test_ok1/doc/example/x.txt

Looping on the failing test set
-----------------------------------------

``py.test --looponfailing`` (implemented through the external
`pytest-xdist`_ plugin) allows to run a test suite,
memorize all failures and then loop over the failing set
of tests until they all pass.  It will re-start running
the tests when it detects file changes in your project.

select tests by keyword / test name search
-----------------------------------------------------

.. _`selection by keyword`:

You can selectively run tests by specifiying a keyword
on the command line.  Examples::

    py.test -k test_simple
    py.test -k "-test_simple"

will run all tests matching (or not matching) the
"test_simple" keyword.  Note that you need to quote
the keyword if "-" is recognized as an indicator
for a commandline option.  Lastly, you may use::

    py.test. -k "test_simple:"

which will run all tests after the expression has *matched once*, i.e.
all tests that are seen after a test that matches the "test_simple"
keyword.

By default, all filename parts and
class/function names of a test function are put into the set
of keywords for a given test.  You can specify additional
kewords like this:

.. sourcecode:: python

    @py.test.mark.webtest
    def test_send_http():
        ...

and then use those keywords to select tests.  See the `pytest_keyword`_
plugin for more information.

.. _`pytest_keyword`: plugin/mark.html
skip or expect-to-fail a test
-------------------------------------------

py.test has a dedicated `skipping plugin`_ that allows to define

* define "skip" outcomes indicating a platform or a
  dependency mismatch.

* "xfail" outcomes indicating an "expected failure" either with
  with or without running a test.

* skip and xfail outcomes can be applied at module, class or method
  level or even only for certain argument sets of a parametrized function.

.. _`skipping plugin`: plugin/skipping.html
.. _`funcargs mechanism`: funcargs.html
.. _`doctest.py`: http://docs.python.org/library/doctest.html
.. _`xUnit style setup`: xunit_setup.html
.. _`pytest_nose`: plugin/nose.html


no-boilerplate testing
----------------------------------

.. _`autocollect`:

automatic Python test discovery
+++++++++++++++++++++++++++++++++++

By default, all python modules with a ``test_*.py``
filename are inspected for finding tests:

* functions with a name beginning with ``test_``
* classes with a leading ``Test`` name and ``test`` prefixed methods.
* ``unittest.TestCase`` subclasses

parametrizing test functions and functional testing
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

py.test offers the unique `funcargs mechanism`_ for setting up
and passing project-specific objects to Python test functions.
Test Parametrization happens by triggering a call to the same test
function with different argument values.  For doing fixtures
using the funcarg mechanism makes your test and setup code
more efficient and more readable. This is especially true
for functional tests which might depend on command line
options and a setup that needs to be shared across
a whole test run.

per-test capturing of output, including subprocesses
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

By default, ``py.test`` captures all writes to stdout/stderr.
Output from ``print`` statements as well as from subprocesses
is captured_.  When a test fails, the associated captured outputs are shown.
This allows you to put debugging print statements in your code without
being overwhelmed by all the output that might be generated by tests
that do not fail.

.. _captured: plugin/capture.html

assert with the ``assert`` statement
+++++++++++++++++++++++++++++++++++++++++++++

``py.test`` allows to use the standard python
``assert statement`` for verifying expectations
and values in Python tests.  For example, you can
write the following in your tests::

     assert hasattr(x, 'attribute')

to state that your object has a certain ``attribute``. In case this
assertion fails you will see the value of ``x``.  Intermediate
values are computed by executing the assert expression a second time.
If you execute code with side effects, e.g. read from a file like this::

        assert f.read() != '...'

then you may get a warning from pytest if that assertions
first failed and then succeeded.

asserting expected exceptions
+++++++++++++++++++++++++++++++++++++++

In order to write assertions about exceptions, you can use
``py.test.raises`` as a context manager like this:

.. sourcecode:: python

    with py.test.raises(ZeroDivisionError):
        1 / 0

and if you need to have access to the actual exception info you may use:

.. sourcecode:: python

    with py.test.raises(RuntimeError) as excinfo:
        def f():
            f()
        f()

    # do checks related to excinfo.type, excinfo.value, excinfo.traceback

If you want to write test code that works on Python2.4 as well,
you may also use two other ways to test for an expected exception:

.. sourcecode:: python

    py.test.raises(ExpectedException, func, *args, **kwargs)
    py.test.raises(ExpectedException, "func(*args, **kwargs)")

both of which execute the specified function with args and kwargs and
asserts that the given ``ExpectedException`` is raised.  The reporter will
provide you with helpful output in case of failures such as *no
exception* or *wrong exception*.

information-rich tracebacks, PDB introspection
+++++++++++++++++++++++++++++++++++++++++++++++++++++++

.. _`example tracebacks`: http://paste.pocoo.org/show/134814/

A lot of care is taken to present useful failure information
and in particular nice and concise Python tracebacks.  This
is especially useful if you need to regularly look at failures
from nightly runs, i.e. are detached from the actual test
running session.  Here are `example tracebacks`_ for a number of failing
test functions.  You can modify traceback printing styles through the
command line.  Using the `--pdb`` option you can automatically activate
a PDB `Python debugger`_ when a test fails.