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python自動(dòng)化測試之DDT數(shù)據(jù)驅(qū)動(dòng)的實(shí)現(xiàn)代碼

 更新時(shí)間:2019年07月23日 15:41:58   作者:Secret608  
這篇文章主要介紹了python自動(dòng)化測試之DDT數(shù)據(jù)驅(qū)動(dòng)的實(shí)現(xiàn)代碼,本文給大家介紹的非常詳細(xì),具有一定的參考借鑒價(jià)值,需要的朋友可以參考下

時(shí)隔已久,再次冒煙,自動(dòng)化測試工作仍在繼續(xù),自動(dòng)化測試中的數(shù)據(jù)驅(qū)動(dòng)技術(shù)尤為重要,不然咋去實(shí)現(xiàn)數(shù)據(jù)分離呢,對(duì)吧,這里就簡單介紹下與傳統(tǒng)unittest自動(dòng)化測試框架匹配的DDT數(shù)據(jù)驅(qū)動(dòng)技術(shù)。

話不多說,先擼一波源碼,其實(shí)整體代碼并不多

# -*- coding: utf-8 -*-
# This file is a part of DDT (https://github.com/txels/ddt)
# Copyright 2012-2015 Carles Barrobés and DDT contributors
# For the exact contribution history, see the git revision log.
# DDT is licensed under the MIT License, included in
# https://github.com/txels/ddt/blob/master/LICENSE.md
import inspect
import json
import os
import re
import codecs
from functools import wraps
try:
  import yaml
except ImportError: # pragma: no cover
  _have_yaml = False
else:
  _have_yaml = True
__version__ = '1.2.1'
# These attributes will not conflict with any real python attribute
# They are added to the decorated test method and processed later
# by the `ddt` class decorator.
DATA_ATTR = '%values'   # store the data the test must run with
FILE_ATTR = '%file_path'  # store the path to JSON file
UNPACK_ATTR = '%unpack'  # remember that we have to unpack values
index_len = 5       # default max length of case index
try:
  trivial_types = (type(None), bool, int, float, basestring)
except NameError:
  trivial_types = (type(None), bool, int, float, str)
def is_trivial(value):
  if isinstance(value, trivial_types):
    return True
  elif isinstance(value, (list, tuple)):
    return all(map(is_trivial, value))
  return False
def unpack(func):
  """
  Method decorator to add unpack feature.
  """
  setattr(func, UNPACK_ATTR, True)
  return func
def data(*values):
  """
  Method decorator to add to your test methods.
  Should be added to methods of instances of ``unittest.TestCase``.
  """
  global index_len
  index_len = len(str(len(values)))
  return idata(values)
def idata(iterable):
  """
  Method decorator to add to your test methods.
  Should be added to methods of instances of ``unittest.TestCase``.
  """
  def wrapper(func):
    setattr(func, DATA_ATTR, iterable)
    return func
  return wrapper
def file_data(value):
  """
  Method decorator to add to your test methods.
  Should be added to methods of instances of ``unittest.TestCase``.
  ``value`` should be a path relative to the directory of the file
  containing the decorated ``unittest.TestCase``. The file
  should contain JSON encoded data, that can either be a list or a
  dict.
  In case of a list, each value in the list will correspond to one
  test case, and the value will be concatenated to the test method
  name.
  In case of a dict, keys will be used as suffixes to the name of the
  test case, and values will be fed as test data.
  """
  def wrapper(func):
    setattr(func, FILE_ATTR, value)
    return func
  return wrapper
def mk_test_name(name, value, index=0):
  """
  Generate a new name for a test case.
  It will take the original test name and append an ordinal index and a
  string representation of the value, and convert the result into a valid
  python identifier by replacing extraneous characters with ``_``.
  We avoid doing str(value) if dealing with non-trivial values.
  The problem is possible different names with different runs, e.g.
  different order of dictionary keys (see PYTHONHASHSEED) or dealing
  with mock objects.
  Trivial scalar values are passed as is.
  A "trivial" value is a plain scalar, or a tuple or list consisting
  only of trivial values.
  """
  # Add zeros before index to keep order
  index = "{0:0{1}}".format(index + 1, index_len)
  if not is_trivial(value):
    return "{0}_{1}".format(name, index)
  try:
    value = str(value)
  except UnicodeEncodeError:
    # fallback for python2
    value = value.encode('ascii', 'backslashreplace')
  test_name = "{0}_{1}_{2}".format(name, index, value)
  return re.sub(r'\W|^(?=\d)', '_', test_name)
def feed_data(func, new_name, test_data_docstring, *args, **kwargs):
  """
  This internal method decorator feeds the test data item to the test.
  """
  @wraps(func)
  def wrapper(self):
    return func(self, *args, **kwargs)
  wrapper.__name__ = new_name
  wrapper.__wrapped__ = func
  # set docstring if exists
  if test_data_docstring is not None:
    wrapper.__doc__ = test_data_docstring
  else:
    # Try to call format on the docstring
    if func.__doc__:
      try:
        wrapper.__doc__ = func.__doc__.format(*args, **kwargs)
      except (IndexError, KeyError):
        # Maybe the user has added some of the formating strings
        # unintentionally in the docstring. Do not raise an exception
        # as it could be that user is not aware of the
        # formating feature.
        pass
  return wrapper
def add_test(cls, test_name, test_docstring, func, *args, **kwargs):
  """
  Add a test case to this class.
  The test will be based on an existing function but will give it a new
  name.
  """
  setattr(cls, test_name, feed_data(func, test_name, test_docstring,
      *args, **kwargs))
def process_file_data(cls, name, func, file_attr):
  """
  Process the parameter in the `file_data` decorator.
  """
  cls_path = os.path.abspath(inspect.getsourcefile(cls))
  data_file_path = os.path.join(os.path.dirname(cls_path), file_attr)
  def create_error_func(message): # pylint: disable-msg=W0613
    def func(*args):
      raise ValueError(message % file_attr)
    return func
  # If file does not exist, provide an error function instead
  if not os.path.exists(data_file_path):
    test_name = mk_test_name(name, "error")
    test_docstring = """Error!"""
    add_test(cls, test_name, test_docstring,
         create_error_func("%s does not exist"), None)
    return
  _is_yaml_file = data_file_path.endswith((".yml", ".yaml"))
  # Don't have YAML but want to use YAML file.
  if _is_yaml_file and not _have_yaml:
    test_name = mk_test_name(name, "error")
    test_docstring = """Error!"""
    add_test(
      cls,
      test_name,
      test_docstring,
      create_error_func("%s is a YAML file, please install PyYAML"),
      None
    )
    return
  with codecs.open(data_file_path, 'r', 'utf-8') as f:
    # Load the data from YAML or JSON
    if _is_yaml_file:
      data = yaml.safe_load(f)
    else:
      data = json.load(f)
  _add_tests_from_data(cls, name, func, data)
def _add_tests_from_data(cls, name, func, data):
  """
  Add tests from data loaded from the data file into the class
  """
  for i, elem in enumerate(data):
    if isinstance(data, dict):
      key, value = elem, data[elem]
      test_name = mk_test_name(name, key, i)
    elif isinstance(data, list):
      value = elem
      test_name = mk_test_name(name, value, i)
    if isinstance(value, dict):
      add_test(cls, test_name, test_name, func, **value)
    else:
      add_test(cls, test_name, test_name, func, value)
def _is_primitive(obj):
  """Finds out if the obj is a "primitive". It is somewhat hacky but it works.
  """
  return not hasattr(obj, '__dict__')
def _get_test_data_docstring(func, value):
  """Returns a docstring based on the following resolution strategy:
  1. Passed value is not a "primitive" and has a docstring, then use it.
  2. In all other cases return None, i.e the test name is used.
  """
  if not _is_primitive(value) and value.__doc__:
    return value.__doc__
  else:
    return None
def ddt(cls):
  """
  Class decorator for subclasses of ``unittest.TestCase``.
  Apply this decorator to the test case class, and then
  decorate test methods with ``@data``.
  For each method decorated with ``@data``, this will effectively create as
  many methods as data items are passed as parameters to ``@data``.
  The names of the test methods follow the pattern
  ``original_test_name_{ordinal}_{data}``. ``ordinal`` is the position of the
  data argument, starting with 1.
  For data we use a string representation of the data value converted into a
  valid python identifier. If ``data.__name__`` exists, we use that instead.
  For each method decorated with ``@file_data('test_data.json')``, the
  decorator will try to load the test_data.json file located relative
  to the python file containing the method that is decorated. It will,
  for each ``test_name`` key create as many methods in the list of values
  from the ``data`` key.
  """
  for name, func in list(cls.__dict__.items()):
    if hasattr(func, DATA_ATTR):
      for i, v in enumerate(getattr(func, DATA_ATTR)):
        test_name = mk_test_name(name, getattr(v, "__name__", v), i)
        test_data_docstring = _get_test_data_docstring(func, v)
        if hasattr(func, UNPACK_ATTR):
          if isinstance(v, tuple) or isinstance(v, list):
            add_test(
              cls,
              test_name,
              test_data_docstring,
              func,
              *v
            )
          else:
            # unpack dictionary
            add_test(
              cls,
              test_name,
              test_data_docstring,
              func,
              **v
            )
        else:
          add_test(cls, test_name, test_data_docstring, func, v)
      delattr(cls, name)
    elif hasattr(func, FILE_ATTR):
      file_attr = getattr(func, FILE_ATTR)
      process_file_data(cls, name, func, file_attr)
      delattr(cls, name)
  return cls

ddt源碼

通過源碼的說明,基本可以了解個(gè)大概了,其核心用法就是利用裝飾器來實(shí)現(xiàn)功能的復(fù)用及擴(kuò)展延續(xù),以此來實(shí)現(xiàn)數(shù)據(jù)驅(qū)動(dòng),現(xiàn)在簡單介紹下其主要函數(shù)的基本使用場景。

1. @ddt(cls) ,其服務(wù)于unittest類裝飾器,主要功能是判斷該類中是否具有相應(yīng) ddt 裝飾的方法,如有則利用自省機(jī)制,實(shí)現(xiàn)測試用例命名 mk_test_name、 數(shù)據(jù)回填 _add_tests_from_data 并通過 add_test 添加至unittest的容器TestSuite中去,然后執(zhí)行得到testResult,流程非常清晰。

def ddt(cls):
  for name, func in list(cls.__dict__.items()):
    if hasattr(func, DATA_ATTR):
      for i, v in enumerate(getattr(func, DATA_ATTR)):
        test_name = mk_test_name(name, getattr(v, "__name__", v), i)
        test_data_docstring = _get_test_data_docstring(func, v)
        if hasattr(func, UNPACK_ATTR):
          if isinstance(v, tuple) or isinstance(v, list):
            add_test(
              cls,
              test_name,
              test_data_docstring,
              func,
              *v
            )
          else:
            # unpack dictionary
            add_test(
              cls,
              test_name,
              test_data_docstring,
              func,
              **v
            )
        else:
          add_test(cls, test_name, test_data_docstring, func, v)
      delattr(cls, name)
    elif hasattr(func, FILE_ATTR):
      file_attr = getattr(func, FILE_ATTR)
      process_file_data(cls, name, func, file_attr)
      delattr(cls, name)
  return cls

2. @file_data(PATH) ,其主要是通過 process_file_data 方法實(shí)現(xiàn)數(shù)據(jù)解析,這里通過 _add_tests_from_data 實(shí)現(xiàn)測試數(shù)據(jù)回填,通過源碼可以得知目前文件只支持 Yaml 和 JSON 數(shù)據(jù)文件,想擴(kuò)展其它文件比如 xml 等直接改源碼就行

def process_file_data(cls, name, func, file_attr):
  """
  Process the parameter in the `file_data` decorator.
  """
  cls_path = os.path.abspath(inspect.getsourcefile(cls))
  data_file_path = os.path.join(os.path.dirname(cls_path), file_attr)
  def create_error_func(message): # pylint: disable-msg=W0613
    def func(*args):
      raise ValueError(message % file_attr)
    return func
  # If file does not exist, provide an error function instead
  if not os.path.exists(data_file_path):
    test_name = mk_test_name(name, "error")
    test_docstring = """Error!"""
    add_test(cls, test_name, test_docstring,
         create_error_func("%s does not exist"), None)
    return
  _is_yaml_file = data_file_path.endswith((".yml", ".yaml"))
  # Don't have YAML but want to use YAML file.
  if _is_yaml_file and not _have_yaml:
    test_name = mk_test_name(name, "error")
    test_docstring = """Error!"""
    add_test(
      cls,
      test_name,
      test_docstring,
      create_error_func("%s is a YAML file, please install PyYAML"),
      None
    )
    return
  with codecs.open(data_file_path, 'r', 'utf-8') as f:
    # Load the data from YAML or JSON
    if _is_yaml_file:
      data = yaml.safe_load(f)
    else:
      data = json.load(f)
  _add_tests_from_data(cls, name, func, data)

3. @date(* value ),簡單粗暴的直觀實(shí)現(xiàn)數(shù)據(jù)驅(qū)動(dòng),直接將可迭代對(duì)象傳參,進(jìn)行數(shù)據(jù)傳遞,數(shù)據(jù)之間用逗號(hào)“ , ”隔離,代表一組數(shù)據(jù),此時(shí)如果實(shí)現(xiàn) unpack, 則更加細(xì)化的實(shí)現(xiàn)數(shù)據(jù)驅(qū)動(dòng),切記每組數(shù)據(jù)對(duì)應(yīng)相應(yīng)的形參。

def unpack(func):
  """
  Method decorator to add unpack feature.
  """
  setattr(func, UNPACK_ATTR, True)
  return func
def data(*values):
  """
  Method decorator to add to your test methods.
  Should be added to methods of instances of ``unittest.TestCase``.
  """
  global index_len
  index_len = len(str(len(values)))
  return idata(values)
def idata(iterable):
  """
  Method decorator to add to your test methods.
  Should be added to methods of instances of ``unittest.TestCase``.
  """
  def wrapper(func):
    setattr(func, DATA_ATTR, iterable)
    return func
  return wrapper

4. 實(shí)例

# -*- coding: utf-8 -*-
__author__ = '暮辭'
import time,random
from ddt import ddt, data, file_data, unpack
import unittest
import json
from HTMLTestRunner import HTMLTestRunner
@ddt
class Demo(unittest.TestCase):
  @file_data("./migrations/test.json")
  def test_hello(self, a, **b):
    '''
    測試hello
    '''
    print a
    print b
    #print "hello", a, type(a)
    if isinstance(a, list):
      self.assertTrue(True, "2")
    else:
      self.assertTrue(True, "3")
  @data([1, 2, 3, 4])
  def test_world(self, *b):
    '''
    測試world
    '''
    print b
    self.assertTrue(True)
  @data({"test1":[1, 2], "test2":[3, 4]}, {"test1":[1, 2],"test2":[3, 4]})
  @unpack
  def test_unpack(self, **a):
    '''
    測試unpack
    '''
    print a
    self.assertTrue(True)
if __name__ == "__main__":
  suit = unittest.TestSuite()
  test = unittest.TestLoader().loadTestsFromTestCase(Demo)
  suit.addTests(test)
  #suit.addTests(test)
  with open("./migrations/Demo.html", "w") as f:
    result = HTMLTestRunner(stream=f, description=u"Demo測試報(bào)告", title=u"Demo測試報(bào)告")
    result.run(suit)

測試結(jié)果:

至此關(guān)于ddt的數(shù)據(jù)驅(qū)動(dòng)暫時(shí)告一段落了,后面還會(huì)介紹基于excel、sql等相關(guān)的數(shù)據(jù)驅(qū)動(dòng)內(nèi)容,并進(jìn)行對(duì)比總結(jié),拭目以待~

總結(jié)

以上所述是小編給大家介紹的python自動(dòng)化測試之DDT數(shù)據(jù)驅(qū)動(dòng)的實(shí)現(xiàn)代碼,希望對(duì)大家有所幫助,如果大家有任何疑問請給我留言,小編會(huì)及時(shí)回復(fù)大家的。在此也非常感謝大家對(duì)腳本之家網(wǎng)站的支持!
如果你覺得本文對(duì)你有幫助,歡迎轉(zhuǎn)載,煩請注明出處,謝謝!

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