test_ok1/doc/test/features.txt

==================================================
py.test feature overview 
==================================================

.. contents:: 
    :local:
    :depth: 1

mature command line testing tool 
====================================================

py.test is a command line tool to collect, run and report about automated tests.  It runs well on Linux, Windows and OSX and on Python 2.4 through to 3.1 versions.
It is used in many projects, ranging from running 10 thousands of tests 
to a few inlined tests on a command line script.  As of version 1.2 you can also
generate a no-dependency py.test-equivalent standalone script that you
can distribute along with your application.  

extensive easy plugin system 
======================================================

.. _`suprisingly easy`: http://bruynooghe.blogspot.com/2009/12/skipping-slow-test-by-default-in-pytest.html

py.test delegates almost all aspects of its operation to plugins_.
It is `suprisingly easy`_ to add command line options or
do other kind of add-ons and customizations.  This can 
be done per-project or by distributing a global plugin. 
One can can thus modify or add aspects for purposes such as: 

* reporting extensions
* customizing collection and execution of tests 
* running and managing non-python tests
* managing domain-specific test state setup 
* adding non-python tests into the run, e.g. driven by data files

.. _`plugins`: plugin/index.html

distributing tests to your CPUs and SSH accounts
==========================================================

.. _`pytest-xdist`: plugin/xdist.html

Through the use of the separately released `pytest-xdist`_ plugin you
can seemlessly distribute runs to multiple CPUs or remote computers
through SSH and sockets.  This plugin also offers a ``--looponfailing``
mode which will continously re-run only failing tests in a subprocess.  

supports several testing practises and methods 
==================================================================

py.test supports many testing methods conventionally used in 
the Python community. It runs traditional `unittest.py`_, 
`doctest.py`_, supports `xUnit style setup`_ and nose_ specific
setups and test suites.  It offers minimal no-boilerplate model 
for configuring and deploying tests written as simple Python 
functions or methods.  It also integrates `coverage testing
with figleaf`_ or `Javasript unit- and functional testing`_. 

.. _`Javasript unit- and functional testing`: plugin/oejskit.html
.. _`coverage testing with figleaf`: plugin/figleaf.html

integrates well with CI systems
====================================================

py.test can produce JUnitXML style output as well as formatted
"resultlog" files that can be postprocessed by Continous Integration
systems such as Hudson or Buildbot easily.  It also provides command 
line options to control test configuration lookup behaviour or ignoring 
certain tests or directories. 

no-boilerplate test functions with Python 
===================================================

.. _`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 advanced 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 use
one of two forms::

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

both of which execute the specified function with args and kwargs and
asserts that the given ``Exception`` 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. 

advanced skipping of tests 
======================================

py.test has `advanced support for skipping tests`_ or expecting
failures on tests on certain platforms.  Apart from the 
minimal py.test style also unittest- and nose-style tests 
can make use of this feature.  

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

advanced test selection and running modes 
=========================================================

.. _`selection by keyword`: 

``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. 

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::

    @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


.. _`reStructured Text`: http://docutils.sourceforge.net
.. _`Python debugger`: http://docs.python.org/lib/module-pdb.html


.. _nose: http://somethingaboutorange.com/mrl/projects/nose/