元编程很有趣，但它也会碍事。这里的大多数解决方案都很难:

有选择地启动测试 指向给出测试名称的代码

所以，我的第一个建议是遵循简单/显式路径(适用于任何测试运行程序):

import unittest

class TestSequence(unittest.TestCase):

    def _test_complex_property(self, a, b):
        self.assertEqual(a,b)

    def test_foo(self):
        self._test_complex_property("a", "a")
    def test_bar(self):
        self._test_complex_property("a", "b")
    def test_lee(self):
        self._test_complex_property("b", "b")

if __name__ == '__main__':
    unittest.main()

既然我们不应该重复，我的第二个建议建立在Javier的回答之上:接受基于属性的测试。假设库:

“在生成测试用例方面比我们人类更加无情地迂回” 会提供简单的计数例子吗 与任何测试运行程序一起工作 具有更多有趣的特性(统计数据、额外的测试输出……) 类TestSequence (unittest.TestCase): st.text @given (st.text () () Def test_complex_property(self, a, b): self.assertEqual (a, b)

为了测试您的特定示例，只需添加:

    @example("a", "a")
    @example("a", "b")
    @example("b", "b")

为了只运行一个特定的示例，您可以注释掉其他示例(提供的示例将首先运行)。你可能想要使用@given(st.nothing())。另一种选择是将整个区块替换为:

    @given(st.just("a"), st.just("b"))

好的，您没有不同的测试名称。但也许你只需要:

被测属性的描述性名称。 哪个输入会导致失败(伪造的例子)。

有趣的例子

我在一种非常特殊的参数化测试风格上遇到了麻烦。我们所有的Selenium测试都可以在本地运行，但它们也应该能够在SauceLabs上的多个平台上远程运行。基本上，我想要使用大量已经编写好的测试用例，并用尽可能少的代码更改参数化它们。此外，我需要能够将参数传递到setUp方法中，这是我在其他地方没有看到的任何解决方案。

以下是我想到的:

import inspect
import types

test_platforms = [
    {'browserName': "internet explorer", 'platform': "Windows 7", 'version': "10.0"},
    {'browserName': "internet explorer", 'platform': "Windows 7", 'version': "11.0"},
    {'browserName': "firefox", 'platform': "Linux", 'version': "43.0"},
]


def sauce_labs():
    def wrapper(cls):
        return test_on_platforms(cls)
    return wrapper


def test_on_platforms(base_class):
    for name, function in inspect.getmembers(base_class, inspect.isfunction):
        if name.startswith('test_'):
            for platform in test_platforms:
                new_name = '_'.join(list([name, ''.join(platform['browserName'].title().split()), platform['version']]))
                new_function = types.FunctionType(function.__code__, function.__globals__, new_name,
                                                  function.__defaults__, function.__closure__)
                setattr(new_function, 'platform', platform)
                setattr(base_class, new_name, new_function)
            delattr(base_class, name)

    return base_class

With this, all I had to do was add a simple decorator @sauce_labs() to each regular old TestCase, and now when running them, they're wrapped up and rewritten, so that all the test methods are parameterized and renamed. LoginTests.test_login(self) runs as LoginTests.test_login_internet_explorer_10.0(self), LoginTests.test_login_internet_explorer_11.0(self), and LoginTests.test_login_firefox_43.0(self), and each one has the parameter self.platform to decide what browser/platform to run against, even in LoginTests.setUp, which is crucial for my task since that's where the connection to SauceLabs is initialized.

无论如何，我希望这对那些希望对他们的测试进行类似的“全局”参数化的人有所帮助!

还有一个假说，它增加了模糊或基于属性的测试。

这是一种非常强大的测试方法。

只使用元类，如这里所示;

class DocTestMeta(type):
    """
    Test functions are generated in metaclass due to the way some
    test loaders work. For example, setupClass() won't get called
    unless there are other existing test methods, and will also
    prevent unit test loader logic being called before the test
    methods have been defined.
    """
    def __init__(self, name, bases, attrs):
        super(DocTestMeta, self).__init__(name, bases, attrs)

    def __new__(cls, name, bases, attrs):
        def func(self):
            """Inner test method goes here"""
            self.assertTrue(1)

        func.__name__ = 'test_sample'
        attrs[func.__name__] = func
        return super(DocTestMeta, cls).__new__(cls, name, bases, attrs)

class ExampleTestCase(TestCase):
    """Our example test case, with no methods defined"""
    __metaclass__ = DocTestMeta

输出:

test_sample (ExampleTestCase) ... OK

除了使用setattr，我们还可以在Python 3.2及更高版本中使用load_tests。

class Test(unittest.TestCase):
    pass

def _test(self, file_name):
    open(file_name, 'r') as f:
        self.assertEqual('test result',f.read())

def _generate_test(file_name):
    def test(self):
        _test(self, file_name)
    return test

def _generate_tests():
    for file in files:
        file_name = os.path.splitext(os.path.basename(file))[0]
        setattr(Test, 'test_%s' % file_name, _generate_test(file))

test_cases = (Test,)

def load_tests(loader, tests, pattern):
    _generate_tests()
    suite = TestSuite()
    for test_class in test_cases:
        tests = loader.loadTestsFromTestCase(test_class)
        suite.addTests(tests)
    return suite

if __name__ == '__main__':
    _generate_tests()
    unittest.main()

使用ddt库。它为测试方法添加了简单的装饰器:

import unittest
from ddt import ddt, data
from mycode import larger_than_two

@ddt
class FooTestCase(unittest.TestCase):

    @data(3, 4, 12, 23)
    def test_larger_than_two(self, value):
        self.assertTrue(larger_than_two(value))

    @data(1, -3, 2, 0)
    def test_not_larger_than_two(self, value):
        self.assertFalse(larger_than_two(value))

这个库可以用pip安装。它不需要nose，并且与标准库unittest模块一起出色地工作。