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Python单元测试技巧 Python单元测试的9个技巧技巧

肖恩 人气:0
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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:

本文分如下几个部分:

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:

$ 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:

$ 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:

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:

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

# 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:

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:

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:

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:

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:

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:

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) 


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:

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:

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:

# 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:

# 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:

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:

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:

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:

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'

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。

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:

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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