Support for running tests in parallel with pytest is available through the pytest-xdist package.

With pytest, failed tests also appear in the Problems panel, where you can double-click on an issue to navigate directly to the test

VS Code also shows test results in the Python Test Log output panel (use the View > Output menu command to show the Output panel, then select Python Test Log

For Python, test discovery also activates the Test Explorer with an icon on the VS Code activity bar. The Test Explorer helps you visualize, navigate, and run tests

Once VS Code recognizes tests, it provides several ways to run those tests

You can trigger test discovery at any time using the Python: Discover Tests command.

Sometimes tests placed in subfolders aren't discovered because such test files cannot be imported. To make them importable, create an empty file named __init__.py in that folder.

Testing in Python is disabled by default. To enable testing, use the Python: Configure Tests command on the Command Palette.

python.testing.pytestArgs: Looks for any Python (.py) file whose name begins with "test_" or ends with "_test", located anywhere within the current folder and all subfolders.

python.testing.unittestArgs: Looks for any Python (.py) file with "test" in the name in the top-level project folder.

Create a file named test_unittest.py that contains a test class with two test methods

import inc_dec    # The code to test
import unittest   # The test framework

class Test_TestIncrementDecrement(unittest.TestCase):
    def test_increment(self):
        self.assertEqual(inc_dec.increment(3), 4) # checks if the results is 4 when x = 3

    def test_decrement(self):
        self.assertEqual(inc_dec.decrement(3), 4)

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

Each test framework has its own conventions for naming test files and structuring the tests within, as described in the following sections. Each case includes two test methods, one of which is intentionally set to fail for the purposes of demonstration.

Nose2, the successor to Nose, is just unittest with plugins

Python tests are Python classes that reside in separate files from the code being tested.

general background on unit testing, see Unit Testing on Wikipedia. For a variety of useful unit test examples, see https://github.com/gwtw/py-sorting

Running the unit test early and often means that you quickly catch regressions, which are unexpected changes in the behavior of code that previously passed all its unit tests.

Developers typically run unit tests even before committing code to a repository; gated check-in systems can also run unit tests before merging a commit.

in unit testing you avoid external dependencies and use mock data or otherwise simulated inputs

The practice of test-driven development is where you actually write the tests first, then write the code to pass more and more tests until all of them pass.

The combined results of all the tests is your test report

each test is very simple: invoke the function with an argument and assert the expected return value.

    def test_validator_valid_string():
        # The exact assertion call depends on the framework as well
        assert(validate_account_number_format("1234567890"), true)

Unit tests are concerned only with the unit's interface—its arguments and return values—not with its implementation

unit is a specific piece of code to be tested, such as a function or a class. Unit tests are then other pieces of code that specifically exercise the code unit with a full range of different inputs, including boundary and edge cases.

SMOKE TESTING is a type of software testing that determines whether the deployed build is stable or not.

the Mock Object or Test Stub types of Test Double

Then the programmer(s) will go over the scenarios, refining the steps for clarification and increased testability. The result is then reviewed by the domain expert to ensure the intent has not been compromised by the programmers’ reworking.

Enable Frictionless Collaboration CucumberStudio empowers the whole team to read and refine executable specifications without needing technical tools. Business and technology teams can collaborate on acceptance criteria and bridge their gap.

We really only need to test that the button gets rendered at all (we don’t care about what the label says — it may say different things in different languages, depending on user locale settings). We do want to make sure that the correct number of clicks gets displayed

Methods must be tested both via a Lemon unit test and as a QED demo. The Lemon unit tests are for testing a method in detail whereas the QED demos are for demonstrating usage.

Standardized test scores improved dramatically. In 2006, only 10% of Noyes' students scored "proficient" or "advanced" in math on the standardized tests required by the federal No Child Left Behind law. Two years later, 58% achieved that level. The school showed similar gains in reading. Because of the remarkable turnaround, the U.S. Department of Education named the school in northeast Washington a National Blue Ribbon School. Noyes was one of 264 public schools nationwide given that award in 2009. Michelle Rhee, then chancellor of D.C. schools, took a special interest in Noyes. She touted the school, which now serves preschoolers through eighth-graders, as an example of how the sweeping changes she championed could transform even the lowest-performing Washington schools. Twice in three years, she rewarded Noyes' staff for boosting scores: In 2008 and again in 2010, each teacher won an $8,000 bonus, and the principal won $10,000. A closer look at Noyes, however, raises questions about its test scores from 2006 to 2010. Its proficiency rates rose at a much faster rate than the average for D.C. schools. Then, in 2010, when scores dipped for most of the district's elementary schools, Noyes' proficiency rates fell further than average.

Atlanta’s rampant test manipulation amplified calls for nationwide education reform. Seven years after the Atlanta Journal-Constitution first reported on testing problems, policymakers have failed to make significant progress toward changing the way students take standardized tests and how teachers interpret those scores. In fact, the problem has worsened, resulting in documented cheating in at least 40 states, since the APS cheating scandal first came to light. “Atlanta is the tip of the iceberg,” says Bob Schaeffer, public education director of FairTest, a nonprofit opposed to current testing standards. “Cheating is a predictable outcome of what happens when public policy puts too much pressure on test scores.” Some experts, including Schaeffer, point to the No Child Left Behind Act of 2001 as a source of today’s testing problems, though others say the woes predated the law. Then-president George W Bush, who signed the measure in January 2002, aimed to boost national academic performance and close the achievement gap between white and minority students. To make that happen, the law relied upon standardized tests designed to hold teachers accountable for classroom improvements. Federal funding hinged on school improvements, as did the future of the lowest-performing schools. But teachers in many urban school districts already faced enormous challenges that fell outside their control – including high poverty, insufficient food access, and unstable family situations. Though high-stakes testing increased student achievement in some schools, the federal mandate turned an already-difficult challenge into a feat some considered insurmountable. The pressure led to problems. Dr Beverly Hall, the former APS superintendent who was praised for turning around student performance, was later accused of orchestrating the cheating operation. During her tenure, Georgia investigators found 178 educators had inflated test scores at 44 elementary and middle schools.

Atlanta public schools. The urban school district has already suffered one of the most devastating standardized-testing scandals of recent years. A state investigation in 2011 found that 178 principals and teachers in the city school district were involved in cheating on standardized tests. Dozens of former employees of the school district have either been fired or have resigned, and 21 educators have pleaded guilty to crimes like obstruction and making false statements.

For automated testing, include the parameter is_test=1 in your tests. That will tell Akismet not to change its behaviour based on those API calls – they will have no training effect. That means your tests will be somewhat repeatable, in the sense that one test won’t influence subsequent calls.

I would like to make an appeal to core developers: all design decisions involving involuntary session creation MUST be made with a great caution. In case of a high-load project, avoiding to create a session for non-authenticated users is a vital strategy with a critical influence on application performance. It doesn't really make a big difference, whether you use a database backend, or Redis, or whatever else; eventually, your load would be high enough, and scaling further would not help anymore, so that either network access to the session backend or its “INSERT” performance would become a bottleneck. In my case, it's an application with 20-25 ms response time under a 20000-30000 RPM load. Having to create a session for an each session-less request would be critical enough to decide not to upgrade Django, or to fork and rewrite the corresponding components.

development is hard because you have to preserve the ability to quickly improve the product in the future

Performance Benchmarking What it is: Testing a system under certain reproducible conditions Why do it: To establish a baseline which can be tested against regularly to ensure a system’s performance remains constant, or validate improvements as a result of change Answers the question: “How is my app performing, and how does that compare with the past?”

Tsung utilizes multiple CPUs better than perhaps any other tool I have seen

Do Browse like a user wouldTake natural pauses that users would take to consume page contentFocus on the most common use cases, rather than all the possible use casesTake note of pages where forms/logins occur, you will likely need to complete some scripting there

Yes, traditional scenario load tests are naturally in the risk zone of being axed in the name of this-step-is-taking-too-long as load tests need time to ramp-up and execute the user journeys with the simulated traffic to gain enough measurements that can be acted on. This is why we don’t recommend load tests to be run on every commit for scenario type load tests, but rather in the frequency range of “daily” for performance regression type tests. When merging code into a release branch or as a nightly build perhaps, so that you can have your snazzy load test report with your morning coffee before you’ve settled into your zone!

We believe load test scripts should be plain code to get all the benefits of version control, as opposed to say unreadable and tool generated XML.

It is also good practice to make sure that your load testing is functionally correct. Both the performance and functional goals can be codified using thresholds and checks (like asserts).

Load Testing Manifesto Simple testing is better than no testingLoad testing should be goal orientedLoad testing by developersDeveloper experience is super importantLoad test in a pre-production environment

You can use it for running tests with a high load (spike, stress, endurance tests) in pre-production and QA environments.

Yes; everything needed to run the tests are bundled inside the test suite or executable. There's no connections to foreign processes or systems. I.e, no talking to databases or reading files from disk. If necessary, these connection points are faked / mocked.

boiling and blanching

No backup regimen would be complete without testing. You should regularly test recovery of random files or entire directory structures to ensure not only that the backups are working, but that the data in the backups can be recovered for use after a disaster. I have seen too many instances where a backup could not be restored for one reason or another and valuable data was lost because the lack of testing prevented discovery of the problem.

Various test runners support using compiled webpack code to drive tests, but may be difficult to integrate with webpack, and Neutrino helps alleviate this

But what you absolutely should do is test your NPM package in its published format. Create some smoke tests that reside in the actual codebase, but are not part of the test suite.

Testing is hard. For most developers, testing is a sad story.

In React, there are different aspects of UI Testing. We categorize them as follows along with their tooling:

Storybook integrates with Jest Snapshot through an add-on called StoryShots. StoryShots adds automatic Jest Snapshot Testing to our codebase by using our existing Storybook stories as the input for Jest Snapshot Testing.

The most common phrase in a Pull Request (PR) is: Please add the tests

Since the checkbox is rendering an input I would work with it rather than focusing on the image. You could do something like this: const checkbox = getByTestId('checkbox-1234').querySelector('input[type="checkbox"]') expect(checkbox).toHaveProperty('checked', true)

the way Material UI works is it renders a different SVG when the checkbox is clicked, and not changing the attributes or anything on the actual input element. So how do I actually test that the element is checked in line with the react-testing-library philosophy?

If you're writing a tool for developers, it's a really common case that you want to write a test to ensure that a good error or warning message is logged to the console for the developers using your tool. Before snapshot testing I would always write a silly regex that got the basic gist of what the message should say, but with snapshot testing it's so much easier.

These four things lead to a near total loss in the intended utility of integrated/functional tests: as the code changes make sure nothing is broken.

(After all, it's not like the past snapshot was well understood or carefully expressed authorial intent.) As a result, if a snapshot test fails because some intended behavior disappeared, then there's little stated intention describing it and we'd much rather regenerate the file than spend a lot of time agonizing over how to get the same test green again.

They are generated files, and developers tend to be undisciplined about scrutinizing generated files before committing them, if not at first then definitely over time. Most developers, upon seeing a snapshot test fail, will sooner just nuke the snapshot and record a fresh passing one instead of agonizing over what broke it.

Good tests encode the developer's intention, they don't only lock in the test's behavior without editorialization of what's important and why. Snapshot tests lack (or at least, fail to encourage) expressing the author's intent as to what the code does (much less why)

I very rarely use snapshot testing with react and I certainly wouldn't use it with shallow. That's a recipe for implementation details. The whole snapshot is nothing but implementation details (it's full of component and prop names that change all the time on refactors). It'll fail any time you touch the component and the git diff for the snapshot will look almost identical to the one for your changes to the component.This will make people careless about to the snapshot updates because they change all the time. So it's basically worthless (almost worse than no tests because it makes you think you're covered when you're not and you won't write proper tests because they're in place).

I should also add that I'm in favor of relying more heavily on integration testing. When you do this, you need to unit test fewer of your simple components and wind up only having to unit test edge cases for components (which can mock all they want).

But isn't the point of testing to be confident the application works? Who cares if your unit works if the app is broken? I definitely want to know if the third party component I'm using breaks my use case. I mean, I'm not going to rewrite their entire test base, but if I can easily test my use case by not mocking out their component then why not do that and get the extra confidence?

it doesn't even render in-file components. For example, the <Fade /> component we have above is an implementation detail of the <HiddenMessage /> component, but because we're shallow rendering <Fade /> isn't rendered so changes to that component could break our application but not our test. That's a major issue in my mind and is evidence to me that we're testing implementation details.

The reason this kind of test fails those considerations is because it's testing irrelevant implementation details. The user doesn't care one bit what things are called. In fact, that test doesn't even verify that the message is hidden properly when the show state is false or shown when the show state is true. So not only does the test not do a great job keeping us safe from breakages, it's also flakey and doesn't actually test the reason the component exists in the first place.

I could rename toggle to handleButtonClick (and update the corresponding onClick reference). My test breaks despite this being a refactor.

I could mistakenly set onClick of the button to this.tgogle instead of this.toggle. My test continues to work, but my component is broken.

let's add a console.log(wrapper.debug()) which will log out the structure of what enzyme has rendered for us

Will this test break when there's a mistake that would break the component in production?Will this test continue to work when there's a fully backward compatible refactor of the component?

So finally I'm coming out with it and explaining why I never use shallow rendering and why I think nobody else should either. Here's my main assertion:With shallow rendering, I can refactor my component's implementation and my tests break. With shallow rendering, I can break my application and my tests say everything's still working.This is highly concerning to me because not only does it make testing frustrating, but it also lulls you into a false sense of security. The reason I write tests is to be confident that my application works and there are far better ways to do that than shallow rendering.

For instance, an integration test could verify that all necessary props are passed through from the tested component to a specific child component.

Often, if you are writing a test that amounts to “it does what it does”, or “it does exactly this, which happens to be duplicated in the application code”, then the test is either unnecessary or too broad.

Here are three rules of thumb I use to determine that something is not worth testing:Will the test have to duplicate exactly the application code? This will make it brittle.Will making assertions in the test duplicate any behavior that is already covered by (and the responsibility of) library code?From an outsider’s perspective, is this detail important, or is it only an internal concern? Can the effect of this internal detail be described using only the component’s public API?

Yes, we've violated that "one assertion per test" rule, but that rule was originally created because testing frameworks did a poor job of giving you the contextual information you needed to determine what was causing your test failures.

When you mock something, you're making a trade-off. You're trading confidence for something else. For me, that something else is usually practicality — meaning I wouldn't be able to test this thing at all, or it may be pretty difficult/messy, without mocking. (Like in our credit card example.)

You’ll rest easy at night, knowing that the proper amount of automated test coverage is in place and protecting your product from unintentional breakage.

But far too often, I see tests which are testing implementation details (read this before continuing if you haven't already). When you do this, you introduce a third user. The developer user and the end user are really all that matters for this component. So long as it serves those two, then it has a reason to exist. And when you're maintaining the component you need to keep those two users in mind to make sure that if you break the contract with them, you do something to handle that change.But as soon as you start testing things which your developer user and end user don't know or care about (implementation details), you add a third testing user, you're now having to keep that third user in your head and make sure you account for changes that affect the testing user as well.

"The more your tests resemble the way your software is used, the more confidence they can give you. "

Why is testing implementation details bad?There are two distinct reasons that it's important to avoid testing implementation details. Tests which test implementation details:Can break when you refactor application code. False negativesMay not fail when you break application code. False positives

Unit test coverage grants confidence that code logic is correct(and serves as great developer documentation!)

You want to write maintainable tests for your React components. As a part of this goal, you want your tests to avoid including implementation details of your components and rather focus on making your tests give you the confidence for which they are intended. As part of this, you want your testbase to be maintainable in the long run so refactors of your components (changes to implementation but not functionality) don't break your tests and slow you and your team down.

We try to only expose methods and utilities that encourage you to write tests that closely resemble how your web pages are used.

The more your tests resemble the way your software is used, the more confidence they can give you.

Most of the damaging features have to do with encouraging testing implementation details. Primarily, these are shallow rendering, APIs which allow selecting rendered elements by component constructors, and APIs which allow you to get and interact with component instances (and their state/properties) (most of enzyme's wrapper APIs allow this).

This library is a replacement for Enzyme.

“Code without tests is broken by design”

Here are my tools of choice for testing React apps:react-test-renderer for snapshot unit testingAct API for unit testing React componentsJest for unit and integration testing of JavaScript codeCypress for end to end / ui testing

In Double-loop TDD it’s functional testing that drives our development. We write a functional test for testing that our application satisfy some user stories. When the functional test fails we move to write unit and integration tests which in turn drive how we code our components.

I don't recommend unit testing stateful components, or components with side-effects. Write functional tests for those, instead, because you'll need tests which describe the complete end-to-end flow, from user input, to back-end-services, and back to the UI. Those tests frequently duplicate any testing effort you would spend unit-testing stateful UI behaviors. You'd need to do a lot of mocking to properly unit test those kinds of components anyway, and that mocking may cover up problems with too much coupling in your component.

The amount of dev time it saves is uncanny. Like, what used to take 3 hours to write now takes like 30 minutes. No joke.

If you’re dedicated to reducing flakiness, you can take this a step further and reduce flake in your tests even more by killing timers that are set in your tests:

Snapshot testing is great as it let us capture strings that represent our rendered components and the store it in a separate snapshot file to compare later in order to ensure that UI is not change. While it is ideal for React apps, we can use snapshots for comparing values that are serialized from other frameworks.

Screenshot Test: Applications are not often screenshot tested. However, if the business requirement is there, screenshot tests can be used to diff two screenshots from the same application state in order to verify whether something (styling, layout, …) has changed. It’s similar to a snapshot test, whereas the snapshot test only diffs the DOM and the screenshot test diffs screenshots.

Snapshot Test: Introduced by Facebook’s library Jest, Snapshot Tests should be the lightweight variation of testing (React) components. It should be possible to create a DOM snapshot of a component once a test for it runs for the first time and compare this snapshot to a future snapshot, when the test runs again, to make sure that nothing has changed. If something has changed, the developer has to either accept the new snapshot test (the developer is okay with the changes) or deny them and fix the component instead.

The process of learning effective TDD is the process of learning how to build more modular applications.

Hopefully, the implementation is buggy enough that people will report bugs and that we will add tests for.

been set. This