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TurboGears 2.1 Standard Installation

Testing TurboGears Applications

Why is writing tests so essential?

If you’re already convinced about the merits of test-driven development, you can skip this section. But maybe you’re still wondering whether it’s worthwile spending time writing tests instead of actually coding your application. We usually tend to not even have time enough for writing the code and making it work, and we’re testing the functionality anyway while we are coding, right? Will it not unnecessarily slow down your whole development process?

The reality is, as you will see very soon when you start writing automated tests, that you will not only be writing better software, but developing the software will even become less expensive and faster. How so? Well, you may have experienced it already: While it is quite simple to fix bugs during the design and implementation phase, once your software will be released and in production, the time needed for fixing bugs will drastically increase. This is because with every change you make to fix one bug, you may break other code and introduce new bugs. You will have to check the functionality manually over and over again. However, if you have automated tests, you will notice such problems immediately when running your test suite.

Besides these obvious benefits of automated tests, they inevitably generate other positive side effects. For example, if you want to automate your testing, you have to write code in a way that is testable. Such code automatically tends to have a much better quality, because it is usually less complex and better structured, therefore more robust and easier to understand and maintain. Tests often reveal problems with edge cases that you wouldn’t have thought about before writing or executing the tests. As another side benefit, the test suite can partially replace written documentation with use case examples and detailed specification of how the code should actually work.

If you have a carefully written test suite in place, it will also encourage you to refactor your code to make it less complex or more performant. Without such a test suite, you would be reluctant to make such changes because of the old saying “never touch a running system.” But the automated tests will assure that you won’t break anything during the refactoring, and in the end, the code will become even better.

If you do not write automated tests, you will inevitably spend much more time debugging and manually testing your system. And you will never be sure that your last change didn’t break anything. Your development will be driven by fear of failure and stagnate, while with automated tests, you can always be confident about your code and you are encouraged to improve it even more.

Since the benefits of autmated tests are so overwhelming, they are not considered an annoying duty you carried out after writing your code, but some developers even start writing code by writing the corresponding test (“test first development” or “test driven development”). This approach sounds illogical at first, but it has several advantages, besides making sure that every function in the code is accompanied by an automated test. In the end, you will find out that test driven programming makes writing tests fun and you will start wondering how you ever had written programs without writing tests.

Unit testing with “Nose”

The foundation of all automated testing is the so-called “unit testing”. As the name says, a “unit test” will only test one unit of your code at a time, i.e. the smallest testable parts of your application. In our case, this is usually a Python method or function. One of the basic principles of unit testing is that each test should be independent from the others.

The Python standard library provides the unittest framework that helps you to write unit tests based on these principles. Alternatively, you can also write tests using the doctest module in the Python standard library. This allows you to embed your tests in the docstrings of your code, nicely utilizing the mentioned overlap between writing tests and documentation.

A popular Python tool that extends the basic unittest framework is nose. It makes writing unit tests even easier by providing more and simpler ways of collecting the tests, running the tests and setting up the so-called “test fixtures”. The nose framework also provides a plugin mechanism for adding in further test-related tools such as the “coverage” module allowing you to measure exact code coverage of your application.

Nose is used as the base for testing TurboGears applications as well as TurboGears itself, and therefore will be automatically installed together with TurboGears. You can run the tests with the nosetests command. Let’s try this with a quickstarted TurboGears application:

$ paster quickstart --noinput myapp
$ cd myapp
$ python setup.py develop
$ paster setup-app development.ini
$ nosetests

....................
----------------------------------------------------------------------
Ran 20 tests in 3.000s

OK

As you see, the quickstarted application already comes with 20 tests which you can use as the starting point for building the complete test suite for your application, and which should all pass unless you changed anything to your application. Nose is able to find these tests due to certain naming conventions. So you don’t need to manually specify where your tests are when writing and executing the tests. This is one of the many features that makes testing with nose so comfortable. By the way, nose will also be used when running the test command of setuptools, i.e. you can also run the tests with python setup.py test or python setup.py nosetests. However, the nosetests command is simpler to type and you can pass it a lot of useful command-line options. For instance, the option -v (for “verbose”) will display more information about the individual tests nose has collected:

$ nosetests -v

Anonymous users are forced to login ... ... ok
Logouts must work correctly ... ok
Voluntary logins must work correctly ... ok
The data display demo works with HTML ... ok
The data display demo works with JSON ... ok
Displaying the wsgi environ works ... ok
The front page is working properly ... ok
Anonymous users must not access the secure controller ... ok
The editor cannot access the secure controller ... ok
The manager can access the secure controller ... ok
Model objects can be created ... ok
Model objects can be queried ... ok
that Model objects can be created ... ok
Model objects can be queried ... ok
Model objects can be created ... ok
Users should be fetcheable by their email addresses ... ok
User objects should have no permission by default ... ok
The obj constructor must set the email right ... ok
The obj constructor must set the user name right ... ok
Model objects can be queried ... ok

----------------------------------------------------------------------
Ran 20 tests in 3.000s

OK

You will find all of these tests in the package tests inside your TurboGears application. This package has been divided into two subpackages, functional and models. The models package contains unit tests for your model classes. The functional package contains tests for the controllers of your application. Since they test the whole application stack, they are actually not unit tests, but so-called “functional tests” or “integration tests”. Let’s have a look at the example tests in these packages in more detail.

Testing your model classes

The model package inside your test package comes with a simple base class for testing your SQLAlchemy model classes, called ModelTest. Unit tests allow setting up a so-called “test fixture” with a setUp() method, often accompanied by a tearDown() method for cleaning up the fixture after use. The ModelTest class uses this mechanism for creating a model object and writing it to the database. The example test class for the User model class, using ModelTest as its base class, looks like this:

class TestUser(ModelTest):
    """Unit test case for the ``User`` model."""

    klass = model.User
    attrs = dict(
        user_name = u"ignucius",
        email_address = u"ignucius@example.org"
        )

    def test_obj_creation_username(self):
        """The obj constructor must set the user name right"""
        eq_(self.obj.user_name, u"ignucius")

    def test_obj_creation_email(self):
        """The obj constructor must set the email right"""
        eq_(self.obj.email_address, u"ignucius@example.org")

    def test_no_permissions_by_default(self):
        """User objects should have no permission by default."""
        eq_(len(self.obj.permissions), 0)

    def test_getting_by_email(self):
        """Users should be fetcheable by their email addresses"""
        him = model.User.by_email_address(u"ignucius@example.org")
        eq_(him, self.obj)

You will find this code in the test_auth module, because the User class is defined in the model.auth module of your application. For clarity, the names of the test files should correspond to the name of the files they are testing. As you see, you need to specify the name of the model class with the klass attribute, and you can also specify the attributes for initializing the model object in attrs. You don’t need to add tests for creating and querying users from the database, as these tests are already inherited from the base class MOdelTest. The object that is created by the setUp() method is stored in the obj member. The eq_ function used in the four test method has been imported from nose.tools and is just a shorthand for the assert statement that is actually at the core of every unit test. So the first test method is equivalent to:

def test_obj_creation_username(self):
    """The obj constructor must set the user name right"""
    assert self.obj.user_name == u"ignucius"

The nose.tools package contains some more of such convenience functions and decorators. A more useful one is the raises decorator for checking whether your test method raises a certain (expected) exception.

Let’s assume we want to add a property top_level_domain to our User class that returns the top level domain of the user’s email address. As already mentioned, it is a good idea to write the unit test before writing the actual code. So we add the following method to our TestUser class:

def test_top_level_domain_property(self):
    """The top level domain must be returned as a property"""
    eq_(self.obj.top_level_domain, 'org')

You see how simple it is to add a uni test, and that this test also documents that we do not want the returned value to start with a dot. Let’s run our test suite. If you don’t want to run the full test suite, you can specify the tests to run as arguments on the command line, like this:

$ nosetests myapp.tests.models.test_auth

..........E
======================================================================
ERROR: The top level domain must be returned as a property
----------------------------------------------------------------------
Traceback (most recent call last):
    ...
    eq_(self.obj.top_level_domain, 'org')
AttributeError: 'User' object has no attribute 'top_level_domain'

----------------------------------------------------------------------
Ran 11 tests in 0.063s

FAILED (errors=1)

As expected, the test failed, because we haven’t added any code to the User class yet. However, it is important to verify that the test is actually picked up by nose and that it really fails if the tested functionality is not implemented. Let’s now add our top_level_domain property to the User class which can be found in the file myapp/model/auth.py:

@property
def top_level_domain(self):
    """Return the top level domain of the user's email address."""
    return self.email_address.rsplit('.', 1)[-1]

We re-run our test suite to check that this code is working properly:

$ nosetests

.....................
----------------------------------------------------------------------
Ran 21 tests in 3.125s

OK

Et voilà, we know that our new property is working. You soon will start to love these little dots indicating that your tests are passing...

Testing your controllers

As already mentioned, you will find the tests for the example controller methods of your quickstarted application in the tests.functional package. There are actually two test modules, test_authentication for testing the user login provided by the authentication sub-system, and test_root for testing the actual functionality of the root controller.

Again, the quickstarted test package provides a base class TestController for all of these tests. In its setUp() method, it creates an instance of your application which is then stored in the app attribute and run. By default, this application instance has authentication disabled. The idea behind this is that you test authentication separately from the actual functionality of the controller, and independently of which kind of authentication you have configured. Here is the test for the front page provided by the root controller of your quickstarted application:

class TestRootController(TestController):
    """Tests for the method in the root controller."""

    def test_index(self):
        """The front page is working properly"""
        response = self.app.get('/')
        msg = 'TurboGears 2 is rapid web application development toolkit '\
              'designed to make your life easier.'
        # You can look for specific strings:
        assert_true(msg in response)

In self.app you actually get a wrapper around the WSGI application, which is provided by the WebTest utility. This wrapper provides a convenient interface for testing WSGI applications like those created with TurboGears. What is so nice about this approach is that you don’t need to run a web server for the functional tests, which makes testing much speedier. WebTest simply simulates the full request-response cycle for you.

As you see in the first line of the test_index() method, you can send a request to your imaginary webserver using the get() method. As the method name indicates, this is a GET request. For a POST request, you would use the post() method. You can also add headers as argument ot the get() or post() method. As return value, you will get a response object. This response object has the usual attributes such as status , headers, body, and request plus some additional functionality for testing. For instance, as you see in the example above, msg in response allows you to check that the string msg is found in the response body. The assert_true function is imported from nose.tools again and simply checks that the given expression is true.

Some of the example methods of the root controller require authorization. There is also the secc subcontroller which is set up so that only users with “manage” permission, such as the “manager”, can access it in a quickstarted application. The following test verifies this by trying to access the secc controller as user “editor”:

def test_secc_with_editor(self):
    """The editor cannot access the secure controller"""
    environ = {'REMOTE_USER': 'editor'}
    self.app.get('/secc', extra_environ=environ, status=403)

As you can see here, you can pass extra environment variables and an expected HTTP status code (in this case 403, i.e. “forbidden”) to the get() method of the test application. We still need to check that we can access the secc controller when we log in as “manager”:

def test_secc_with_manager(self):
    """The manager can access the secure controller"""
    environ = {'REMOTE_USER': 'manager'}
    resp = self.app.get('/secc', extra_environ=environ, status=200)
    assert 'Secure Controller here' in resp.body, resp.body

In this case, the response should have the HTTP status code 200 (i.e. “ok”). The text “Secure Controller here” is displayed to the user by the index method method of the secure controller using a flash message. You don’t need to worry that the flash mechanism is using a cookie in the background; the testing framework handles all of this transparently for you.

In the case that the assert statement fails, it prints the response body as an error message. This helps you to fix spelling errors in your expected text. You can also print the values of all the objects in failed assert statements by running nosetests with the -d option. Another way of inspecting the values of objects involved in your test is simply adding print statements to your test methods. Note that nose very conveniently will only display the output of failing tests. Even interactive debugging of your tests is possible with the --pdb and --pdb-failures options of nose.

You can set up the configuration used for your test suite in the test.ini configuration file. Note that by default, an in-memory database will be used, but most of the other settings will be the same as in your development environment, because by default the test.ini file has the following entry:

[app:main]
sqlalchemy.url = sqlite:///:memory:
use = config:development.ini

Measuring code coverage

Your goal should be to have a test suite covering 100% of your application code. How can you make sure this is the case, and there are no untested areas? Luckily, with coverage.py you have a useful tool for measuring code coverage of any Python program. You need to install it first, which is as simple as:

$ easy_install coverage

You can instruct nose to run the coverage tool on your test suite and print a coverage report, using the following options:

$ nosetests --with-coverage --cover-package=myapp

Name                       Stmts   Exec  Cover   Missing
-----------------------------------------------------------
myapp                          1      1   100%
myapp.config                   1      1   100%
myapp.config.app_cfg          22     22   100%
myapp.config.environment       4      4   100%
myapp.config.middleware        8      8   100%
myapp.controllers              1      1   100%
myapp.controllers.error        9      9   100%
myapp.controllers.root        51     44    86%   47, 63, 69, ...
myapp.controllers.secure      13     12    92%   31
myapp.lib                      1      1   100%
myapp.lib.app_globals          5      5   100%
myapp.lib.base                13     13   100%
myapp.lib.helpers              2      2   100%
myapp.model                   11     11   100%
myapp.model.auth              79     69    87%   17-18, 83, 86, ...
myapp.templates                1      1   100%
myapp.websetup                11     11   100%
myapp.websetup.bootstrap      38     32    84%   49-54
myapp.websetup.schema          9      9   100%
-----------------------------------------------------------
TOTAL                        280    256    91%
-----------------------------------------------------------
Ran 21 tests in 5.359s

This is already quite a good coverage. Let’s try to improve the coverage of the root controller. The report shows that line 47 of the controllers.root module is missing, and if you open the file with an editor, you will find that this is the controller method for the “about” page. You can add the following test method to the TestRootController class in the tests.functional.test_root module to fix this:

def test_about(self):
    """The about page can be displayed"""
    response = self.app.get('/about.html')
    assert_true('<h2>Architectural basics'
        ' of a quickstart TG2 site.</h2>' in response)

The report also shows that lines 83 and 86 of the model.auth module are not covered, and you will find that these are __repr__() and unicode() methods of the Group class. You can fix this by adding two test methods to the TestGroup class in the tests.models.test_auth module:

def test_obj_repr(self):
    """The obj has a proper string representation"""
    eq_(repr(self.obj), "<Group: name=test_group>")

def test_obj_unicode(self):
    """The obj can be converted to a unicode string"""
    eq_(unicode(self.obj), u"test_group")

If you now print a coverage report again, you will notice that the coverage has increased from 91% to 92%.

Want to learn more?

If you want to learn more about testing TurboGears applications, we recommend studying the following online ressources: