How can I be sure of the unittest methods' order? Is the alphabetical or numeric prefixes the proper way?
class TestFoo(TestCase):
def test_1(self):
...
def test_2(self):
...
or
class TestFoo(TestCase):
def test_a(self):
...
def test_b(self):
...
You can disable it by setting sortTestMethodsUsing to None:
import unittest
unittest.TestLoader.sortTestMethodsUsing = None
For pure unit tests, you folks are right; but for component tests and integration tests...
I do not agree that you shall assume nothing about the state.
What if you are testing the state?
For example, your test validates that a service is auto-started upon installation. If in your setup, you start the service, then do the assertion, and then you are no longer testing the state, but you are testing the "service start" functionality.
Another example is when your setup takes a long time or requires a lot of space and it just becomes impractical to run the setup frequently.
Many developers tend to use "unit test" frameworks for component testing...so stop and ask yourself, am I doing unit testing or component testing?
There is no reason given that you can't build on what was done in a previous test or should rebuild it all from scratch for the next test. At least no reason is usually offered but instead people just confidently say "you shouldn't". That isn't helpful.
In general I am tired of reading too many answers here that say basically "you shouldn't do that" instead of giving any information on how to best do it if in the questioners judgment there is good reason to do so. If I wanted someone's opinion on whether I should do something then I would have asked for opinions on whether doing it is a good idea.
That out of the way, if you read say loadTestsFromTestCase and what it calls it ultimately scans for methods with some name pattern in whatever order they are encountered in the classes method dictionary, so basically in key order. It take this information and makes a testsuite of mapping it to the TestCase class. Giving it instead a list ordered as you would like is one way to do this. I am not so sure of the most efficient/cleanest way to do it but this does work.
If you use 'nose' and you write your test cases as functions (and not as methods of some TestCase derived class), 'nose' doesn't fiddle with the order, but uses the order of the functions as defined in the file.
In order to have the assert_* methods handy without needing to subclass TestCase I usually use the testing module from NumPy. Example:
from numpy.testing import *
def test_aaa():
assert_equal(1, 1)
def test_zzz():
assert_equal(1, 1)
def test_bbb():
assert_equal(1, 1)
Running that with ''nosetest -vv'' gives:
test_it.test_aaa ... ok
test_it.test_zzz ... ok
test_it.test_bbb ... ok
----------------------------------------------------------------------
Ran 3 tests in 0.050s
OK
Note to all those who contend that unit tests shouldn't be ordered: while it is true that unit tests should be isolated and can run independently, your functions and classes are usually not independent.
They rather build up on another from simpler/low-level functions to more complex/high-level functions. When you start optimising your low-level functions and mess up (for my part, I do that frequently; if you don't, you probably don't need unit test anyway;-) then it's a lot better for diagnosing the cause, when the tests for simple functions come first, and tests for functions that depend on those functions later.
If the tests are sorted alphabetically the real cause usually gets drowned among one hundred failed assertions, which are not there because the function under test has a bug, but because the low-level function it relies on has.
That's why I want to have my unit tests sorted the way I specified them: not to use state that was built up in early tests in later tests, but as a very helpful tool in diagnosing problems.
I half agree with the idea that tests shouldn't be ordered. In some cases it helps (it's easier, damn it!) to have them in order... after all, that's the reason for the 'unit' in UnitTest.
That said, one alternative is to use mock objects to mock out and patch the items that should run before that specific code under test. You can also put a dummy function in there to monkey patch your code. For more information, check out Mock, which is part of the standard library now.
Here are some YouTube videos if you haven't used Mock before.
Video 1
Video 2
Video 3
More to the point, try using class methods to structure your code, and then place all the class methods in one main test method.
import unittest
import sqlite3
class MyOrderedTest(unittest.TestCase):
#classmethod
def setUpClass(cls):
cls.create_db()
cls.setup_draft()
cls.draft_one()
cls.draft_two()
cls.draft_three()
#classmethod
def create_db(cls):
cls.conn = sqlite3.connect(":memory:")
#classmethod
def setup_draft(cls):
cls.conn.execute("CREATE TABLE players ('draftid' INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL, 'first', 'last')")
#classmethod
def draft_one(cls):
player = ("Hakeem", "Olajuwon")
cls.conn.execute("INSERT INTO players (first, last) VALUES (?, ?)", player)
#classmethod
def draft_two(cls):
player = ("Sam", "Bowie")
cls.conn.execute("INSERT INTO players (first, last) VALUES (?, ?)", player)
#classmethod
def draft_three(cls):
player = ("Michael", "Jordan")
cls.conn.execute("INSERT INTO players (first, last) VALUES (?, ?)", player)
def test_unordered_one(self):
cur = self.conn.execute("SELECT * from players")
draft = [(1, u'Hakeem', u'Olajuwon'), (2, u'Sam', u'Bowie'), (3, u'Michael', u'Jordan')]
query = cur.fetchall()
print query
self.assertListEqual(query, draft)
def test_unordered_two(self):
cur = self.conn.execute("SELECT first, last FROM players WHERE draftid=3")
result = cur.fetchone()
third = " ".join(result)
print third
self.assertEqual(third, "Michael Jordan")
Why do you need a specific test order? The tests should be isolated and therefore it should be possible to run them in any order, or even in parallel.
If you need to test something like user unsubscribing, the test could create a fresh database with a test subscription and then try to unsubscribe. This scenario has its own problems, but in the end it’s better than having tests depend on each other. (Note that you can factor out common test code, so that you don’t have to repeat the database setup code or create testing data ad nauseam.)
There are a number of reasons for prioritizing tests, not the least of which is productivity, which is what JUnit Max is geared for. It's sometimes helpful to keep very slow tests in their own module so that you can get quick feedback from the those tests that that don't suffer from the same heavy dependencies. Ordering is also helpful in tracking down failures from tests that are not completely self-contained.
Don't rely on the order. If they use some common state, like the filesystem or database, then you should create setUp and tearDown methods that get your environment into a testable state, and then clean up after the tests have run.
Each test should assume that the environment is as defined in setUp, and should make no further assumptions.
You should try the proboscis library. It will allow you to make tests order as well as set up any test dependencies. I use it and this library is truly awesome.
For example, if test case #1 from module A should depend on test case #3 from module B you CAN set this behaviour using the library.
Here is a simpler method that has the following advantages:
No need to create a custom TestCase class.
No need to decorate every test method.
Use the unittest standard load test protocol. See the Python docs here.
The idea is to go through all the test cases of the test suites given to the test loader protocol and create a new suite but with the tests ordered by their line number.
Here is the code:
import unittest
def load_ordered_tests(loader, standard_tests, pattern):
"""
Test loader that keeps the tests in the order they were declared in the class.
"""
ordered_cases = []
for test_suite in standard_tests:
ordered = []
for test_case in test_suite:
test_case_type = type(test_case)
method_name = test_case._testMethodName
testMethod = getattr(test_case, method_name)
line = testMethod.__code__.co_firstlineno
ordered.append( (line, test_case_type, method_name) )
ordered.sort()
for line, case_type, name in ordered:
ordered_cases.append(case_type(name))
return unittest.TestSuite(ordered_cases)
You can put this in a module named order_tests and then in each unittest Python file, declare the test loader like this:
from order_tests import load_ordered_tests
# This orders the tests to be run in the order they were declared.
# It uses the unittest load_tests protocol.
load_tests = load_ordered_tests
Note: the often suggested technique of setting the test sorter to None no longer works because Python now sorts the output of dir() and unittest uses dir() to find tests. So even though you have no sorting method, they still get sorted by Python itself!
From unittest — Unit testing framework
Note that the order in which the various test cases will be run is determined by sorting the test function names with respect to the built-in ordering for strings.
If you need set the order explicitly, use a monolithic test.
class Monolithic(TestCase):
def step1(self):
...
def step2(self):
...
def steps(self):
for name in sorted(dir(self)):
if name.startswith("step"):
yield name, getattr(self, name)
def test_steps(self):
for name, step in self.steps():
try:
step()
except Exception as e:
self.fail("{} failed ({}: {})".format(step, type(e), e)
Check out this Stack Overflow question for details.
There are scenarios where the order can be important and where setUp and Teardown come in as too limited. There's only one setUp and tearDown method, which is logical, but you can only put so much information in them until it gets unclear what setUp or tearDown might actually be doing.
Take this integration test as an example:
You are writing tests to see if the registration form and the login form are working correctly. In such a case the order is important, as you can't login without an existing account.
More importantly the order of your tests represents some kind of user interaction. Where each test might represent a step in the whole process or flow you're testing.
Dividing your code in those logical pieces has several advantages.
It might not be the best solution, but I often use one method that kicks off the actual tests:
def test_registration_login_flow(self):
_test_registration_flow()
_test_login_flow()
A simple method for ordering "unittest" tests is to follow the init.d mechanism of giving them numeric names:
def test_00_createEmptyObject(self):
obj = MyObject()
self.assertIsEqual(obj.property1, 0)
self.assertIsEqual(obj.dict1, {})
def test_01_createObject(self):
obj = MyObject(property1="hello", dict1={"pizza":"pepperoni"})
self.assertIsEqual(obj.property1, "hello")
self.assertIsDictEqual(obj.dict1, {"pizza":"pepperoni"})
def test_10_reverseProperty(self):
obj = MyObject(property1="world")
obj.reverseProperty1()
self.assertIsEqual(obj.property1, "dlrow")
However, in such cases, you might want to consider structuring your tests differently so that you can build on previous construction cases. For instance, in the above, it might make sense to have a "construct and verify" function that constructs the object and validates its assignment of parameters.
def make_myobject(self, property1, dict1): # Must be specified by caller
obj = MyObject(property1=property1, dict1=dict1)
if property1:
self.assertEqual(obj.property1, property1)
else:
self.assertEqual(obj.property1, 0)
if dict1:
self.assertDictEqual(obj.dict1, dict1)
else:
self.assertEqual(obj.dict1, {})
return obj
def test_00_createEmptyObject(self):
obj = self.make_object(None, None)
def test_01_createObject(self):
obj = self.make_object("hello", {"pizza":"pepperoni"})
def test_10_reverseProperty(self):
obj = self.make_object("world", None)
obj.reverseProperty()
self.assertEqual(obj.property1, "dlrow")
I agree with the statement that a blanket "don't do that" answer is a bad response.
I have a similar situation where I have a single data source and one test will wipe the data set causing other tests to fail.
My solution was to use the operating system environment variables in my Bamboo server...
(1) The test for the "data purge" functionality starts with a while loop that checks the state of an environment variable "BLOCK_DATA_PURGE." If the "BLOCK_DATA_PURGE" variable is greater than zero, the loop will write a log entry to the effect that it is sleeping 1 second. Once the "BLOCK_DATA_PURGE" has a zero value, execution proceeds to test the purge functionality.
(2) Any unit test which needs the data in the table simply increments "BLOCK_DATA_PURGE" at the beginning (in setup()) and decrements the same variable in teardown().
The effect of this is to allow various data consumers to block the purge functionality so long as they need without fear that the purge could execute in between tests. Effectively the purge operation is pushed to the last step...or at least the last step that requires the original data set.
Today I am going to extend this to add more functionality to allow some tests to REQUIRE_DATA_PURGE. These will effectively invert the above process to ensure that those tests only execute after the data purge to test data restoration.
See the example of WidgetTestCase on Organizing test code. It says that
Class instances will now each run one of the test_*() methods, with self.widget created and destroyed separately for each instance.
So it might be of no use to specify the order of test cases, if you do not access global variables.
I have implemented a plugin, nosedep, for Nose which adds support for test dependencies and test prioritization.
As mentioned in the other answers/comments, this is often a bad idea, however there can be exceptions where you would want to do this (in my case it was performance for integration tests - with a huge overhead for getting into a testable state, minutes vs. hours).
A minimal example is:
def test_a:
pass
#depends(before=test_a)
def test_b:
pass
To ensure that test_b is always run before test_a.
The philosophy behind unit tests is to make them independent of each other. This means that the first step of each test will always be to try to rethink how you are testing each piece to match that philosophy. This can involve changing how you approach testing and being creative by narrowing your tests to smaller scopes.
However, if you still find that you need tests in a specific order (as that is viable), you could try checking out the answer to Python unittest.TestCase execution order .
It seems they are executed in alphabetical order by test name (using the comparison function between strings).
Since tests in a module are also only executed if they begin with "test", I put in a number to order the tests:
class LoginTest(unittest.TestCase):
def setUp(self):
driver.get("http://localhost:2200")
def tearDown(self):
# self.driver.close()
pass
def test1_check_at_right_page(self):
...
assert "Valor" in driver.page_source
def test2_login_a_manager(self):
...
submit_button.click()
assert "Home" in driver.title
def test3_is_manager(self):
...
Note that numbers are not necessarily alphabetical - "9" > "10" in the Python shell is True for instance. Consider using decimal strings with fixed 0 padding (this will avoid the aforementioned problem) such as "000", "001", ... "010"... "099", "100", ... "999".
Contrary to what was said here:
tests have to run in isolation (order must not matter for that)
and
ordering them is important because they describe what the system do and how the developer implements it.
In other words, each test brings you information of the system and the developer logic.
So if this information is not ordered it can make your code difficult to understand.
To randomise the order of test methods you can monkey patch the unittest.TestLoader.sortTestMethodsUsing attribute
if __name__ == '__main__':
import random
unittest.TestLoader.sortTestMethodsUsing = lambda self, a, b: random.choice([1, 0, -1])
unittest.main()
The same approach can be used to enforce whatever order you need.
I am adding some tests to existing not so test friendly code, as title suggest, I need to test if the complex method actually calls another method, eg.
class SomeView(...):
def verify_permission(self, ...):
# some logic to verify permission
...
def get(self, ...):
# some codes here I am not interested in this test case
...
if some condition:
self.verify_permission(...)
# some other codes here I am not interested in this test case
...
I need to write some test cases to verify self.verify_permission is called when condition is met.
Do I need to mock all the way to the point of where self.verify_permission is executed? Or I need to refactor the def get() function to abstract out the code to become more test friendly?
There are a number of points made in the comments that I strongly disagree with, but to your actual question first.
This is a very common scenario. The suggested approach with the standard library's unittest package is to utilize the Mock.assert_called... methods.
I added some fake logic to your example code, just so that we can actually test it.
code.py
class SomeView:
def verify_permission(self, arg: str) -> None:
# some logic to verify permission
print(self, f"verify_permission({arg=}=")
def get(self, arg: int) -> int:
# some codes here I am not interested in this test case
...
some_condition = True if arg % 2 == 0 else False
...
if some_condition:
self.verify_permission(str(arg))
# some other codes here I am not interested in this test case
...
return arg * 2
test.py
from unittest import TestCase
from unittest.mock import MagicMock, patch
from . import code
class SomeViewTestCase(TestCase):
def test_verify_permission(self) -> None:
...
#patch.object(code.SomeView, "verify_permission")
def test_get(self, mock_verify_permission: MagicMock) -> None:
obj = code.SomeView()
# Odd `arg`:
arg, expected_output = 3, 6
output = obj.get(arg)
self.assertEqual(expected_output, output)
mock_verify_permission.assert_not_called()
# Even `arg`:
arg, expected_output = 2, 4
output = obj.get(arg)
self.assertEqual(expected_output, output)
mock_verify_permission.assert_called_once_with(str(arg))
You use a patch variant as a decorator to inject a MagicMock instance to replace the actual verify_permission method for the duration of the entire test method. In this example that method has no return value, just a side effect (the print). Thus, we just need to check if it was called under the correct conditions.
In the example, the condition depends directly on the arg passed to get, but this will obviously be different in your actual use case. But this can always be adapted. Since the fake example of get has exactly two branches, the test method calls it twice to traverse both of them.
When doing unit tests, you should always isolate the unit (i.e. function) under testing from all your other functions. That means, if your get method calls other methods of SomeView or any other functions you wrote yourself, those should be mocked out during test_get.
You want your test of get to be completely agnostic to the logic inside verify_permission or any other of your functions used inside get. Those are tested separately. You assume they work "as advertised" for the duration of test_get and by replacing them with Mock instances you control exactly how they behave in relation to get.
Note that the point about mocking out "network requests" and the like is completely unrelated. That is an entirely different but equally valid use of mocking.
Basically, you 1.) always mock your own functions and 2.) usually mock external/built-in functions with side effects (like e.g. network or disk I/O). That is it.
Also, writing tests for existing code absolutely has value. Of course it is better to write tests alongside your code. But sometimes you are just put in charge of maintaining a bunch of existing code that has no tests. If you want/can/are allowed to, you can refactor the existing code and write your tests in sync with that. But if not, it is still better to add tests retroactively than to have no tests at all for that code.
And if you write your unit tests properly, they still do their job, if you or someone else later decides to change something about the code. If the change breaks your tests, you'll notice.
As for the exception hack to interrupt the tested method early... Sure, if you want. It's lazy and calls into question the whole point of writing tests, but you do you.
No, seriously, that is a horrible approach. Why on earth would you test just part of a function? If you are already writing a test for it, you may as well cover it to the end. And if it is so complex that it has dozens of branches and/or calls 10 or 20 other custom functions, then yes, you should definitely refactor it.
I want to test my code that is based on the API created by someone else, but im not sure how should I do this.
I have created some function to save the json into file so I don't need to send requests each time I run test, but I don't know how to make it work in situation when the original (check) function takes an input arg (problem_report) which is an instance of some class provided by API and it has this
problem_report.get_correction(corr_link) method. I just wonder if this is a sign of bad written code by me, beacuse I can't write a test to this, or maybe I should rewrite this function in my tests file like I showed at the end of provided below code.
# I to want test this function
def check(problem_report):
corrections = {}
for corr_link, corr_id in problem_report.links.items():
if re.findall(pattern='detailCorrection', string=corr_link):
correction = problem_report.get_correction(corr_link)
corrections.update({corr_id: correction})
return corrections
# function serves to load json from file, normally it is downloaded by API from some page.
def load_pr(pr_id):
print('loading')
with open('{}{}_view_pr.json'.format(saved_prs_path, pr_id)) as view_pr:
view_pr = json.load(view_pr)
...
pr_info = {'view_pr': view_pr, ...}
return pr_info
# create an instance of class MyPR which takes json to __init__
#pytest.fixture
def setup_pr():
print('setup')
pr = load_pr('123')
my_pr = MyPR(pr['view_pr'])
return my_pr
# test function
def test_check(setup_pr):
pr = setup_pr
checked_pr = pr.check(setup_rft[1]['problem_report_pr'])
assert checker_pr
# rewritten check function in test file
#mock.patch('problem_report.get_correction', side_effect=get_corr)
def test_check(problem_report):
corrections = {}
for corr_link, corr_id in problem_report.links.items():
if re.findall(pattern='detailCorrection', string=corr_link):
correction = problem_report.get_correction(corr_link)
corrections.update({corr_id: correction})
return corrections
Im' not sure if I provided enough code and explanation to underastand the problem, but I hope so. I wish you could tell me if this is normal that some function are just hard to test, and if this is good practice to rewritte them separately so I can mock functions inside the tested function. I also was thinking that I could write new class with similar functionality but API is very large and it would be very long process.
I understand your question as follows: You have a function check that you consider hard to test because of its dependency on the problem_report. To make it better testable you have copied the code into the test file. You will test the copied code because you can modify this to be easier testable. And, you want to know if this approach makes sense.
The answer is no, this does not make sense. You are not testing the real function, but completely different code. Well, the code may not start being completely different, but in short time the copy and the original will deviate, and it will be a maintenance nightmare to ensure that the copy always resembles the original. Improving code for testability is a different story: You can make changes to the check function to improve its testability. But then, exactly the same resulting function should be used both in the test and the production code.
How to better test the function check then? First, are you sure that using the original problem_report objects really can not be sensibly used in your tests? (Here are some criteria that help you decide: What to mock for python test cases?). Now, lets assume that you come to the conclusion you can not sensibly use the original problem_report.
In that case, here the interface is simple enough to define a mocked problem_report. Keep in mind that Python uses duck typing, so you only have to create a class that has a links member which has an items() method. Plus, your mocked problem_report class needs a method get_correction(). Beyond that, your mock does not have to produce types that are similar to the types used by problem_report. The items() method can return simply a list of lists, like [["a",2],["xxxxdetailCorrectionxxxx",4]]. The same argument holds for get_correction, which could for example simply return its argument or a derived value, like, its negative.
For the above example (items() returning [["a",2],["xxxxdetailCorrectionxxxx",4]] and get_correction returning the negative of its argument) the expected result would be {4: -4}. No need to simulate real correction objects. And, you can create your mocked versions of problem_report without need to read data from files - the mocks can be setup completely from within the unit-testing code.
Try patching the problem_report symbol in the module. You should put your tests in a separate class.
#mock.patch('some.module.path.problem_report')
def test_check(problem_report):
problem_report.side_effect = get_corr
corrections = {}
for corr_link, corr_id in problem_report.links.items():
if re.findall(pattern='detailCorrection', string=corr_link):
correction = problem_report.get_correction(corr_link)
corrections.update({corr_id: correction})
return corrections
I am new to unit test.I want to write unit test for web scraper that I wrote.My scraper collects the data from website,which is on local disk where inputting different date gives different results
I have the following function in script.
get_date [returns date mentioned on web page]
get_product_and_cost [returns product mentioned and their cost]
I am not sure what to test in these functions.So far I have written this
class SimplisticTest(unittest.TestCase):
def setUp(self):
data = read_file("path to file")
self.soup = BeautifulSoup(data,'html5lib')
def test_date(self):
self.assertIsInstance(get_date(self.soup), str)
def test_date_length(self):
self.assertEqual(len(get_date(self.soup)),10)
if __name__ == '__main__':
unittest.main()
Usually, it is good to test a known output from a known input. In your case you test for the return type, but it would be even better to test if the returned object corresponds to what you would expect from the input, and that's where static test data (a test web page in your case) becomes useful. You can also test for exceptions with self.assertRaises(ExceptionType, method, args). Refer to https://docs.python.org/3.4/library/unittest.html if you haven't already.
Basically you want to test at least one explicit case (like the test page), the exceptions that can be raised like bad argument type (TypeError or ValueError) or a possible None return type depending on your function. Make sure not to test only for the type of the return or the amount of the return but explicitly for the data, such that if a change is made that breaks the feature, it is found (whereas a change could still return 10 elements, yet the elements might contain invalid data). I'd also suggest to have one test method for each method: get_date would have its test method test_get_date.
Keep in mind that what you want to find is if the method does its job, so test for extreme cases (big input data, as much as it can support or at least the method defines it can) and try to create them such that if the method outputs differently from what is expected based on its definition (documentation), the test fails and breaking changes are caught early on.
I'm trying to find a similar approach to Qunit's assertions in Python. When using assertions in Qunit, the message parameter is used in a very descriptive fashion.
test( "test", function() {
ok( fn([])==None, "Function should return 0 if no users" );
ok( fn(["Test User"])==1, "Function should return 1 is users supplied" );
});
Python's unittest module on the other hand, uses the message parameter is a somewhat more negative context. These are only shown when an assertion fails.
class TestSequenceFunctions(unittest.TestCase):
def test_choice(self):
seq = range(10)
element = random.choice(seq)
self.assertTrue(element in seq, msg="Element not found in sequence")
The end result of the Qunit is that there is much clearer transcript which could be compared against a spec document.
I realise that in Python, a similar approach would be achieved by perhaps say writing
def test_choice_ensure_element_exists_in_sequence(self):
It's not the same though. The output isn't presented in a nice way, and test lifecycle then performs setup and teardown for each label you want to use, which isn't necessarily what you want.
There might be a library out there which takes this approach, so perhaps this issue is already solved. Neither the python unittest library or pytest appear to work in this fashion though.
Your problem could be simply that don't know the unittest libary well enough yet. I find being able to write
self.assertIn('s', (1,3,4))
To be very short, expressive and readable.
And if you use the correct assertion method on the testcase then you rarely need to add your own message. assertIn has a perfectly reasonable output all by itself:
AssertionError: 's' not found in (1, 3, 4)
So rather than writing heaps of comments/message code. I rely on well named assertions combined with helpful default messages. If a well named assertion and helpful error message has not already been provided then I extend the test case and add my own.
self.assert_user_is_administrator(user)
Is very readable and will have a nice message if it fails that I provided in only one location.