benpresentation_django
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The document discusses nose-parameterized, a Python library that allows test cases to be dynamically generated from parameterized data. It provides an example of using nose-parameterized to test an add function with different input parameters. The document also lists some use cases, such as generating many tests with different parameters to write less code, and testing APIs by parameterizing different input and expected output JSON blobs. It concludes by providing the GitHub link for nose-parameterized and a note that the speaker's company is hiring full stack Django developers.
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![Example test with parameterized decorator
import unittest
from nose_parameterized import parameterized
class AddTestCase(unittest.TestCase):
@parameterized.expand([
("2 plus 3", 2, 3, 5),
("3 plus 5", 3, 5, 8),
])
def test_add(self, _, a, b, expected):
self.assertEqual(a + b, expected)](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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- Documenting functions in external files like .rst files.
- Running tests on modules, files, or via a unittest suite.
- Isolating test globals and contexts to avoid interference across tests.
How to fake_properly
Aiming at complete code coverage by unit tests tends to be cumbersome, especially for cases where external API calls a part of the code base. For these reasons, Python comes with the unittest.mock library, appearing to be a powerful companion in replacing parts of the system under test.
Auto testing!
This document discusses pytest, a Python testing tool. It provides 3 key benefits over unittest: 1) Detailed info on failing assertions without remembering names; 2) Automatic discovery of test modules and functions; 3) Modular fixtures for managing test resources. Fixtures provide reusable resources for tests and can be parameterized. The conftest.py file allows sharing fixtures across files. Plugins like pytest-django provide Django-specific fixtures. Mocking libraries like responses and aioresponses allow mocking HTTP requests in tests. Tox can run tests across multiple Python environments.
unittest in 5 minutes
This document discusses unit testing in Python. It provides an example of writing a test-driven function that interleaves two lists. The steps shown are:
1. Write test cases for the interleave function before writing the code. This tests various input/output cases.
2. Create a stub interleave function that returns None to make the tests fail as expected.
3. Write the interleave code using itertools.izip_longest to zip the lists together.
4. Rerun the tests to ensure they now pass with the implemented function.
Python testing using mock and pytest
This was demoed in November PyhtonPune meetup: https://deshmukhsuraj.wordpress.com/2015/11/30/november-2015-pythonpune-meetup-event-report/
Python testing
This document discusses various tools and techniques for testing Python code, including:
- Unittest and doctest modules for writing and running tests
- The mocker library for creating mock objects in tests
- Nose for finding and running tests across files
- Twill for testing web applications through a command line interface
- Code coverage tools like Coverage for analyzing test coverage
- Continuous integration tools like Buildbot for running tests automatically
5-minute intro to property-based testing in Python with hypothesis
Slides for 5-minute lightning talk at Pittsburgh Python meetup, 2013-06-26, about the Python module "hypothesis".
PowerShell_LangRef_v3 (1).pdf
This document provides a quick reference to useful commands, operators, and concepts in Windows PowerShell 3.0. It includes summaries of common commands like Get-Help and Get-Command, bitwise and logical operators, arrays, hash tables, variables, and more. The document acts as a cheat sheet for learning the basics of the PowerShell language.
Testing orm based code
It is always a pain to test code with DB/ORM-dependencies. So, here the rules to ease the process ...
Efficient computational modelling for integrated power plant design - Prof Er...
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Mock it right! A beginner’s guide to world of tests and mocks, Maciej PolańczykPôle Systematic Paris-Region
This document provides a beginner's guide to unit testing and mocking in Python. It discusses:
1) Using the Mock class to create basic mocks and set mock attributes and return values.
2) Using patch to mock imports when mocks cannot be passed directly. Patch is used to mock methods like requests.post.
3) Best practices for mocking like using spec or spec_set to define the mock's interface and make invalid attribute access raise AttributeError.
4) Common mock assertions like assert_called_once_with to validate mocks are used as expected.
5) Examples patching requests.post to mock network requests and control return values and exceptions.Test driven development_for_php
Training material I used to introduce Test Driven Development to PHP programmers for one of my clients.
Advanced Django
The document summarizes Simon Willison's presentation on advanced Django topics including unit testing, newforms, Ajax, and OpenID. Key points include:
- Unit testing in Django is encouraged through features like fixtures, doctests, test client, and email capture. Tests should be written before code using test-driven development.
- Newforms provide a declarative way to create forms with fields, validation, rendering, and conversion to Python types. Forms make it easy to display, validate, and process user submissions.
- The test client allows simulating a browser to test views and templates. Ajax functionality can return HTML, XML, or JSON and be integrated with JavaScript libraries like jQuery.
- Form validation
Parametrized testing, v2
This document discusses different techniques for parameterizing tests in Python using the pytest framework. It begins by explaining the value of automated testing and moves on to demonstrate function parametrization, fixture parametrization, and generating tests using pytest_generate_tests(). Combining these techniques allows for testing many permutations of input values. Guidelines are provided for choosing the appropriate technique based on test structure. The document aims to help readers multiply the effectiveness of their tests through parameterization.
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Test Driven Development
TDD involves an iterative process of designing, testing, and implementing code. It begins with writing a test that fails, then implementing the minimum code to pass the test, and refactoring as needed. The document provides an example of using TDD to develop an "add" function in Perl, including writing tests using the Test::More library that validate different cases like number of arguments and argument types. It also briefly discusses testing libraries for other languages like Javascript's QUnit.
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benpresentation_django
- 2. Example test with parameterized decorator import unittest from nose_parameterized import parameterized class AddTestCase(unittest.TestCase): @parameterized.expand([ ("2 plus 3", 2, 3, 5), ("3 plus 5", 3, 5, 8), ]) def test_add(self, _, a, b, expected): self.assertEqual(a + b, expected)
- 3. Dynamically created tests $ nosetests example.py --verbosity=2 test_add_0_2_plus_3 (example.AddTestCase) ... ok test_add_1_3_plus_5 (example.AddTestCase) ... ok ---------------------------------------------------------------------- Ran 2 tests in 0.001s OK
- 4. Some Use Cases ● Write less code to generate many tests with different parameters ● We use it at SimpleLife to test 15 different scenarios of a django FormWizard view that has 7-8 steps ○ (given inputs [a,b,c,...], we expect to see [x,y,z,..] ● Use it to test APIs ○ input is JSON blob X, expect JSON blob Y as output
- 5. https://github.com/wolever/nose-parameterized We’re hiring full stack Django devs!