MultiTest¶

Introduction¶

MultiTest is a functional testing framework. Unlike unit testing frameworks, it can be used to drive many processes at once, and interact with them from the outside, for example through tcp connections.

MultiTest testcases are written in Python and are not inherently limited to a set of APIs or functionalities. While the library provides a large amount of drivers and assertions, users can easily provide their own as well.

For API documentation see the MultiTest class reference.

Usage¶

A MultiTest instance can be constructed from the following parameters:

Name: The name is internal to Testplan, and is used as a handle on the test, for example when using the --patterns command line option to filter tests, or in the report. It must be unique inside a given Testplan. Note that you can select individual testcases in a MultiTest suite by separating them from the suite name with a semicolon. For example: --patterns MultiTestSuite:testcase.
Description: The description will be printed in the report, below the test status. It’s a free-form string, spaces and line returns will be displayed as they are specified.
Suites: MultiTest suites are simply objects (one or more can be passed) that must:
- be decorated with @testsuite.
- have one or more methods decorated with @testcase. @testcase will enforce at import time that the method designated as a testcase has the following signature:
```
@testcase
def a_testcase_method(self, env, result):
  ...
```
The @testcase decorated methods will execute in the order in which they are defined. If more than one suite is passed, the suites will be executed in the order in which they are placed in the list that is used to pass them to the constructor. To change testsuite and testcase execution order, click here.
Environment: The environment is a list of drivers. Drivers are typically implementations of messaging, protocols or external executables. If a testcase is intended to test an executable, a driver for that executable would typically be defined, as well as drivers for the interfaces that are required for interacting with it, such as network connections.
Dependencies: The dependencies is a dict with both keys and values of drivers or iterable collection of drivers. Drivers on the value side should only start after drivers on the key side are fully started. When specified, Testplan will try to schedule more drivers starting concurrently based on the dependencies. Drivers included in environment while not presented in dependencies will be started concurrently at the very beginning. Using empty dict here to instruct all drivers to start concurrently. Using None here to specify the legacy scheduling with async_start flags being set. Click here for more information.
Runtime Information: The environment always contains a member called runtime_info which contains information about the current state of the run. See: MultiTestRuntimeInfo
Initial Context: The initial context is an optional way to pass information to be used by drivers and from within testcases. When drivers are added, they are provided with access to the driver environment that also contains the initial_context input. This mechanism is useful to let drivers know for example how to connect to other drivers. It is possible to use the initial context to pass global values that will be available to all drivers during startup and testcases during execution. Example of initial context can be found here
Hooks: Hooks are used to implement measures that complement the testing process with necessary preparations and subsequent actions. See example.
- before_start: Callable to execute before starting the environment.
- after_start: Callable to execute after starting the environment.
- before_stop: Callable to execute before stopping the environment.
- after_stop: Callable to execute after stopping the environment.
- error_handler: Callable to execute when a step hits an exception.

Example¶

This is an example MultiTest that will start an environment of three drivers and execute three testsuites that contain testcases. From within the testcases, the interaction with the drivers is done with the env argument.

@testsuite
class DriverInteraction(object):

    @testcase
    def restart_app(self, env, result):
        env.converter.restart()
        env.server.accept_connection()
        env.client.restart()

        size = env.server.send_text('hello')
        result.equal('Hello', env.client.receive_text(size=size))

...

MultiTest(name='TestConnections',
          environment=[
               TCPServer(name='server'),
               Bridge(name='bridge',
                      binary=os.path.join(os.getcwd(), 'run_bridge.py')),
               TCPClient(name='client',
                         host=context(converter_name, '{{host}}'),
                         port=context(converter_name, '{{port}}'))
          ],
          suites=[BasicTests(), EdgeCases(), DriverInteraction()])

Many more commented examples are available here.

Testsuites & Testcases¶

Testsuites are @testsuite decorated classes that contain @testcase decorated methods that are representing the actual tests in which assertions are performed.

Multitest accepts a list of testsuites. This may be very useful in case different suites share the same environment. The lifetime of the drivers in respect to multiple suites is the following:

Start each driver in the environment in sequence

Run Suite1

Run Suite2 and any others

Stop each driver in reverse order

Name customization¶

By default, a testsuite is identified in the report by its class name, and testcase by its function name. User can specify custom name for testsuite or testcase like this:

@testcase(name=”My Testcase”)

@testsuite(name=”My Test Suite”)

@testsuite(name=lambda cls_name, suite: “{} – {}”.format(cls_name, id(suite)))

Example can be found here.

To customize names for parametrized testcases another argument name_func can be used, refer to the document of name_func.

Strict order¶

In a test suite all testcases can be forced to run sequentially, which means, they will be executed strictly in the order as they were defined, even some paralell feature like “shuffling” and “execution group” will not take effect. Specify such a test suite like this:

@testsuite(strict_order=True)

When executed in interactive mode, UI can help user to run testcases one by one, that is, in a “strict ordered” test suite, only the first testcase can run, after it finishes its execution then the next testcase is able to run, and there is no idea to re-run the finished testcases unless the whole test report is reset. Similarly, you cannot run a test suite if some testcases in it already finish execution or are running, an error message will be displayed.

Listing¶

Testplan supports listing of all defined tests by command line or programmatic means. Test listing is also compatible with test filters and sorters, meaning you can see how the various filtering / ordering rules would affect your tests, before actually running your plan.

Command line Listing¶

The simplest usage --list will list all tests in readable & indented format.

This is also a shortcut for --info name. The output will be trimmed per suite if number of testcases exceed a certain number (This is most likely to happen when testcase parametrization is used). --info name-full argument will display the full list of all testcases, without trimming the output.

$ test_plan.py --list
Primary
  AlphaSuite
    testcase_a
    testcase_b
    ...

--info pattern argument will list the testcases in a format that is compatible with the --patterns and --tags / --tags-all arguments. Again some testcases may be trimmed (per suite) if they exceed a certain number, and --info pattern-full argument will display the full list of all testcases without any trimming the output.

$ test_plan.py --info pattern
Primary
Primary::AlphaSuite
  Primary::AlphaSuite::testcase_a
  Primary::AlphaSuite::testcase_b
  ...

--info count is a rather short way of listing tests, it will just print out the list of multitests and the number of testsuites & testcases:

$ test_plan.py --info count
Primary: (2 suites, 6 testcases)
Secondary: (1 suite, 3 testcases)

--info json dumps many metadata about the testplan with testsuite and testcase locations. It is useful for tools that want to gain info about tests without running them. It has a form: --info json:/path/to/file.json in which case the json is saved to /path/to/file.json instead of dumping to the stdout.

More examples on command line test listing can be seen here.

Programmatic Listing¶

Similar test listing functionality can be achieved by passing test lister objects to @test_plan decorator via test_lister argument:

from testplan import test_plan
from testplan.testing.listing import PatternLister

# equivalent to `--list` or `--info name`
@test_plan(test_lister=NameLister()):
def main(plan):
  ....

from testplan import test_plan
from testplan.testing.listing import NameLister


# equivalent to `--info pattern`
@test_plan(test_lister=PatternLister()):
def main(plan):
  ....

More examples on programmatic test listing can be seen here.

Custom Test Listers¶

A custom test lister can be implemented by subclassing BaseLister or MetadataBasedLister

and overriding get_output method. The difference is that in Old BaseLister style the get_output is called with all Test instance added to the plan one by one while the MetadataBasedLister case is called with TestPlanMetadata, which contains all info about the testplan.

An example implementation of custom test lister can be seen here.

Listers can be registered to be used with the --info commandline parameter the same way as the built in listers.

The custom lister class should provide:

it’s name either setting the NAME or override the name() method. This should be an Enum name like NAME_FULL. The name will be used to derive the commandline param which is the kebab-case version of the name.
and it’s description either setting the DESCRIPTION or override the description() method

and it need to be registered with testplan.testing.listing.listing_registry as follows

from testplan.testing.listing import BaseLister, listing_registry
from testplan import test_plan

class HelloWorldLister(BaseLister):

  NAME = "HELLO_WORLD"
  DESCRIPTION = "This lister print Hello World for each multitest"

  def get_output(self, instance):
      return "Hello World"

listing_registry.add_lister(HelloWorldLister())

# check --info hello-world
@test_plan()
def main(plan):
  ....

the full example can be found here.

The MetadataBasedLister types can take not just a name in the --info but even an uri where the path will be used as the listing location, and the result is written to a file instead of the stdout. Currently, the only such lister is json. Example call --info josn:/path/to/file.json

Warning

For filtering / ordering / listing operations, programmatic declarations will take precedence over command line arguments, meaning command line arguments will NOT take on effect if there is an explicit test_filter/test_sorter/test_lister argument in the @test_plan declaration.

Filtering¶

Testplan provides a flexible and customizable interface for test filtering (e.g. running a subset of tests). It has built-in logic for tag and pattern based test filtering, which can further be expanded by implementing custom test filters.

Command line filtering¶

The simplest way to filter tests is to use pattern (--patterns) or tag (--tags / --tags-all) filters via command line options.

For pattern filtering individual tests or sets of tests to run can be selected by passing their name or a glob pattern, for example \*string\* will match all testcases whose name includes string.

Note that for MultiTest, the : separator can be used to select individual testsuites and individual testcase methods inside those testsuites; e.g. --patterns MyMultiTest:Suite:test_method. This can of course be combined with wildcarding; e.g. --patterns MyMultiTest:Suite:test_* or --patterns MyMultiTest:*:test_*.

Details regarding the supported patterns can be found here , they’re essentially identical to traditional UNIX shell globbing.

It is also possible to run tests for particular tag(s) using --tags or --tags-all arguments. (e.g. --tags tag1 tag-group=tag1,tag2,tag3). --tags will run tests that match ANY of the tag parameters whereas --tags-all will only run the tests that match ALL tag parameters.

When --patterns and --tags parameters are used together, Testplan will only run the tests that match BOTH the pattern and the tag arguments.

View Tagging section and Tagging and Filtering downloadable examples for more detailed information on tagging and command line filtering usage.

Programmatic Filtering¶

It is also possible to filter out tests to be run via programmatic means by passing a filter object to the @test_plan decorator as test_filter argument. This feature enables more complex filtering logic. For the pattern and tag filters mentioned above, their equivalent programmatic declarations would be:

from testplan import test_plan
from testplan.testing.filtering import Tags, Pattern

# equivalent to `--patterns MyMultiTest:Suite:test_method`
@test_plan(test_filter=Pattern('MyMultiTest:Suite:test_method')):
def main(plan):
    ....

# equivalent to `--tags tag1 tag-group=tag1,tag2,tag3`
@test_plan(test_filter=Tags({
    'simple': 'tag1',
    'tag-group': ('tag1', 'tag2', 'tag3')
})):
def main(plan):
    ....

Programmatic filters can be composed via bitwise operators, so it is possible to apply more complex filtering logic which may not be supported via command line options only.

from testplan import test_plan
from testplan.testing.filtering import Tags, Pattern

# equivalent to `--patterns MyMultiTest --tags server'
@test_plan(test_filter=Pattern('MyMultiTest') & Tags('server')):
def main(plan):
     ....

# no command line equivalent, run tests that match the pattern OR the tag
@test_plan(test_filter=Pattern('MyMultiTest') | Tags('server')):
def main(plan):
    ....

# no command line equivalent, run tests that DO NOT match the tag `server`
@test_plan(test_filter=~Tags('server')):
def main(plan):
     ....

See some examples demonstrating programmatic test filtering.

Multi-level Filtering¶

For more granular test filtering, you can pass test filter objects to MultiTest instances as well. These lower level filtering rules will override plan level filters.

from testplan import test_plan
from testplan.testing.filtering import Tags, Pattern
from testplan.testing.multitest import MultiTest

# Plan level test filter that will run tests tagged with `client`
@test_plan(test_filter=Tags('client')):
def main(plan):
    multitest_1 = MultiTest(name='Primary', ...)
    # Multitest level test filter overrides plan level filter and runs
    # tests tagged with `server`
    multitest_2 = MultiTest(name='Secondary', test_filter=Tags('server'))

See some examples explaining multi-level programmatic test filtering.

Custom Test Filters¶

Testplan supports custom test filters, which can be implemented by subclassing testplan.testing.filtering.Filter and overriding filter_test, filter_suite and filter_case methods.

Example implementations can be seen here.

Ordering Tests¶

By default Testplan runs the tests in the following order:

Test instances (e.g. MultiTests) are being executed in the order they are added to the plan object with plan.add() method.

Test suites are run in the order they are added to the test instance via suites list.

Testcase methods are run in their declaration order in the testsuite class.

This logic can be changed by use of custom or built-in test sorters.

Command line ordering¶

Currently Testplan supports only shuffle ordering via command line options. Sample usage includes:

$ test_plan.py --shuffle testcases
$ test_plan.py --shuffle suites testcases --shuffle-seed 932
$ test_plan.py --shuffle all

Please see the section on Shuffling for more detailed information and benefits of shuffling your test run order.

Programmatic Ordering¶

To modify test run order programmatically, we can pass a test sorter instance to @test_plan decorator via test_sorter argument.

from testplan import test_plan
from testplan.testing.ordering import ShuffleSorter

# equivalent to `--shuffle all --shuffle-seed 15.2`
@test_plan(test_sorter=ShuffleSorter(shuffle_type='all', seed=15.2)):
def main(plan):
    ....

from testplan import test_plan
from testplan.testing.ordering import AlphanumericalSorter

# no command line equivalent, sort everything alphabetically
@test_plan(test_sorter=AlphanumericalSorter(sort_type='all')):
def main(plan):
    ....

More examples explaining programmatic test ordering can be seen here.

Custom Test Sorters¶

A custom test sorter can easily be implemented by subclassing testplan.testing.ordering.TypedSorter and overriding sort_instances, sort_testsuites, sort__testcases methods.

An example implementation of custom test sorter can be seen here.

Multi-level Test Ordering¶

For more granular test ordering, test sorters can be passed to MultiTest objects via test_sorter argument as well. These lower level ordering rules will override plan level sorters.

from testplan import test_plan
from testplan.testing.ordering import ShuffleSorter, AlphanumericalSorter
from testplan.testing.multitest import MultiTest

# Shuffle all testcases of all tests
@test_plan(test_sorter=ShuffleSorter('testcases')):
def main(plan):
    multitest_1 = MultiTest(name='Primary',
                            ...)
    # Run test cases in alphabetical ordering, override plan level sorter
    multitest_2 = MultiTest(name='Secondary',
                            test_sorter=AlphanumericalSorter('testcases'),
                            ...)

More examples explaining multi-level programmatic test ordering can be seen here.

Shuffling¶

Testplan provides command line shuffling functionality via --shuffle and --shuffle-seed arguments. These can be used to randomise the order in which tests are run. We strongly recommend to use them in routine cases.

Why is this useful?

Some bugs may only appear in some states of the application under test.

Some tests may not finish cleanly but because of their position in a testsuite that error remains unseen.

Some tests may make assumptions on the availability of data that are only valid thanks to other tests being run first.

All these cases can be invisible when tests are always run in the same order. Randomizing the order in which tests are run can help unmask these issues.

What does it do?

Given the following tests definitions:

@testsuite
class TestSuite1(object):
    @testcase
    def test11(self, env, result):
        pass

    @testcase
    def test12(self, env, result):
        pass

@testsuite
class TestSuite2(object):
    @testcase
    def test21(self, env, result):
        pass
    @testcase
    def test22(self, env, result):
        pass

@testsuite
class TestSuite3(object):
    @testcase
    def test31(self, env, result):
        pass
    @testcase
    def test32(self, env, result):
        pass

@testsuite
class TestSuite4(object):
    @testcase
    def test41(self, env, result):
        pass
    @testcase
    def test42(self, env, result):
        pass

plan.add(MultiTest(name="A",
                   description="A Description",
                   suites=[TestSuite1(), TestSuite2()]))
plan.add(MultiTest(name="B",
                   description="B description",
                   suites=[TestSuite3(), TestSuite4()]))

--shuffle instances will shuffle the order in which MultiTest instances are executed. The following definitions will be executed as A then B or B then A, with the order of the suites in each preserved, and the order of testcases in suites is also preserved. This is useful if the environment is shared between the instances and you want to make sure that there is no cross-contamination.
--shuffle suites will preserve the order of the MultiTest instances, the order of testcases but shuffle the order of the suites inside each MultiTest instance. So the execution of the above snippet would be for instance:
A:
TestSuite2 : test21, test22

TestSuite1 : test11, test12
B:
TestSuite3 : test31, test32

TestSuite4 : test41, test42
--shuffle testcases will preserve the order of MultiTest instances and suites but will change the order of testcases.
A:
TestSuite1 : test12, test11

TestSuite2 : test21, test22
B:
TestSuite3 : test32, test31

TestSuite4 : test42, test41
--shuffle all will randomise all MultiTest, suites and cases.
B:
TestSuite4 : test42, test41

TestSuite3 : test32, test31
A:
TestSuite1 : test11, test12

TestSuite2 : test21, test22

How can I troubleshoot a problem?

The goal of --shuffle being to find out problems only detectable in random execution ordering, sometimes one needs to be able to replicate the ordering from a past run. When using the --shuffle option, testplan will output the seed with which the randomizer was initialised. Passing that seed back to --shuffle-seed will make sure your tests are run in the order that uncovered the problem again. The output looks as follow : Shuffle seed: 9151.0, to run again in the same order pass --shuffle all --shuffle-seed 9151.0

Tagging¶

Testplan supports test filtering via tags, which can be assigned to top level tests via tags argument (e.g. GTest(name='CPP Tests', tags='TagA'), MultiTest(name='My Test', tags=('TagB', 'TagC')). MultiTest framework also has further support for suite and testcase level tagging as well.

It’s possible to run subset of tests using --tags or --tags-all arguments. The difference between --tags and --tags-all is that --tags tagA tagB will run any test that is tagged with tagA OR tagB whereas --tags-all tagA tagB will run tests that are tagged with both tagA AND tagB.

Note

If you apply the same tag value both on suite level and testcase level, the tag filtering will still work as expected. However keep in mind that applying the same testsuite tag explicitly to a testcase is a redundant operation.

There are multiple ways to assign tags to a target:

Assign a simple tag: @testcase(tags='tagA')

Assign multiple simple tags: @testcase(tags=('tagA', 'tagB'))

Assign a named tag: @testcase(tags={'tag_name': 'tagC'})

Assign multiple named tags: @testcase(tags={'tag_name': ('tagC', 'tagD'), 'tag_name_2': 'tagE'})

While passing command line arguments use tag values directly for simple tag matches and <TAG_NAME>=<TAG_VALUE_1>,<TAG_VALUE_2>... convention for named tag matches:

Filter on a single simple tag: --tags tagA

Filter on multiple simple tags --tags tagA tagB

Filter on single named tag: --tags tag_name_1=tagC

Filter on multiple named tags: --tags tag_name_1=tagC,tagD tag_name_2=tagE

Filter on both simple and named tags: --tags tagA tagB tag_name_1=tagC,tagD

Tag format¶

Tag values and names can consist of alphanumerical characters, as well as underscore, dash, parentheses _-)(, and can also contain whitespaces. However they cannot start or end with these special characters.

Valid: tagA, tag-A, tag_A, tag()A, 'tag A'

Invalid: -tagA, tagA_, (tagA), ' tagA '

Simple tags vs named tags¶

It’s up to the developer to decide on the tagging strategy, both simple and named tags have different advantages:

Simple tags are easier to use and have a simpler API, whereas named tagging needs a little bit of extra typing.

Named tags let you categorize tags into different groups and enables finer tuning on test filtering. (E.g run all tests for a particular regulation on a particular protocol: --tags-all regulation=EMIR protocol=TCP).

Simple tags may cause confusion within different contexts: e.g. @testcase(tags='slow'), is this a testcase with slow startup time, or does it test a piece of code that runs slowly?

A general piece of advice would be to use simple tags when introducing this functionality to your tests, and gradually upgrade to named tags after you feel more comfortable.

Example¶

# Top level test instance tagging
my_gtest = GTest(name='My GTest', tags='tagA')

# Testsuite & test case level tagging

@testsuite(tags='tagA')
class SampleTestAlpha(object):

    @testcase
    def method_1(self, env, result):
        ...

    @testcase(tags='tagB')
    def method_2(self, env, result):
        ...

    @testcase(tags={'category': 'tagC')
    def method_3(self, env, result):
        ...

    @testcase(tags='category': 'tagD')
    def method_4(self, env, result):
        ...


@testsuite(tags='tagB')
class SampleTestBeta(object):

    @testcase
    def method_1(self, env, result):
      ...

    @testcase(tags=('tagA', 'tagC'))
    def method_2(self, env, result):
      ...

    @testcase(tags={'category': ['tagC', 'tagD'])
    def method_3(self, env, result):
      ...


my_multitest = MultiTest(
    name='My MultiTest', tags=['tagE', 'tagF']
    suites=[SampleTestAlpha(), SampleTestBeta()])

Runs all testcases from SampleTestAlpha (suite level match), SampleTestBeta.test_method_2, SampleTestBeta.test_method_3 (testcase level match):

$ ./test_plan.py --tags tagA

Runs all tests from both SampleTestAlpha and SampleTestBeta (suite level match):

$ ./test_plan.py --tags tagA tagB

--tags-all runs the test if and only if all tags match. Runs SampleTestAlpha.test_method_2, SampleTestBeta.test_method_2, SampleTestBeta.test_method_3:

$ ./test_plan.py --tags-all tagA tagB

Runs SampleTestAlpha.test_method_3, SampleTestAlpha.test_method_4, SampleTestBeta.test_method_3:

$ ./test_plan.py --tags category=tagC,tagD

Runs SampleTestBeta.test_method_3 (both tag values must match):

$ ./test_plan.py --tags-all category=tagC,tagD

For more detailed examples, see here.

Tag based multiple reports¶

Multiple PDF reports can be created for tag combinations. See a downloadable example that demonstrates how this can be done programmatically and via command line.

Parametrization¶

Testplan makes it possible to write more compact testcases via use of @testcase(parameters=...) syntax. There are 2 types of parametrization: simple and combinatorial. In both cases you need to:

Add extra arguments to the testcase method declaration.

Pass either a dictionary of lists/tuples or list of dictionaries/tuples as parameters value.

Simple Parametrization¶

You can add simple parametrization support to a testcase by passing a list/tuple of items as parameters value. Each item of the tuple must either be:

A tuple / list with positional values that correspond to the parametrized argument names in the method definition.

A dict that has matching keys & values to the parametrized argument names.

A single value (that is not a tuple, or list) if and only if there is a single parametrization argument. This is more of a shortcut for readability.

The @testcase decorator will generate 2 testcase methods using each element in the parameters tuple below:

@testsuite
class SampleTest(object):

  @testcase(
      parameters=(
          # Tuple notation, assigns values to `a`, `b`, `expected` positionally
          (5, 10, 15),
          (-2, 3, 1),
          (2.2, 4.1, 6.3),
          # Dict notation, assigns values to `a`, `b`, `expected` explicitly
          {'b': 2, 'expected': 12, 'a': 10},
          {'a': 'foo', 'b': 'bar', 'expected': 'foobar'}
      )
  )
  def addition(self, env, result, a, b, expected):
      result.equal(a + b, expected)
      # The call order for the generated methods will be as follows:
      # result.equal(5 + 10, 15)
      # result.equal(-2 + 3, 1)
      # result.equal(2.2 + 4.1, 6.3)
      # result.equal(10 + 2, 12)
      # result.equal('foo' + 'bar', 'foobar')

  #  Shortcut notation that uses single values for single argument parametrization
  #  Assigns 1, 2, 3, 4 to `value` for each generated test case
  #  Verbose notation would be `parameters=((2,), (4,), (6,), (8,))` which
  #  is not that readable.
  @testcase(parameters=(2, 4, 6, 8))
  def is_even(self, env, result, value):
      result.equal(value % 2, 0)

Combinatorial Parametrization¶

If you pass a dictionary of lists/tuples as parameters value, @testcase decorator will then generate new test methods using a cartesian product of all of the values from each element. This can be useful if you would like to run a test using a combination of all possible values.

The example below will generate 27 (3 x 3 x 3) test case methods for each possible combination of the values from each dict item.

@testsuite
class SampleTest(object):

  @testcase(parameters={
      'first_name': ['Ben', 'Michael', 'John'],
      'middle_name': ['Richard', 'P.', None],
      'last_name': ['Brown', 'van der Heide', "O'Connell"]
  })
  def form_validation(self, env, result, first_name, middle_name, last_name):
      """Test if form validation accepts a variety of inputs"""
      form = NameForm()
      form.validate(first_name, middle_name, last_name)

      # The call order for the generated methods will be:
      # form.validate('Ben', 'Richard', 'Brown')
      # form.validate('Ben', 'Richard', 'van der Heide')
      # form.validate('Ben', 'Richard', "O'Connell")
      # form.validate('Ben', 'P.', 'Brown')
      # ...
      # ...
      # form.validate('John', None, 'van der Heide')
      # form.validate('John', None, "O'Connell")

This is equivalent to declaring each method call explicitly:

@testsuite
class SampleTest(object):

  @testcase(parameters=(
      ('Ben', 'Richard', 'Brown'),
      ('Ben', 'Richard', 'van der Heide'),
      ('Ben', 'Richard', "O'Connell"),
      ('Ben', 'P.', "Brown"),
      ...
  ))
  def form_validation(self, env, result, first_name, middle_name, last_name):
      """Test if form validation accepts a variety of inputs"""
      ...

See the addition_associativity test in the downloadable example.

If a pre_testcase or post_testcase method is defined in test suite and used along with parameterized testcases, then it can have an extra argument named kwargs to access the parameters of the associated testcase, for non parameterized testcases an empty dictionary is passed for kwargs.

@testcase(parameters=(("foo", "bar"), ("baz", "quz")))
def sample_test(self, env, result, x, y):
    pass

def pre_testcase(name, self, env, result, kwargs):
    result.log("Param 1 is {}".format(kwargs.get("x")))
    result.log("Param 2 is {}".format(kwargs.get("y")))
    ...

def post_testcase(name, self, env, result, kwargs):
    ...

Default Values¶

You can provide partial parametrization context assuming that the decorated method has default values assigned to the parametrized arguments:

@testsuite
class SampleTest(object):

    @testcase(parameters=(
        (5,),  # b=5, expected=10
        (3, 7)  # expected=10
        {'a': 10, 'expected': 15},  # b=5
    ))
    def addition(self, env, result, a, b=5, expected=10):
        result.equal(expected, a + b)

Testcase name generation¶

When you use parametrization, Testplan will try creating a sensible name for each generated testcase. By default the naming convention is: 'ORIGINAL_TESTCASE_NAME <arg1=value1, arg2=value2, ... argN=valueN>'

In the example below, 2 new testcases will be generated, and their names become 'Add List <number_list=[1, 2, 3], expected=6>' and 'Add List <number_list=[6, 7, 8, 9], expected=30>'.

@testsuite
class SampleTest(object):

    @testcase(
        name="Add List",
        parameters=(
            ([1, 2, 3], 6),
            (range(6, 10), 30),
        )
    )
    def add_list(self, env, result, number_list, expected):
        result.equal(expected, sum(number_list))

User can provide custom name generation functions to override this default behavior via @testcase(name_func=...) syntax. You need to implement a function that accepts func_name and kwargs as arguments, func_name being a string and kwargs being an OrderedDict. See default_name_func for sample implementation.

def custom_name_func(func_name, kwargs):
    return '{func_name} -- (numbers: [{joined_list}], result: {expected})'.format(
        func_name=func_name,
        joined_list=' '.join(map(str, kwargs['number_list'])),
        expected=kwargs['expected']
    )

@testsuite
class SampleTest(object):

    @testcase(
        name="Add List",
        parameters=(
            ([1, 2, 3], 6),
            (range(6, 10), 30),
        ),
        name_func=custom_name_func
    )
    def add_list(self, env, result, number_list, expected):
        ...

In the above example, the custom testcase names should be '"Add List -- (numbers: [1 2 3], result: 6)"' and '"Add List -- (numbers: [6 7 8 9], result: 30)"'. If you deliberately set name_func to None, then the display names generated are simply 'Add List 0' and 'Add List 1', that is, integer suffixes appended to the original testcase names, without any argument showed.

Testcase docstring generation¶

Similar to testcase name generation, you can also build custom docstrings for generated testcases via @testcase(parameters=..., docstring_func=custom_docstring_func) syntax.

Testplan will then use these docstrings as test descriptions while generating the test reports.

The custom_docstring_func function should accept docstring and kwargs arguments, docstring being a string or None and kwargs being an OrderedDict.

import os

def custom_docstring_func(docstring, kwargs):
  """
  Returns original docstring (if available) and
  parametrization arguments in the format ``key: value``.
  """
  kwargs_items = [
      '{}: {}'.format(arg_name, arg_value)
      for arg_name, arg_value in kwargs.items()
  ]

  kwargs_string = os.linesep.join(kwargs_items)

  if docstring:
      return '{}{}{}'.format(docstring, os.linesep, kwargs_string)
  return kwargs_string

@testsuite
class SampleTest(object):

    @testcase(
        parameters=(
            ([1, 2, 3], 6),
            (range(6, 10), 30),
        ),
        docstring_func=custom_docstring_func
    )
    def add_list(self, env, result, number_list, expected):
        ...

Tagging Generated Test Cases¶

You can tag generated testcases, all you need to do is to pass tags argument along with parameters:

@testsuite
class SampleTest(object):

    @testcase(
        tags=('tagA', 'tagB'),
        parameters=(
            (1, 2),
            (3, 4),
        )
    )
    def addition(self, env, result, a, b):
        ...

It is also possible to use parametrization values to assign tags dynamically, via tag_func argument. The tag_func should accept a single argument (kwargs) which will be the parametrized keyword argument dictionary for that particular generated testcase.

def custom_tag_func(kwargs):
    """
    Returns a dictionary that is interpreted as named tag context.
    A string or list of strings will be interpreted as simple tags.
    """
    region_map = {
      'EU': 'Europe',
      'AS': 'Asia',
      'US': 'United States'
    }

    return {
      'product': kwargs['product'].title(),
      'region': region_map.get(kwargs['region'], 'Other')
    }

@testsuite
class SampleTest(object):

    @testcase(
        parameters=(
            ('productA', 'US'),  # tags: product=ProductA, region=United States
            ('productA', 'EU'),  # tags: product=ProductA, region=Europe
            ('productB', 'EMEA'),  # tags: product=ProductB, region=Other
            ('productC', 'BR')  # tags: product=ProductC, region=Other
        ),
        tag_func=custom_tag_func
    )
    def product(self, env, result, product, region):
        ...

Note

If you use tag_func along with tags argument, testplan will merge the dynamically generated tag context with the explicitly passed tag values.

Decorating Parametrized Testcases¶

Decorating parametrized testcases uses a different syntax than usual python decorator convention: You need to pass your decorators via custom_wrappers argument instead of decorating the testcase via @decorator syntax. If you implement custom decorators, please make sure you use testplan.common.utils.callable.wraps(), instead of @functools.wraps.

from testplan.common.utils.callable import wraps

def my_custom_decorator(func):
    @wraps(func)
    def wrapper(*args, **kwargs):
        ...

@testsuite
class SampleTest(object):

    # Decorating a normal testcase, can use `@decorator` syntax.
    @my_custom_decorator
    @testcase
    def normal_test(self, env, result):
        ...

    # For parametrized testcases, need to use `custom_wrappers` argument.
    @testcase(
        parameters=(
            (1, 2),
            (3, 4),
        ),
        custom_wrappers=my_custom_decorator  # can pass a single decorator
                                             # instead of a list with
                                             # single element
    )
    def addition(self, env, result, a, b):
        ...

Testcase Parallel Execution¶

It is possible to run testcases in parallel with a thread pool. This feature can be used to accelerate a group of testcases that spend a lot of time on IO or waiting. Due to Python global interpreter lock, the feature is not going to help CPU-bounded tasks, it also requires testcase written in a thread-safe way.

To enable this feature, instantiate MultiTest with a non-zero thread_pool_size and define execution_group for testcases you would like to run in parallel. Testcases in the same group will be executed concurrently.

@testsuite
class SampleTest(object):

    @testcase(execution_group='first')
    def test_g1_1(kwargs):
        ...

    @testcase(execution_group='second')
    def test_g2_1(kwargs):
        ...

    @testcase(execution_group='first')
    def test_g1_2(kwargs):
        ...

    @testcase(execution_group='second')
    def test_g2_2(kwargs):
        ...

    my_multitest = MultiTest((name='Testcase Parallezation',
                              suites=[SampleTest()],
                              thread_pool_size=2))

Testcase timeout¶

If testcases are susceptible to hanging, or not expected to be time consuming, you may want to spot this and abort those testcases early. You can achieve it by passing a “timeout” parameter to the testcase decorator, like:

@testcase(timeout=10*60)  # 10 minutes timeout, given in seconds.
def test_hanging(self, env, result):
    ...

If the testcase times out it will raise a TimeoutException, causing its status to be “ERROR”. The timeout will be noted on the report in the same way as any other unhandled Exception. The timeout parameter can be combined with other testcase parameters (e.g. used with parametrized testcases) in the way you would expect - each individual parametrized testcase will be subject to a seperate timeout.

Also keep in mind that testplan will take a little bit of effort to monitor execution time of testcases with timeout attribute, so it is better to allocate a little more seconds than you have estimated how long a testcase would need.

Similarly, setup and teardown methods in a test suite can be limited to run in specified time period, like this:

from testplan.testing.multitest.suite import timeout

@timeout(120)  # 2 minutes timeout, given in seconds.
def setup(self, env, result):
    ...

@timeout(60)  # 1 minute timeout, given in seconds.
def teardown(self, env):
    ...

It’s useful when setup has much initialization work that takes long, e.g. connects to a server but has no response and makes program hanging. Note that this @timeout decorator can also be used for pre_testcase and post_testcase, but that is not suggested because pre/post testcase methods are called everytime before/after each testcase runs, they should be written as simple as possible.

Hooks¶

In addition suites can have setup and teardown methods. The setup method will be executed on suite entry, prior to any testcase if present. The teardown method will be executed on suite exit, after setup and all @testcase-decorated testcases have executed.

Again, the signature of those methods is checked at import time, and must be as follows:
def setup(self, env):
    ...

def teardown(self, env):
    ...
The result object can be optionally used to perform logging and basic assertions:
def setup(self, env, result):
    ...

def teardown(self, env, result):
    ...
To signal that either setup or teardown hasn’t completed correctly, you must raise an exception. Raising an exception in setup will abort the execution of the testsuite, raising one in teardown will be logged in the report but will not prevent the execution of the next testsuite.

Similarly suites can have pre_testcase and post_testcase methods. The pre_testcase method is executed before each testcase runs, and the post_testcase method is executed after each testcase finishes. Exceptions raised in these methods will be logged in the report. Note that argument name is populated with name of testcase.
def pre_testcase(self, name, env, result):
    pass

def post_testcase(self, name, env, result):
    pass

Xfail¶

Testcases and testsuites that you expect to fail can be marked with the @xfail decorator. These failures will be visible in the test report, highlighted in orange. Expected failures will not cause the testplan as a whole to be considered a failure.

The Xfail means that you expect a test to fail for some reason. If a testcase/testsuite is unstable (passing sometimes, failling other times) then strict=False (default value is False) can be used. This means if the testcase/testsuite fails it will be marked “expected to fail” (xfail), if it passes it will be marked as “unexpectedly passing” (xpass). Both xfail and xpass don’t cause the parent testsuite or MultiTest to be marked as a failure.

The xfail decorator mandates a reason that explains why the test is marked as Xfail:

@xfail(reason='unstable test')
def unstable_testcase(self, env, result):
    ...

If a test is expect to fail all the time, you can also use the strict=True then xpass will be considered as fail. This will cause the unexpectedly passing result to fail the testcase or testsuite.

@xfail(reason='api changes', strict=True)
def fail_testcase(self, env, result):
    ...

Skip if¶

@skip_if decorator can be used to annotate a testcase. It take one or more predicates, and if any of them evaluated to True, then the testcase will be skipped by MultiTest instead of being normally executed. The predicate’s signature must name the argument testsuite or a MethodSignatureMismatch exception will be raised.
def skip_func(testsuite):
    # It must accept an argument named "testsuite"
    return True

@testsuite
class MySuite(object):

    @skip_if(skip_func, lambda testsuite: False)
    @suite.testcase
    def case(self, env, result):
        pass

Logging¶

Python standard logging infrastructure can be used for logging, however testplan provide mixins to use a conveniently configured logger from testcases.