everything that's constant for a collection of TestFieldSet inputs:

@dataclass(frozen=True)
class ExampleTestMetadata:
    common_property: str
    other_common_property: int | None

6. Define the main test execution @rule

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To actually execute your test runner, define a rule like:

from pants.core.goals.test import TestResult

@rule
async def run_example_tests(
    batch: ExampleTestRequest.Batch[ExampleTestFieldSet, ExampleTestMetadata],
    # Any other subsystems/inputs you need.
) -> TestResult:
    ...

If you didn't define a custom metadata type, you can use Any as the second type argument to the Batch type:

from pants.core.goals.test import TestResult

@rule
async def run_example_tests(
    batch: ExampleTestRequest.Batch[ExampleTestFieldSet, Any],
    # Any other subsystems/inputs you need.
) -> TestResult:
    ...

The batch input will have two properties:

1. elements contains all the field sets that should be tested by your runner
2. metadata contains any (optional) common data about the batch returned by your partitioning rule

If you didn't override the partitioner_type in your TestRequest subclass, elements will be a list of size 1 and metadata will be None. For convenience, you can use batch.single_element in this case to get the single field set. The single_element property will raise a TypeError if used on a batch with more than one element.

7. Define @rules for debug testing

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pants test exposes --debug and --debug-adapter options for interactive execution of tests. To hook into these execution modes, opt-in in your TestRequest subclass and define one/both additional rules:

from pants.core.goals.test import TestDebugAdapterRequest, TestDebugRequest
from pants.core.subsystems.debug_adapter import DebugAdapterSubsystem

@dataclass(frozen=True)
class ExampleTestRequest(TestRequest):
    ...  # Fields from earlier
    supports_debug = True  # Supports --debug
    supports_debug_adapter = True  # Supports --debug-adapter

@rule
async def setup_example_debug_test(
    batch: ExampleTestRequest.Batch[ExampleTestFieldSet, ExampleTestMetadata],
) -> TestDebugRequest:
    ...

@rule
async def setup_example_debug_adapter_test(
    batch: ExampleTestRequest.Batch[ExampleTestFieldSet, ExampleTestMetadata],
    debug_adapter: DebugAdapterSubsystem,
) -> TestDebugAdapterRequest:
    ...