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Description

Nose extends unittest to make testing easier. Nose has been in maintenance mode for the past several years. New projects should consider using Nose2 or Pytest.

Support and updates

  • Nose has 1350 stars, 385 forks.
  • It has 0 major releases in the past 6 months.
  • It has 0 commits and there are 52 open pull requests.
  • It has 451 open issues and 462 have been closed.

Code statistics

  • Nose has 68 methods.

Blogs

Check out the latest blogs from LambdaTest on this topic:

Decide as a team we must… helping a team resistant to change

Change is a difficult subject and it’s one that many struggle to implement. There is no shame in admitting that I have had times where change has been accepted by the whole team and other times it has not been accepted.

CircleCI Vs. GitLab: Choosing The Right CI/CD Tool

He is a gifted driver. Famed for speed, reverse J, and drifts. He can breeze through the Moscow and Mexico traffic without sweating a drop. Of course, no one gets cracking on Bengaluru roads ???? But despite being so adept behind the wheels, he sometimes fails to champ the street races. Screeching tyres buzz in his head doesn’t let him sleep at times. I wish to tell him it’s not always about the driver, sometimes it’s the engine. That’s what happens when the right dev talent uses wrong, inefficient, incompatible CI/CD tools. The DevOps technologies you chose can abruptly break or smoothly accelerate your software development cycle. This article explores the Ford & the Ferrari of the CI/CD world in detail, CircleCI vs. GitLab, to help you pick the right one.

Eradicating Memory Leaks In Javascript

If you are wondering why your Javascript application might be suffering from severe slowdowns, poor performance, high latency or frequent crashes and all your painstaking attempts to figure out the problem were to no avail, there is a pretty good chance that your code is plagued by ‘Memory Leaks’. Memory leaks are fairly common as memory management is often neglected by developers due to the misconceptions about automatic memory allocation and release in modern high level programming languages like javascript. Failure to deal with javascript memory leaks can wreak havoc on your app’s performance and can render it unusable. The Internet is flooded with never-ending complex jargon which is often difficult to wrap your head around. So in this article, we will take a comprehensive approach to understand what javascript memory leaks are, its causes and how to spot and diagnose them easily using chrome developer tools.

Easily Execute Python UnitTest Parallel Testing In Selenium

This article is a part of our Content Hub. For more in-depth resources, check out our content hub on Selenium Python Tutorial.

LambdaTest Now Live With An Online Selenium Grid For Automated Cross Browser Testing

It has been around a year since we went live with the first iteration of LambdaTest Platform. We started off our product offering manual cross browser testing solutions and kept expanding our platform. We were asked many feature requests, and we implemented quite a lot of them. However, the biggest demand was to bring automation testing to the platform. Today we deliver on this feature.

Automation Testing Tutorials

Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.

LambdaTest Learning Hubs:

YouTube

You could also refer to video tutorials over LambdaTest YouTube channel to get step by step demonstration from industry experts.

License

Nose is lincensed under the

LambdaTest Community Discussions

Questions
Discussion

What are the best practices for managing test variables in `pytest` compared to `unittest`?

Can Python be used for UI automation?

Which is best Python or testing?

What is testing framework in Python?

Selenium Python 101 Certification | LambdaTest Certifications | Automation Testing

What are the best practices for managing test variables in pytest compared to unittest?

In unittest, I can set up variables in a class, and then the methods of this class can choose whichever variable they want to use. Here’s an example:

class TestClass(unittest.TestCase):
    def setUp(self):        
        self.varA = 1
        self.varB = 2
        self.varC = 3
        self.modified_varA = 2

    def test_1(self):
        do_something_with(self.varA, self.varB)

    def test_2(self):
        do_something_with(self.modified_varA, self.varC)

With unittest, it’s easy to group tests together in one class and use various variables across different methods. In pytest, however, I’m using fixtures in conftest.py instead of a class, like this:

@pytest.fixture(scope="module")
def input1():
    varA = 1
    varB = 2
    return varA, varB

@pytest.fixture(scope="module")
def input2():
    varA = 2
    varC = 3
    return varA, varC

I then feed these fixtures to my functions in a different file (let’s say test_this.py) for different test functions. But, I’m running into a couple of challenges:

  1. Since I can’t just declare local variables in conftest.py and directly import them, is there a better way to declare different variables in conftest.py that can be used in multiple functions in test_this.py? I have five different configurations for these variables, and defining that many different fixtures sounds cumbersome. I would rather go back to the unittest class structure, where I can define my variables and pick and choose what I want.

  2. Should I declare global variables in test_this.py and use them across functions? That doesn’t seem very Pythonic since these variables are only used in that file.

  3. Let’s say I have multiple test files, such as test_that.py and test_them.py. If I have shared variables between these files, how should I manage them? Should I create a file like variables.py in the same directory and import it where needed? This way, I can keep all data separate from the test logic.

  4. Does pytest discourage organizing tests using classes? Every example I read online uses a bunch of functions with fixtures only. What is the recommended way to define a class and methods for organizing tests in pytest?

  5. In a scenario where I need to use the result of one function in another, how would I do that in pytest? Since I have an assert statement at the end of a function instead of a return, I won’t be able to use this function as a fixture. How can I accomplish this? I understand that it’s not ideal for one test to rely on another, but is there a workaround?

In summary, what are the differences between pytest vs unittest in terms of managing variables and organizing tests? How can I effectively use the fixtures in pytest while keeping things flexible like I did with the unittest class structure?

https://community.lambdatest.com/t/35730

In pytest, you can declare fixtures not only in conftest.py, but also in any Python module you wish, and then simply import that module wherever needed. Additionally, you can use fixtures similarly to how you use the setUp method in unittest.

Here’s an example:

@pytest.fixture(scope='class')
def input(request):
    request.cls.varA = 1
    request.cls.varB = 2
    request.cls.varC = 3
    request.cls.modified_varA = 2

@pytest.usefixtures('input')
class TestClass:
    def test_1(self):
        do_something_with(self.varA, self.varB)

    def test_2(self):
        do_something_with(self.modified_varA, self.varC)

Alternatively, you can define separate fixtures for each variable:

@pytest.fixture()
def fixture_a():
    return 1  # varA

@pytest.fixture()
def fixture_b():
    return 2  # varB

@pytest.fixture()
def fixture_c():
    return 3  # varC

@pytest.fixture()
def fixture_mod_A():
    return 2  # modified_varA

Or, you can create one fixture that returns all the variables together:

@pytest.fixture()
def all_variables():
    return {'varA': 1, 'varB': 2, 'varC': 3, 'modified_varA': 2}

If you have many configurations, you can even use an indirect parametrized fixture to return variables by your choice, though this can be a bit more complex:

@pytest.fixture()
def parametrized_input(request):
   vars = {'varA': 1, 'varB': 2, 'varC': 3}
   var_names = request.param
   return (vars[var_name] for var_name in var_names)

@pytest.mark.parametrize('parametrized_input', [('varA', 'varC')], indirect=True)
def test_1(parametrized_input):
    varA, varC = parametrized_input
    # your test logic here

Alternatively, you could create a fixture factory that generates fixtures dynamically. While this is an overkill for a small number of tests, it can be useful if you have many test cases with different variable configurations.

If you want to manage shared variables across multiple test files, you can create a separate variables.py file and import them in the test files that need them.

However, I recommend not directly importing this file into your test files but instead using a command-line option to specify which file to import. This approach allows you to choose different variable files without modifying your code, keeping your test configuration flexible.

Regarding the organization of tests, it’s important to note that pytest doesn’t discourage using classes for organizing tests.

You can still use classes in pytest, especially if you’re migrating from other frameworks like unittest or nose. I use classes in my tests because I migrated from nosetest, and I haven’t encountered any issues with using classes in pytest.

In situations where you need to use the result of one function in another, you can follow this approach:

def some_actions(a, b):
    # perform actions here
    result = a + b  # just an example action
    return result

def test():
    result = some_actions(1, 2)
    assert result == 3
@pytest.fixture()
def some_fixture():
    return some_actions(1, 2)

In this solution, the logic of the function is reusable as both a fixture and in individual tests. This avoids directly relying on another test, which is generally not a good practice.

https://community.lambdatest.com/t/35730

Use Fixture for Shared Setup Between Tests: Instead of writing separate functions to handle logic, you could leverage a fixture that sets up shared variables and can be used across multiple tests in different modules.

This allows for cleaner and more manageable tests without code duplication.

https://community.lambdatest.com/t/35730

Modularize Variables Across Files: As an alternative, you can organize your test setup into several files to ensure that shared variables and configurations are easily imported and used across multiple test cases in different test files.

This is particularly useful for large projects with many tests that need shared configurations. You can manage the variables in a centralized location like variables.py and use them via fixtures in your test files.

https://community.lambdatest.com/t/35730

Test case code snippets

API Testing - Check multi-word search

Description:

Verify that the API returns the correct results when searching for a string with multiple words.

Database testing - Check database logs

Description:

For every database add/update operation logs should be added.

API Testing - Check CORS preflight

Description:

Verify that the API correctly handles CORS preflight requests and returns the correct HTTP status code and error message.

Shopify webpage testing - Test site's login and registration feature

Description:

Ensure that it works properly and that user data is collected and stored securely. This test case ensures that the login and registration features are operational and that user data is collected and stored securely in order to provide a secure user experience.

Downloads

Nose can be downloaded from it’s GitHub repository - https://github.com/nose-devs/nose

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