Charming Python #b16: Testing Frameworks In Python.

Making sure your software does what you think it does

David Mertz, Ph.D.
Random Traveller, Gnosis Software, Inc.
March, 2004

Python comes with two standard modules for unit testings: unittest and doctest. These modules expand on the capability of the built-in assert statement, which is used for validation of pre-conditions and post-conditions within functions. This installment discusses the best ways to incorporate testing into Python development, weighing the advantages of different styles for different types of projects.

About Unit Testing

I have a confession. Even as the author of a fairly widely used public domain Python library, I have been far less than systematic in including unit tests for my modules. In fact, the bulk of those tests that are included in Gnosis Utilities fall under gnosis.xml.pickle, and were written by a contributor to that subpackage. I have found that the large majority of third-party Python packages I donwnload also lack a thorough unit test collection.

Moreover, the tests that exist in Gnosis Utilities suffer from another flaw: you often need to understand the expected output in a fair amount of detail to even know whether a test succeeds or fails. What passes as tests are actually--in many cases--more like small utilities that utilize parts of the library. These tests/utilities allow input from arbitrary data sources (of the right type) and/or output in descriptive data formats. These test utilities are actually rather useful when you need to debug some subtle error. But as self-explanatory sanity checks of changes between library versions, these kinds of tests do not succeed.

In this installment, I try to improve the tests in my utility collection using the Python standard library modules doctest and unittest, and walk readers through my experience (with a few pointers on best approaches).

The script gnosis/xml/objectify/test/ provides a typical example of both currrent testing weaknesses and their solution. Let us look at a recent version of the script:


"Read and print and objectified XML file"
import sys
from gnosis.xml.objectify import XML_Objectify, pyobj_printer
if len(sys.argv) > 1:
    for filename in sys.argv[1:]:
        for parser in ('DOM','EXPAT'):
                xml_obj = XML_Objectify(filename, parser=parser)
                py_obj = xml_obj.make_instance()
                print pyobj_printer(py_obj).encode('UTF-8')
                sys.stderr.write("++ SUCCESS (using "+parser+")\n")
                print "="*50
                sys.stderr.write("++ FAILED (using "+parser+")\n")
                print "="*50
    print "Please specify one or more XML files to Objectify."

The utility function pyobj_printer() produces a non-XML representation of arbitrary Python object--specifically one that does not use any other facilities of gnosis.xml.objectify (nor anything else from Gnosis Utilities). I will probably move the function elsewhere within the Gnosis package for later releases. In any case, pyobj_printer() uses various Python-like indentation and symbols to describe objects and their attributes (similar to pprint, but expanding instances rather than built-in datatypes).

The script provides a good debugging tool if some particular XML does not seem to get "objectified" right--you can inspect visually exactly what attributes and values the resultant object has. Moreover, if you redirect STDOUT, you can look at the simple messages on STDERR, e.g.

Examining STDERR result messages

$ python testns.xml > /dev/null
++ SUCCESS (using DOM)
++ FAILED (using EXPAT)

However, success or failure in the above example run are defined very weakly: success just means no exception was raised, not that the (redirected) output was correct.

Using Doctest

The module doctest lets you embed comments within docstrings that show the desired behavior of various statements, especially function and method results. To do this is mostly just a matter of making the docstring look like an interactive shell session; and easy way to do that is to copy-and-paste from a Python interactive shell (or from Idle, PythonWin, MacPython, or other IDEs with interactive sessions). Let us look at an improved script to illustrate: script with self-diagnostics

import sys
from gnosis.xml.objectify import XML_Objectify, pyobj_printer, EXPAT, DOM
LF = "\n"

def show(xml_src, parser):
    """Self test using simple or user-specified XML data

    >>> xml = '''<?xml version="1.0"?>
    ...  <!DOCTYPE Spam SYSTEM "spam.dtd" >
    ...  <Spam>
    ...    <Eggs>Some text about eggs.</Eggs>
    ...    <MoreSpam>Ode to Spam</MoreSpam>
    ...  </Spam>'''
    >>> squeeze = lambda s: s.replace(LF*2,LF).strip()
    >>> print squeeze(show(xml,DOM)[0])
    -----* _XO_Spam *-----
       PCDATA=Some text about eggs.
       PCDATA=Ode to Spam
    >>> print squeeze(show(xml,EXPAT)[0])
    -----* _XO_Spam *-----
       PCDATA=Some text about eggs.
       PCDATA=Ode to Spam
        xml_obj = XML_Objectify(xml_src, parser=parser)
        py_obj = xml_obj.make_instance()
        return (pyobj_printer(py_obj).encode('UTF-8'),
                "++ SUCCESS (using "+parser+")\n")
        return ("","++ FAILED (using "+parser+")\n")

if __name__ == "__main__":
    if len(sys.argv)==1 or sys.argv[1]=="-v":
        import doctest, test_basic
    elif sys.argv[1] in ('-h','-help','--help'):
        print "You may specify XML files to objectify instead of self-test"
        print "(Use '-v' for verbose output, otherwise no message means success)"
        for filename in sys.argv[1:]:
            for parser in (DOM, EXPAT):
                output, message = show(filename, parser)
                print output
                print "="*50

There are several things worth noting about the improved (and expanded) test script. I arranged the _main_ block so that if you specify XML files on the command line, the script continues to implement the prior behavior. This lets you continue to check XML other than the test case, and eyeball the result--either to find errors in what gnosis.xml.objectify does, or just to understand its purpose. In standard fashion, -h or --help get you an explanation of usage.

The interesting new functionality comes when is run with no arguments (or with the -v argument that simply gets obeyed by doctest in its turn). In this case we run doctest on the module/script itself--you can see from the fact that we import test_basic into the scripts own namespace that we could just as easily import some other module that we wanted to test. The function doctest.testmod() looks through all the docstrings in the module itself, its functions, and its classes, to find anything that resembles an interactive shell session; in this case, such a session is found in the show() function.

The docstring to show() illustrates a couple minor gotchas in designing good doctest sessions. Unfortunately, the way doctest parses apparent sessions treats blank lines as ending sessions--so output like the return value of pyobj_printer() needs to be munged slightly to be tested. The easiest approach is a function like squeeze(), defined in the docstring itself (it just removes adjacent newlines). Also, since a docstring is--after all--a string escape sequences like \n are expanded, which makes it slightly confusing to escape a newline within the code sample. You could use \\n, but I found defining LF to clarify things.

The self-test defined in the docstring to show() does a bit more than merely make sure no exception is raised (as with the initial test script). At least one simple XML document is checked for correct "objectification." Of course, it is still possible that some other XML document would not be processed correctly--for example, the namespace XML document testns.xml that we tried above fails with the EXPAT parser. The docstrings in processed by doctest can contain tracebacks within them, but a better approach to special cases is to utilize unittest.

Using Unittest

Another test included with gnosis.xml.objectify is The main reason for this test is simply that users of the subpackage who use the EXPAT parser currently need to call a special setup function to enable processing XML documents with namespaces (this fact is evolutionary rather than designed, and might change later). The old test tried printing the object without the setup, caught the exception if it occurs, then prints with the setup if needed (and gives a message about what happened).

As with the tool lets you examine how a novel XML document is represented by gnosis.xml.objectify. But as before, there are a number of concrete behaviors that we would like to verify. An enhanced and expanded version of uses unittest to check what happens when various actions are performed, including assertions that certain conditions or (approximate) equalities hold, or that certain exceptions are raised when expected. Let us take a look: script with self-diagnostics

"Objectify using Expat parser, namespace setup where needed"

import unittest, sys, cStringIO
from os.path import isfile
from gnosis.xml.objectify import make_instance, config_nspace_sep,\
BASIC, NS = 'test.xml','testns.xml'

class Prerequisite(unittest.TestCase):
    def testHaveLibrary(self):
        "Import the gnosis.xml.objectify library"
        import gnosis.xml.objectify
    def testHaveFiles(self):
        "Check for sample XML files, NS and BASIC"

class ExpatTest(unittest.TestCase):
    def setUp(self):
        self.orig_nspace = XML_Objectify.expat_kwargs.get('nspace_sep','')
    def testNoNamespace(self):
        "Objectify namespace-free XML document"
        o = make_instance(BASIC)
    def testNamespaceFailure(self):
        "Raise SyntaxError on non-setup namespace XML"
        self.assertRaises(SyntaxError, make_instance, NS)
    def testNamespaceSuccess(self):
        "Sucessfully objectify NS after setup"
        o = make_instance(NS)
    def testNspaceBasic(self):
        "Successfully objectify BASIC despite extra setup"
        o = make_instance(BASIC)
    def tearDown(self):
        XML_Objectify.expat_kwargs['nspace_sep'] = self.orig_nspace

if __name__ == '__main__':
    if len(sys.argv) == 1:
    elif sys.argv[1] in ('-q','--quiet'):
        suite = unittest.TestSuite()
        out = cStringIO.StringIO()
        results = unittest.TextTestRunner(stream=out).run(suite)
        if not results.wasSuccessful():
            for failure in results.failures:
                print "FAIL:", failure[0]
            for error in results.errors:
                print "ERROR:", error[0]
    elif sys.argv[1].startswith('-'):   # pass args to unittest
        from gnosis.xml.objectify import pyobj_printer as show
        for fname in sys.argv[1:]:
            print show(make_instance(fname)).encode('UTF-8')

Using unittest adds quite a few capabilities to the simpler doctest style. We can divide our tests into several classes, each inheriting from unittest.TestCase. Within each test class, every method whose name begins with ".test" is considered another test. The two extra classes defined for ExpatTest are interesting: .setUp() is run prior to each test performed with the class, and .tearDown() is run at the end of the test (whether the test succeeds, fails, or raises an error). In our case above we perform a bit of bookkeeping around the special expat_kwargs dictionary to make sure each test runs independently.

The distinction between failures and errors is important, by the way. A test can fail because some specific assertion does not hold (assertion methods either start with ".fail" or ".assert"). In a sense, failures are expected--at least in the sense we have specifically checked for them. Errors, on the other hand, are unexpected problems--since we do not know in advance what can go wrong, we will need to examine the traceback in the actual test run to diagnose such problems. However, we can plan failures to give hints on diagnosing errors. For example, if Prerequisite.haveFiles() fails, that will show up as errors in some of the TestExpat tests; if the former succeeds, that tells you to look elsewhere for the source of the error(s).

A particular test method within a unittest.TestCase derived class may contain some .assert...() or .fail...() methods, but it may also simply carry out a series of actions that we believe should run successfully. If the test method does not run as anticipated, we will get an error (and a traceback describing the error).

The _main_ block in is worth looking over also. In the simplest case, we can run the test cases using just unittest.main() which figures out what needs to be run. Done this way, the unittest module will accept a -v option for more verbose output. With filename(s) specified we print out representations of the named XML file(s) after performing namespace setup, thereby maintaining rough backward compatibility with older versions of the tool.

The most interesting branch of _main_ is the one that looks for the -q or --quiet flags. As you would expect, this branch is quiet unless failures or errors occur. Moreover, being quiet, only a one-line report of where the failure/error is shown for each problem, not a whole diagnostic traceback. Aside from the direct utility of the quiet output style, the branch illustrates custom testing against test suites and control of result reporting. The somewhat wordy default output of unittest.TextTestRunner() is directed to the StringIO out--if you want to see it, you are welcome to poke at out.getvalue(). But the result object lets us test for overall success, and process the failures and errors if success was not total. Obviously, being values in variables, you could easily log the contents of the result object, send the as email, display them in a GUI, or whatever, rather than simply print to STDOUT.

Combining Tests

Perhaps the nicest feature of the unittest framework is that it lets you easily combine tests contained in different modules. With Python 2.3+, in fact, you can even convert doctest tests into unittest suites. Let us combine the tests we have created so far into a script (admittedly an exageration for what we test so far): combined unit tests

"Combine tests for gnosis.xml.objectify package (req 2.3+)"
import unittest, doctest, test_basic, test_expat
suite = doctest.DocTestSuite(test_basic)

Since simply contains test classes, they can easily be added to the local test suite. The function doctest.DocTestSuite() performs the conversion of a docstring test. Let's see what does when run:

Successful output from

$ python2.3
doctest of ... ok
Check for sample XML files, NS and BASIC ... ok
Import the gnosis.xml.objectify library ... ok
Raise SyntaxError on non-setup namespace XML ... ok
Sucessfully objectify NS after setup ... ok
Objectify namespace-free XML document ... ok
Successfully objectify BASIC despite extra setup ... ok

Ran 7 tests in 0.052s


Notice the descriptions of the tests performed: in the case of the unittest test methods their description is taken from the corresponding function docstring. If you do not specify a docstring, the module, class and method names are used as best-available descriptions. It is also intersting to see what we get if some tests fail (trimming the traceback details for this article):

$ mv testns.xml testns.xml# && python2.3 2>&1 | head -7
doctest of ... ok
Check for sample XML files, NS and BASIC ... FAIL
Import the gnosis.xml.objectify library ... ok
Raise SyntaxError on non-setup namespace XML ... ERROR
Sucessfully objectify NS after setup ... ERROR
Objectify namespace-free XML document ... ok
Successfully objectify BASIC despite extra setup ... ok

Incidentally, the final line written to STDERR on this failure is "FAILED (failures=1, errors=2)" which makes a good summary if you want such (compare with the final "OK" for success).

Working From Here

For this article I have merely looked at some typical usage of unittest and doctest by way of improving some tests in my own software. Take a look at the Python documentation for more details on the full range of methods available to test suites, test cases, and test results. All of them follow the pattern of the example presented, however.

Committing yourself to the methodology provided by Python's standard testing modules is good software practice. Test-driven development is a mantra in many software circles; I do not wish to weigh in strongly on such methodological disputes right here, but clearly Python is a language well suited to the test-driven model. Moreover, a package or library that is accompanied by a well considered set of tests is simply more useful to users than is one lacking them, if for no other reason than that the package is more likely to work as intended.


The documentation accompanying Python's standard library is really the best place to look for up-to-date details on unittest:

And on doctest:

The PyUnit website is worth looking at for supplemtary modules. The unittest module is derived from the formerly independent PyUnit. The older project site still has links to tools not including in unittest such as GUI front-ends for testing:

About The Author

Picture of Author David Mertz is feeling a bit testy. David may be reached at [email protected]; his life pored over at Check out David's book Text Processing in Python (