Python单元测试的9个技巧

时间:2022-05-22 08:51:28

前言:

requestspython知名的http爬虫库,同样简单易用,是python开源项目的TOP10。

pytestpython的单元测试框架,简单易用,在很多知名项目中应用。requestspython知名的http爬虫库,同样简单易用,是python开源项目的TOP10。关于这2个项目,之前都有过介绍,本文主要介绍requests项目如何使用pytest进行单元测试,会达到下面3个目标:

  • 熟练pytest的使用
  • 学习如何对项目进行单元测试
  • 深入requests的一些实现细节

本文分如下几个部分:

  • requests项目单元测试状况
  • 简单工具类如何测试
  • request-api如何测试
  • 底层API测试

1、requests项目单元测试状况

requests的单元测试代码全部在 tests 目录,使用 pytest.ini 进行配置。测试除pytest外,还需要安装:

 

库名 描述
httpbin 一个使用flask实现的http服务,可以客户端定义http响应,主要用于测试http协议
pytest-httpbin pytest的插件,封装httpbin的实现
pytest-mock pytest的插件,提供mock
pytest-cov pytest的插件,提供覆盖率

 

上述依赖 master 版本在requirement-dev文件中定义;2.24.0版本会在pipenv中定义。

测试用例使用make命令,子命令在Makefile中定义, 使用make ci运行所有单元测试结果如下:

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$ make ci
pytest tests --junitxml=report.xml
======================================================================================================= test session starts =======================================================================================================
platform linux -- Python 3.6.8, pytest-3.10.1, py-1.10.0, pluggy-0.13.1
rootdir: /home/work6/project/requests, inifile: pytest.ini
plugins: mock-2.0.0, httpbin-1.0.0, cov-2.9.0
collected 552 items                                                                                                                                                                                                               
 
tests/test_help.py ...                                                                                                                                                                                                      [  0%]
tests/test_hooks.py ...                                                                                                                                                                                                     [  1%]
tests/test_lowlevel.py ...............                                                                                                                                                                                      [  3%]
tests/test_packages.py ...                                                                                                                                                                                                  [  4%]
tests/test_requests.py .................................................................................................................................................................................................... [ 39%]
127.0.0.1 - - [10/Aug/2021 08:41:53] "GET /stream/4 HTTP/1.1" 200 756
.127.0.0.1 - - [10/Aug/2021 08:41:53] "GET /stream/4 HTTP/1.1" 500 59
----------------------------------------
Exception happened during processing of request from ('127.0.0.1', 46048)
Traceback (most recent call last):
  File "/usr/lib64/python3.6/wsgiref/handlers.py", line 138, in run
    self.finish_response()
x.........................................................................................                                                                                                                                 [ 56%]
tests/test_structures.py ....................                                                                                                                                                                               [ 59%]
tests/test_testserver.py ......s....                                                                                                                                                                                        [ 61%]
tests/test_utils.py ..s................................................................................................................................................................................................ssss [ 98%]
ssssss.....                                                                                                                                                                                                                 [100%]
 
----------------------------------------------------------------------------------- generated xml file: /home/work6/project/requests/report.xml -----------------------------------------------------------------------------------
======================================================================================= 539 passed, 12 skipped, 1 xfailed in 64.16 seconds ========================================================================================

可以看到requests在1分钟内,总共通过了539个测试用例,效果还是不错。使用 make coverage 查看单元测试覆盖率:

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$ make coverage
----------- coverage: platform linux, python 3.6.8-final-0 -----------
Name                          Stmts   Miss  Cover
-------------------------------------------------
requests/__init__.py             71     71     0%
requests/__version__.py          10     10     0%
requests/_internal_utils.py      16      5    69%
requests/adapters.py            222     67    70%
requests/api.py                  20     13    35%
requests/auth.py                174     54    69%
requests/certs.py                 4      4     0%
requests/compat.py               47     47     0%
requests/cookies.py             238    115    52%
requests/exceptions.py           35     29    17%
requests/help.py                 63     19    70%
requests/hooks.py                15      4    73%
requests/models.py              455    119    74%
requests/packages.py             16     16     0%
requests/sessions.py            283     67    76%
requests/status_codes.py         15     15     0%
requests/structures.py           40     19    52%
requests/utils.py               465    170    63%
-------------------------------------------------
TOTAL                          2189    844    61%
Coverage XML written to file coverage.xml

结果显示requests项目总体覆盖率61%,每个模块的覆盖率也清晰可见。

单元测试覆盖率使用代码行数进行判断,Stmts显示模块的有效行数,Miss显示未执行到的行。如果生成html的报告,还可以定位到具体未覆盖到的行;pycharmcoverage也有类似功能。

tests下的文件及测试类如下表:

 

文件 描述
compat python2和python3兼容
conftest pytest配置
test_help,test_packages,test_hooks,test_structures 简单测试类
utils.py 工具函数
test_utils 测试工具函数
test_requests 测试requests
testserver\server 模拟服务
test_testserver 模拟服务测试
test_lowlevel 使用模拟服务测试模拟网络测试

 

2、简单工具类如何测试

2.1 test_help 实现分析

先从最简单的test_help上手,测试类和被测试对象命名是对应的。先看看被测试的模块help.py。这个模块主要是2个函数 info _implementation:

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import idna
 
def _implementation():
    ...
     
def info():
    ...
    system_ssl = ssl.OPENSSL_VERSION_NUMBER
    system_ssl_info = {
        'version': '%x' % system_ssl if system_ssl is not None else ''
    }
    idna_info = {
        'version': getattr(idna, '__version__', ''),
    }
    ...
    return {
        'platform': platform_info,
        'implementation': implementation_info,
        'system_ssl': system_ssl_info,
        'using_pyopenssl': pyopenssl is not None,
        'pyOpenSSL': pyopenssl_info,
        'urllib3': urllib3_info,
        'chardet': chardet_info,
        'cryptography': cryptography_info,
        'idna': idna_info,
        'requests': {
            'version': requests_version,
        },
    }

info提供系统环境的信息, _implementation是其内部实现,以下划线*_*开头。再看测试类test_help:

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from requests.help import info
 
def test_system_ssl():
    """Verify we're actually setting system_ssl when it should be available."""
    assert info()['system_ssl']['version'] != ''
 
class VersionedPackage(object):
    def __init__(self, version):
        self.__version__ = version
 
def test_idna_without_version_attribute(mocker):
    """Older versions of IDNA don't provide a __version__ attribute, verify
    that if we have such a package, we don't blow up.
    """
    mocker.patch('requests.help.idna', new=None)
    assert info()['idna'] == {'version': ''}
 
def test_idna_with_version_attribute(mocker):
    """Verify we're actually setting idna version when it should be available."""
    mocker.patch('requests.help.idna', new=VersionedPackage('2.6'))
    assert info()['idna'] == {'version': '2.6'}

首先从头部的导入信息可以看到,仅仅对info函数进行测试,这个容易理解。info测试通过,自然覆盖到_implementation这个内部函数。这里可以得到单元测试的第1个技巧:仅对public的接口进行测试

test_idna_without_version_attributetest_idna_with_version_attribute均有一个mocker参数,这是pytest-mock提供的功能,会自动注入一个mock实现。使用这个mock对idna模块进行模拟

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# 模拟空实现
mocker.patch('requests.help.idna', new=None)
# 模拟版本2.6
mocker.patch('requests.help.idna', new=VersionedPackage('2.6'))

可能大家会比较奇怪,这里patch模拟的是 requests.help.idna , 而我们在help中导入的是 inda 模块。这是因为在requests.packages中对inda进行了模块名重定向:

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for package in ('urllib3', 'idna', 'chardet'):
    locals()[package] = __import__(package)
    # This traversal is apparently necessary such that the identities are
    # preserved (requests.packages.urllib3.* is urllib3.*)
    for mod in list(sys.modules):
        if mod == package or mod.startswith(package + '.'):
            sys.modules['requests.packages.' + mod] = sys.modules[mod]

使用mocker后,idna的__version__信息就可以进行控制,这样info中的idna结果也就可以预期。那么可以得到第2个技巧:使用mock辅助单元测试

2.2 test_hooks 实现分析

我们继续查看hooks如何进行测试:

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from requests import hooks
 
def hook(value):
    return value[1:]
 
@pytest.mark.parametrize(
    'hooks_list, result', (
        (hook, 'ata'),
        ([hook, lambda x: None, hook], 'ta'),
    )
)
def test_hooks(hooks_list, result):
    assert hooks.dispatch_hook('response', {'response': hooks_list}, 'Data') == result
 
def test_default_hooks():
    assert hooks.default_hooks() == {'response': []}

hooks模块的2个接口default_hooksdispatch_hook都进行了测试。其中default_hooks是纯函数,无参数有返回值,这种函数最容易测试,仅仅检查返回值是否符合预期即可。dispatch_hook会复杂一些,还涉及对回调函数(hook函数)的调用:

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def dispatch_hook(key, hooks, hook_data, **kwargs):
    """Dispatches a hook dictionary on a given piece of data."""
    hooks = hooks or {}
    hooks = hooks.get(key)
    if hooks:
        # 判断钩子函数
        if hasattr(hooks, '__call__'):
            hooks = [hooks]
        for hook in hooks:
            _hook_data = hook(hook_data, **kwargs)
            if _hook_data is not None:
                hook_data = _hook_data
    return hook_data

pytest.mark.parametrize提供了2组参数进行测试。第一组参数hook和ata很简单,hook是一个函数,会对参数裁剪,去掉首位,ata是期望的返回值。test_hooksresponse的参数是Data,所以结果应该是ata。第二组参数中的第一个参数会复杂一些,变成了一个数组,首位还是hook函数,中间使用一个匿名函数,匿名函数没有返回值,这样覆盖到 if _hook_data is not None: 的旁路分支。执行过程如下:

  • hook函数裁剪Data首位,剩余ata
  • 匿名函数不对结果修改,剩余ata
  • hook函数继续裁剪ata首位,剩余ta

经过测试可以发现dispatch_hook的设计十分巧妙,使用pipeline模式,将所有的钩子串起来,这是和事件机制不一样的地方。细心的话,我们可以发现 if hooks: 并未进行旁路测试,这个不够严谨,有违我们的第3个技巧:

测试尽可能覆盖目标函数的所有分支

2.3 test_structures 实现分析

LookupDict的测试用例如下:

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class TestLookupDict:
 
    @pytest.fixture(autouse=True)
    def setup(self):
        """LookupDict instance with "bad_gateway" attribute."""
        self.lookup_dict = LookupDict('test')
        self.lookup_dict.bad_gateway = 502
 
    def test_repr(self):
        assert repr(self.lookup_dict) == "<lookup 'test'>"
 
    get_item_parameters = pytest.mark.parametrize(
        'key, value', (
            ('bad_gateway', 502),
            ('not_a_key', None)
        )
    )
 
    @get_item_parameters
    def test_getitem(self, key, value):
        assert self.lookup_dict[key] == value
 
    @get_item_parameters
    def test_get(self, key, value):
        assert self.lookup_dict.get(key) == value

可以发现使用setup方法配合@pytest.fixture,给所有测试用例初始化了一个lookup_dict对象;同时pytest.mark.parametrize可以在不同的测试用例之间复用的,我们可以得到第4个技巧:

使用pytest.fixture复用被测试对象,使用pytest.mark.parametriz复用测试参数

通过TestLookupDicttest_getitemtest_get可以更直观的了解LookupDict的get和__getitem__方法的作用:

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class LookupDict(dict):
    ...
    def __getitem__(self, key):
        # We allow fall-through here, so values default to None
        return self.__dict__.get(key, None)
 
    def get(self, key, default=None):
        return self.__dict__.get(key, default)
  • get自定义字典,使其可以使用 get 方法获取值
  • __getitem__自定义字典,使其可以使用 [] 符合获取值

CaseInsensitiveDict的测试用例在test_structurestest_requests中都有测试,前者主要是基础测试,后者偏向业务使用层面,我们可以看到这两种差异:

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class TestCaseInsensitiveDict:
 
# 类测试
 
def test_repr(self):
 
assert repr(self.case_insensitive_dict) == "{'Accept': 'application/json'}"
 
def test_copy(self):
 
copy = self.case_insensitive_dict.copy()
 
assert copy is not self.case_insensitive_dict
 
assert copy == self.case_insensitive_dict
 
class TestCaseInsensitiveDict:
 
# 使用方法测试
 
def test_delitem(self):
 
cid = CaseInsensitiveDict()
 
cid['Spam'] = 'someval'
 
del cid['sPam']
 
assert 'spam' not in cid
 
assert len(cid) == 0
 
def test_contains(self):
 
cid = CaseInsensitiveDict()
 
cid['Spam'] = 'someval'
 
assert 'Spam' in cid
 
assert 'spam' in cid
 
assert 'SPAM' in cid
 
assert 'sPam' in cid
 
assert 'notspam' not in cid

借鉴上面的测试方法,不难得出第5个技巧:

可以从不同的层面对同一个对象进行单元测试

后面的test_lowleveltest_requests也应用了这种技巧

2.4 utils.py

utils中构建了一个可以写入env的生成器(由yield关键字提供),可以当上下文装饰器使用:

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import contextlib
 
import os
 
@contextlib.contextmanager
 
def override_environ(**kwargs):
 
save_env = dict(os.environ)
 
for key, value in kwargs.items():
 
if value is None:
 
del os.environ[key]
 
else:
 
os.environ[key] = value
 
try:
 
yield
 
finally:
 
os.environ.clear()
 
os.environ.update(save_env)

下面是使用方法示例:

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# test_requests.py
 
kwargs = {
 
var: proxy
 
}
 
# 模拟控制proxy环境变量
 
with override_environ(**kwargs):
 
proxies = session.rebuild_proxies(prep, {})
 
def rebuild_proxies(self, prepared_request, proxies):
 
bypass_proxy = should_bypass_proxies(url, no_proxy=no_proxy)
 
def should_bypass_proxies(url, no_proxy):
 
...
 
get_proxy = lambda k: os.environ.get(k) or os.environ.get(k.upper())
 
...

得出第6个技巧:涉及环境变量的地方,可以使用上下文装饰器进行模拟多种环境变量

2.5 utils测试用例

utils的测试用例较多,我们选择部分进行分析。先看to_key_val_list函数:

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# 对象转列表
 
def to_key_val_list(value):
 
if value is None:
 
return None
 
if isinstance(value, (str, bytes, bool, int)):
 
raise ValueError('cannot encode objects that are not 2-tuples')
 
if isinstance(value, Mapping):
 
value = value.items()
 
return list(value)

对应的测试用例TestToKeyValList:

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class TestToKeyValList:
 
@pytest.mark.parametrize(
 
'value, expected', (
 
([('key', 'val')], [('key', 'val')]),
 
((('key', 'val'), ), [('key', 'val')]),
 
({'key': 'val'}, [('key', 'val')]),
 
(None, None)
 
))
 
def test_valid(self, value, expected):
 
assert to_key_val_list(value) == expected
 
def test_invalid(self):
 
with pytest.raises(ValueError):
 
to_key_val_list('string')

重点是test_invalid中使用pytest.raise对异常的处理:

第7个技巧:使用pytest.raises对异常进行捕获处理

TestSuperLen介绍了几种进行IO模拟测试的方法:

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class TestSuperLen:
 
@pytest.mark.parametrize(
 
'stream, value', (
 
(StringIO.StringIO, 'Test'),
 
(BytesIO, b'Test'),
 
pytest.param(cStringIO, 'Test',
 
marks=pytest.mark.skipif('cStringIO is None')),
 
))
 
def test_io_streams(self, stream, value):
 
"""Ensures that we properly deal with different kinds of IO streams."""
 
assert super_len(stream()) == 0
 
assert super_len(stream(value)) == 4
 
def test_super_len_correctly_calculates_len_of_partially_read_file(self):
 
"""Ensure that we handle partially consumed file like objects."""
 
s = StringIO.StringIO()
 
s.write('foobarbogus')
 
assert super_len(s) == 0
 
@pytest.mark.parametrize(
 
'mode, warnings_num', (
 
('r', 1),
 
('rb', 0),
 
))
 
def test_file(self, tmpdir, mode, warnings_num, recwarn):
 
file_obj = tmpdir.join('test.txt')
 
file_obj.write('Test')
 
with file_obj.open(mode) as fd:
 
assert super_len(fd) == 4
 
assert len(recwarn) == warnings_num
 
def test_super_len_with_tell(self):
 
foo = StringIO.StringIO('12345')
 
assert super_len(foo) == 5
 
foo.read(2)
 
assert super_len(foo) == 3
 
def test_super_len_with_fileno(self):
 
with open(__file__, 'rb') as f:
 
length = super_len(f)
 
file_data = f.read()
 
assert length == len(file_data)

使用StringIO来模拟IO操作,可以配置各种IO的测试。当然也可以使用BytesIO/cStringIO, 不过单元测试用例一般不关注性能,StringIO简单够用。

pytest提供tmpdirfixture,可以进行文件读写操作测试

可以使用__file__来进行文件的只读测试,__file__表示当前文件,不会产生副作用。

第8个技巧:使用IO模拟配合进行单元测试

2.6 request-api如何测试

requests的测试需要httpbinpytest-httpbin,前者会启动一个本地服务,后者会安装一个pytest插件,测试用例中可以得到httpbinfixture,用来操作这个服务的URL。

 

功能
TestRequests requests业务测试
TestCaseInsensitiveDict 大小写不敏感的字典测试
TestMorselToCookieExpires cookie过期测试
TestMorselToCookieMaxAge cookie大小
TestTimeout 响应超时的测试
TestPreparingURLs URL预处理
... 一些零碎的测试用例

 

坦率的讲:这个测试用例内容庞大,达到2500行。看起来是针对各种业务的零散case,我并没有完全理顺其组织逻辑。我选择一些感兴趣的业务进行介绍, 先看TimeOut的测试:

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TARPIT = 'http://10.255.255.1'
 
class TestTimeout:
 
def test_stream_timeout(self, httpbin):
 
try:
 
requests.get(httpbin('delay/10'), timeout=2.0)
 
except requests.exceptions.Timeout as e:
 
assert 'Read timed out' in e.args[0].args[0]
 
@pytest.mark.parametrize(
 
'timeout', (
 
(0.1, None),
 
Urllib3Timeout(connect=0.1, read=None)
 
))
 
def test_connect_timeout(self, timeout):
 
try:
 
requests.get(TARPIT, timeout=timeout)
 
pytest.fail('The connect() request should time out.')
 
except ConnectTimeout as e:
 
assert isinstance(e, ConnectionError)
 
assert isinstance(e, Timeout)

test_stream_timeout利用httpbin创建了一个延迟10s响应的接口,然后请求本身设置成2s,这样可以收到一个本地timeout的错误。test_connect_timeout则是访问一个不存在的服务,捕获连接超时的错误。

TestRequests都是对requests的业务进程测试,可以看到至少是2种:

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class TestRequests:
 
def test_basic_building(self):
 
req = requests.Request()
 
req.url = 'http://kennethreitz.org/'
 
req.data = {'life': '42'}
 
pr = req.prepare()
 
assert pr.url == req.url
 
assert pr.body == 'life=42'
 
def test_path_is_not_double_encoded(self):
 
request = requests.Request('GET', "http://0.0.0.0/get/test case").prepare()
 
assert request.path_url == '/get/test%20case
 
...
 
def test_HTTP_200_OK_GET_ALTERNATIVE(self, httpbin):
 
r = requests.Request('GET', httpbin('get'))
 
s = requests.Session()
 
s.proxies = getproxies()
 
r = s.send(r.prepare())
 
assert r.status_code == 200
 
ef test_set_cookie_on_301(self, httpbin):
 
s = requests.session()
 
url = httpbin('cookies/set?foo=bar')
 
s.get(url)
 
assert s.cookies['foo'] == 'bar'
  • 对url进行校验,只需要对request进行prepare,这种情况下,请求并未发送,少了网络传输,测试用例会更迅速
  • 需要响应数据的情况,需要使用httbin构建真实的请求-响应数据

3、底层API测试

testserver构建一个简单的基于线程的tcp服务,这个tcp服务具有__enter____exit__方法,还可以当一个上下文环境使用。

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class TestTestServer:
 
def test_basic(self):
 
"""messages are sent and received properly"""
 
question = b"success?"
 
answer = b"yeah, success"
 
def handler(sock):
 
text = sock.recv(1000)
 
assert text == question
 
sock.sendall(answer)
 
with Server(handler) as (host, port):
 
sock = socket.socket()
 
sock.connect((host, port))
 
sock.sendall(question)
 
text = sock.recv(1000)
 
assert text == answer
 
sock.close()
 
def test_text_response(self):
 
"""the text_response_server sends the given text"""
 
server = Server.text_response_server(
 
"HTTP/1.1 200 OK\r\n" +
 
"Content-Length: 6\r\n" +
 
"\r\nroflol"
 
)
 
with server as (host, port):
 
r = requests.get('http://{}:{}'.format(host, port))
 
assert r.status_code == 200
 
assert r.text == u'roflol'
 
assert r.headers['Content-Length'] == '6'

test_basic方法对Server进行基础校验,确保收发双方可以正确的发送和接收数据。先是客户端的sock发送question,然后服务端在handler中判断收到的数据是question,确认后返回answer,最后客户端再确认可以正确收到answer响应。test_text_response方法则不完整的测试了http协议。按照http协议的规范发送了http请求,Server.text_response_server会回显请求。下面是模拟浏览器的锚点定位不会经过网络传输的testcase:

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def test_fragment_not_sent_with_request():
 
"""Verify that the fragment portion of a URI isn't sent to the server."""
 
def response_handler(sock):
 
req = consume_socket_content(sock, timeout=0.5)
 
sock.send(
 
b'HTTP/1.1 200 OK\r\n'
 
b'Content-Length: '+bytes(len(req))+b'\r\n'
 
b'\r\n'+req
 
)
 
close_server = threading.Event()
 
server = Server(response_handler, wait_to_close_event=close_server)
 
with server as (host, port):
 
url = 'http://{}:{}/path/to/thing/#view=edit&token=hunter2'.format(host, port)
 
r = requests.get(url)
 
raw_request = r.content
 
assert r.status_code == 200
 
headers, body = raw_request.split(b'\r\n\r\n', 1)
 
status_line, headers = headers.split(b'\r\n', 1)
 
assert status_line == b'GET /path/to/thing/ HTTP/1.1'
 
for frag in (b'view', b'edit', b'token', b'hunter2'):
 
assert frag not in headers
 
assert frag not in body
 
close_server.set()

可以看到请求的path /path/to/thing/#view=edit&token=hunter2,其中 # 后面的部分是本地锚点,不应该进行网络传输。上面测试用例中,对接收到的响应进行判断,鉴别响应头和响应body中不包含这些关键字。

结合requests的两个层面的测试,们可以得出第9个技巧:

构造模拟服务配合测试

小结:

简单小结一下,从requests的单元测试实践中,可以得到下面9个技巧:

  1. 仅对public的接口进行测试
  2. 使用mock辅助单元测试
  3. 测试尽可能覆盖目标函数的所有分支
  4. 使用pytest.fixture复用被测试对象,使用pytest.mark.parametriz复用测试参数
  5. 可以从不同的层面对同一个对象进行单元测试
  6. 涉及环境变量的地方,可以使用上下文装饰器进行模拟多种环境变量
  7. 使用pytest.raises对异常进行捕获处理
  8. 使用IO模拟配合进行单元测试
  9. 构造模拟服务配合测试

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原文链接:https://developer.51cto.com/art/202109/683755.htm?utm_source=tuicool&utm_medium=referral