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
import unittest
def generator(test_class, a, b,c,d,name):
def test(self):
print('Testexecution=',name)
print('a=',a)
print('b=',b)
print('c=',c)
print('d=',d)
return test
def add_test_methods(test_class):
test_list = [[3,3,5,6, 'one'], [5,5,8,9, 'two'], [0,0,5,6, 'three'],[0,0,2,3,'Four']]
for case in test_list:
print('case=',case[0], case[1],case[2],case[3],case[4])
test = generator(test_class, case[0], case[1],case[2],case[3],case[4])
setattr(test_class, "test_%s" % case[4], test)
class TestAuto(unittest.TestCase):
def setUp(self):
print ('Setup')
pass
def tearDown(self):
print ('TearDown')
pass
add_test_methods(TestAuto)
if __name__ == '__main__':
unittest.main(verbosity=1)
其他回答
我发现这很适合我的目的，特别是当我需要生成在数据集合上执行稍微不同的过程的测试时。
import unittest
def rename(newName):
def renamingFunc(func):
func.__name__ == newName
return func
return renamingFunc
class TestGenerator(unittest.TestCase):
TEST_DATA = {}
@classmethod
def generateTests(cls):
for dataName, dataValue in TestGenerator.TEST_DATA:
for func in cls.getTests(dataName, dataValue):
setattr(cls, "test_{:s}_{:s}".format(func.__name__, dataName), func)
@classmethod
def getTests(cls):
raise(NotImplementedError("This must be implemented"))
class TestCluster(TestGenerator):
TEST_CASES = []
@staticmethod
def getTests(dataName, dataValue):
def makeTest(case):
@rename("{:s}".format(case["name"]))
def test(self):
# Do things with self, case, data
pass
return test
return [makeTest(c) for c in TestCluster.TEST_CASES]
TestCluster.generateTests()
TestGenerator类可以用来生成不同的测试用例集，比如TestCluster。
TestCluster可以被认为是TestGenerator接口的实现。
从Python 3.4开始，已经为此目的向unittest引入了子测试。详细信息请参见文档。TestCase。subTest是一个上下文管理器，它允许在测试中隔离断言，以便用参数信息报告失败，但它不会停止测试执行。下面是文档中的例子:
class NumbersTest(unittest.TestCase):
def test_even(self):
"""
Test that numbers between 0 and 5 are all even.
"""
for i in range(0, 6):
with self.subTest(i=i):
self.assertEqual(i % 2, 0)
测试运行的输出是:
======================================================================
FAIL: test_even (__main__.NumbersTest) (i=1)
----------------------------------------------------------------------
Traceback (most recent call last):
File "subtests.py", line 32, in test_even
self.assertEqual(i % 2, 0)
AssertionError: 1 != 0
======================================================================
FAIL: test_even (__main__.NumbersTest) (i=3)
----------------------------------------------------------------------
Traceback (most recent call last):
File "subtests.py", line 32, in test_even
self.assertEqual(i % 2, 0)
AssertionError: 1 != 0
======================================================================
FAIL: test_even (__main__.NumbersTest) (i=5)
----------------------------------------------------------------------
Traceback (most recent call last):
File "subtests.py", line 32, in test_even
self.assertEqual(i % 2, 0)
AssertionError: 1 != 0
这也是unittest2的一部分，因此可用于早期版本的Python。
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!
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
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。
