unittest data-driven ddt

 

Brief introduction

ddt (data driven test) data-driven test: set by the external data driven test case, a method for testing the same, but the situation requires a large amount of change in the test data, test data and the purpose is to separate the steps of

Since there is no data-driven unittest module, the main use of this library ddt, following installation

pip install ddt

ddt ddt comprising decorator three classes of common methods and decorators data (direct input test data), file_data (test data may be obtained from the json or yaml), unpack (decomposed data)

 

use

1. Several individual time data

Import unittest
 Import ddt 

@ ddt.ddt                           # Before testing the use of the class definition: @ ddt.ddt 
class Mytest (unittest.TestCase): 
    
    @ ddt.data ( 1,2,3,4)        # use before the test case definition: @ ddt.data (test data) separated by commas test data 
    DEF TEST_1 (Self, A):
         Print (A) 

IF  the __name__ == " __main__ " : 
    unittest.main ()

result

1
.2
.3
.4
.
----------------------------------------------------------------------
Ran 4 tests in 0.013s

OK

 

2. When the data set is a list, split into individual elements

Import the unittest
 Import DDT 

value = [(1,2), (3,4-), (5,6 )] 

@ ddt.ddt                           # used in the test before the class definition: @ ddt.ddt 
class the Mytest (of unittest.TestCase): 
    
    @ ddt.data ( * value)        # before the test case definitions used: @ ddt.data (test data), the parameter * python is decomposed into a list element in turn passed 
    DEF TEST_1 (Self, a):
         Print (A) 

IF  __name__ == " __main__ " : 
    unittest.main ()

result

(1, 2)
.(3, 4)
.(5, 6)
.
----------------------------------------------------------------------
Ran 3 tests in 0.004s

OK

If you want the list above which tuples decomposed into individual elements, used unpack

Import the unittest
 Import DDT 

value = [(1,2), (3,4-), (5,6 )] 

@ ddt.ddt                           # used in the test before the class definition: @ ddt.ddt 
class the Mytest (of unittest.TestCase): 
    
    @ ddt.data ( * value)        # before using the test case definition: @ ddt.data (test data), parameter * python is decomposed into a list of elements sequentially passed 
    @ ddt.unpack      # take separate value [1], is decomposed into 1,2 incoming 
    DEF TEST_1 (Self, A, B):
         Print (A, B) 

IF  the __name__ == " __main__ " : 
    unittest.main ()

result

1 2
.3 4
.5 6
.
----------------------------------------------------------------------
Ran 3 tests in 0.010s

OK

 

3. The data dictionary is a set time

import unittest
import ddt

value = {"a":1,"b":2}

@ddt.ddt
class Mytest(unittest.TestCase):

    @ddt.data(value)
    @ddt.unpack
    def test_1(self,a,b):
        print(a,b)

if __name__ == "__main__":
    unittest.main()

result

1 2
.
----------------------------------------------------------------------
Ran 1 test in 0.004s

OK

 

4. Use json file or yaml

json file

{
    "test1":1,
    "test2":"abc",
    "test3":[1,2,3]
}

Code

import unittest
import ddt


@ddt.ddt
class Mytest(unittest.TestCase):

    @ddt.file_data("tmp.json")
    def test_1(self,a):
        print(a)

if __name__ == "__main__":
    unittest.main()

result

1
.abc
.[1, 2, 3]
.
----------------------------------------------------------------------
Ran 3 tests in 0.014s

OK