我有一些测试数据，想为每个项目创建一个单元测试。我的第一个想法是这样做的:
import unittest
l = [["foo", "a", "a",], ["bar", "a", "b"], ["lee", "b", "b"]]
class TestSequence(unittest.TestCase):
def testsample(self):
for name, a,b in l:
print "test", name
self.assertEqual(a,b)
if __name__ == '__main__':
unittest.main()
这样做的缺点是它在一个测试中处理所有数据。我想在飞行中为每个项目生成一个测试。有什么建议吗?
当前回答
load_tests是2.7中引入的一种鲜为人知的机制，用于动态创建TestSuite。有了它，您可以轻松地创建参数化测试。
例如:
import unittest
class GeneralTestCase(unittest.TestCase):
def __init__(self, methodName, param1=None, param2=None):
super(GeneralTestCase, self).__init__(methodName)
self.param1 = param1
self.param2 = param2
def runTest(self):
pass # Test that depends on param 1 and 2.
def load_tests(loader, tests, pattern):
test_cases = unittest.TestSuite()
for p1, p2 in [(1, 2), (3, 4)]:
test_cases.addTest(GeneralTestCase('runTest', p1, p2))
return test_cases
该代码将运行load_tests返回的测试套件中的所有测试用例。发现机制不会自动运行其他测试。
或者，您也可以使用此票据所示的继承:http://bugs.python.org/msg151444
其他回答
这实际上与之前的回答中提到的parameterized相同，但具体到unittest:
def sub_test(param_list):
"""Decorates a test case to run it as a set of subtests."""
def decorator(f):
@functools.wraps(f)
def wrapped(self):
for param in param_list:
with self.subTest(**param):
f(self, **param)
return wrapped
return decorator
使用示例:
class TestStuff(unittest.TestCase):
@sub_test([
dict(arg1='a', arg2='b'),
dict(arg1='x', arg2='y'),
])
def test_stuff(self, arg1, arg2):
...
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
load_tests是2.7中引入的一种鲜为人知的机制，用于动态创建TestSuite。有了它，您可以轻松地创建参数化测试。
例如:
import unittest
class GeneralTestCase(unittest.TestCase):
def __init__(self, methodName, param1=None, param2=None):
super(GeneralTestCase, self).__init__(methodName)
self.param1 = param1
self.param2 = param2
def runTest(self):
pass # Test that depends on param 1 and 2.
def load_tests(loader, tests, pattern):
test_cases = unittest.TestSuite()
for p1, p2 in [(1, 2), (3, 4)]:
test_cases.addTest(GeneralTestCase('runTest', p1, p2))
return test_cases
该代码将运行load_tests返回的测试套件中的所有测试用例。发现机制不会自动运行其他测试。
或者，您也可以使用此票据所示的继承:http://bugs.python.org/msg151444
我使用元类和装饰器来生成测试。您可以检查我的实现python_wrap_cases。这个库不需要任何测试框架。
你的例子:
import unittest
from python_wrap_cases import wrap_case
@wrap_case
class TestSequence(unittest.TestCase):
@wrap_case("foo", "a", "a")
@wrap_case("bar", "a", "b")
@wrap_case("lee", "b", "b")
def testsample(self, name, a, b):
print "test", name
self.assertEqual(a, b)
控制台输出:
testsample_u'bar'_u'a'_u'b' (tests.example.test_stackoverflow.TestSequence) ... test bar
FAIL
testsample_u'foo'_u'a'_u'a' (tests.example.test_stackoverflow.TestSequence) ... test foo
ok
testsample_u'lee'_u'b'_u'b' (tests.example.test_stackoverflow.TestSequence) ... test lee
ok
你也可以使用发电机。例如，这段代码用参数a__list和b__list生成所有可能的测试组合
import unittest
from python_wrap_cases import wrap_case
@wrap_case
class TestSequence(unittest.TestCase):
@wrap_case(a__list=["a", "b"], b__list=["a", "b"])
def testsample(self, a, b):
self.assertEqual(a, b)
控制台输出:
testsample_a(u'a')_b(u'a') (tests.example.test_stackoverflow.TestSequence) ... ok
testsample_a(u'a')_b(u'b') (tests.example.test_stackoverflow.TestSequence) ... FAIL
testsample_a(u'b')_b(u'a') (tests.example.test_stackoverflow.TestSequence) ... FAIL
testsample_a(u'b')_b(u'b') (tests.example.test_stackoverflow.TestSequence) ... ok
您将从尝试testscenario库中受益。
testscenes为python unittest风格的测试提供了干净的依赖注入。这可以用于接口测试(通过单个测试套件测试许多实现)或经典的依赖项注入(在测试代码本身的外部提供依赖项测试，允许在不同情况下轻松测试)。
