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python自動化測試之DDT數據驅動的實現代碼

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時隔已久,再次冒煙,自動化測試工作仍在繼續,自動化測試中的數據驅動技術尤為重要,不然咋去實現數據分離呢,對吧,這里就簡單介紹下與傳統unittest自動化測試框架匹配的DDT數據驅動技術。

話不多說,先擼一波源碼,其實整體代碼并不多

            
# -*- 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,基本可以了解個大概了,其核心用法就是利用裝飾器來實現功能的復用及擴展延續,以此來實現數據驅動,現在簡單介紹下其主要函數的基本使用場景。

1. @ddt(cls) ,其服務于unittest類裝飾器,主要功能是判斷該類中是否具有相應 ddt 裝飾的方法,如有則利用自省機制,實現測試用例命名 mk_test_name 、 數據回填 _add_tests_from_data 并通過 add_test 添加至unittest的容器TestSuite中去,然后執行得到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 方法實現數據解析,這里通過 _add_tests_from_data 實現測試數據回填,通過源碼可以得知目前文件只支持 Yaml 和 JSON 數據文件,想擴展其它文件比如 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 ),簡單粗暴的直觀實現數據驅動,直接將可迭代對象傳參,進行數據傳遞,數據之間用逗號“ , ”隔離,代表一組數據,此時如果實現 unpack, 則更加細化的實現數據驅動,切記每組數據對應相應的形參。

            
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測試報告", title=u"Demo測試報告")
    result.run(suit)
          

測試結果:

python自動化測試之DDT數據驅動的實現代碼_第1張圖片

至此關于ddt的數據驅動暫時告一段落了,后面還會介紹基于excel、sql等相關的數據驅動內容,并進行對比總結,拭目以待~

總結

以上所述是小編給大家介紹的python自動化測試之DDT數據驅動的實現代碼,希望對大家有所幫助,如果大家有任何疑問請給我留言,小編會及時回復大家的。在此也非常感謝大家對腳本之家網站的支持!
如果你覺得本文對你有幫助,歡迎轉載,煩請注明出處,謝謝!


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