*import unittest*

import qc

class TestCompression(unittest.TestCase):

@qc.property

def test_compress_decompress(self):

"""Test that compressing and decompressing returns the original data."""

data = qc.str() # An arbitrary string. Values are randomized.

self.assertEqual(data, decompress(compress(data)), repr(data))

import qc

class TestCompression(unittest.TestCase):

@qc.property

def test_compress_decompress(self):

"""Test that compressing and decompressing returns the original data."""

data = qc.str() # An arbitrary string. Values are randomized.

self.assertEqual(data, decompress(compress(data)), repr(data))

That's an ordinary test with Python's built-in unittest framework (which is why there's so much boilerplate). Alternately, you could do the exact same thing with a different testing framework, like the minimally verbose, quite pleasant nose. The @qc.property decorator runs the decorated function several times, and each time the values returned by functions like qc.string() are different. In other words, QuickCheck is compatible with pretty much every unit test framework out there; it's not particularly demanding.

Functions like qc.str(), qc.int(), and so on, generate arbitrary values of a certain type. In the example above, we're asserting that the property holds true for all strings. When you run the tests, QuickCheck will generate randomized strings for testing.

You'll notice that I said "randomized", not "random". This is intentional. The distribution of values is tweaked to include interesting values, like empty strings, or strings with NUL characters in the middle, or strings containing English text. In general, QuickCheck tries to give a good mix of clever tricky values and randomness. This is essentially what you would do, if you had to write really thorough test cases by hand, except that you don't have to do it. In practice, the computer has fewer preconceptions about what constitutes sane data, so it will often find bugs that would never have occurred to you to write test cases for. It doesn't know how to subconsciously avoid the bugs.

You're not limited to the built-in arbitrary value functions. You can use them as building blocks to generate your own. For example:

*class Point(object):*

def __init__(self, x, y):

self.x, self.y = float(x), float(y)

def point():

"""Get an arbitrary point."""

x = qc.int(-20, 20)

y = qc.int(-34, 50)

return Point(x, y)

def __init__(self, x, y):

self.x, self.y = float(x), float(y)

def point():

"""Get an arbitrary point."""

x = qc.int(-20, 20)

y = qc.int(-34, 50)

return Point(x, y)

You can then use this to generate arbitrary point values in properties. Here's a nose-style test:

*@qc.property*

def test_triangle_inequality():

pt = point()

assert abs(pt.x) + abs(pt.y) >= math.sqrt(pt.x**2 + pt.y**2), (pt.x, pt.y)

def test_triangle_inequality():

pt = point()

assert abs(pt.x) + abs(pt.y) >= math.sqrt(pt.x**2 + pt.y**2), (pt.x, pt.y)

When you run this, something magical happens: QuickCheck will try to generate tricky values for both the x and y variables in the Point class, together, so you'll see points like (0, 0), (1, 1), (0, 1), (385904, 0), as well as totally random ones like (584, -35809648). In other words, rather than just drawing x and y values from a stream of random numbers with some tricky values in it, QuickCheck will actually try to generate tricky combinations of x and y coordinates.

**Functions for getting arbitrary data**

- int(low, high) gives ints, between the optional bounds low and high.

- long(low, high) gives longs, between the optional bounds low and high.

- float(low, high) gives floats, between the optional bounds low and high. No Infinities or NaN values.

str(length=None, maxlen=None) gives strings, of type str. The encoding is UTF-8. If length is given, the strings will be exactly that long. If maxlen is given, the string length will be at most maxlen characters.

- unicode(length=None, maxlen=None) gives unicode strings, of type unicode. If length is given, the strings will be exactly that long. If maxlen is given, the string length will be at most maxlen characters.

- name() gives names, in Unicode. These range from the prosaic, like "John Smith", to the exotic -- names containing non-breaking spaces, or email addresses, or Unicode characters outside the Basic Multilingual Plane. This is, if anything, less perverse than the names you will see in a sufficiently large set of Internet data.

- nameUtf8() is the same as name().encode('utf8').

- fromList(items) returns random items from a list. This is mostly useful for creating your own arbitrary data generator functions.

- randstr(length=None, maxlen=sys.maxint) gives strings of random bytes. If length is given, the strings will be exactly that long. If maxlen is given, the string length will be at most maxlen bytes.

The strings produced by str and unicode are randomized, but some effort has been put into making them sufficiently perverse as to reveal bugs in a whole lot of string processing code. The name list is loosely based on horrible memories of seeing name processing code crash on real-world data, over and over and over again, as it became ever more clear that the world is mad, and we are truly doomed. (This feeling passes once you get enough test coverage and things finally stop crashing. There is hope!)

The name and string example data in qc.arbitrary may be interesting as a source of more deteministic test case data. Feel free to borrow any of it. The internals are magic, but of the magical internal parts, the most interesting ones are in qc.arbitrary and qc.

Last updated on

**March 26th, 2012**