The term “PyTest” itself gives the hint that it is the combination of the two terms “python” and “test”, two of the highly popular terms in the tech world. So, the name clearly states that it has something to do with Python, the widely used programming language, and testing, a crucial part of the software development life cycle. You will get to know more about PyTest as you go through the further sections of this article.
PyTest is a testing framework used to easily write & scale test codes using the Python programming language. Using it, you become capable of writing simple and scalable test cases for databases, UIs, or APIs. Though PyTest can be used for all types & levels of software testing, this framework is primarily used for writing simple unit tests to complex functional tests for APIs. The amazing experience of using PyTest is cherished by different groups of people including development teams, QA teams, independent testing groups along with individuals practicing TDD & open source projects. The extremely useful advantages of using PyTest have made projects (e.g. Mozilla, Dropbox, etc.) from all over the internet switch from unittest or nose to pytest.
The reason behind such immense popularity of PyTest is its design, like an extensible system that allows writing plugins easily. It already has a rich ecosystem with lots of plugins and extensions that are highly useful to solve various purposes. Let’s dive more into the details of this framework in further sections.
It is evident that PyTest is a great testing framework but why should it be preferred to other tools? Well, its advantages can state the reasons so, let’s check them out.
The features that make PyTest a preferred choice of many teams are the following -
Here we have jotted down the step-by-step method to install PyTest in Linux.
Let’s see how to use PyTest using a basic example.
The output will be shown as:
Here in test_sample1.py F.
F means failure
Dot(.) means success.
In the failures section, the failed method(s) and the line of failure can be seen. Here x==y means 5==6 which is false.
Source of example: https://www.guru99.com/pytest-tutorial.html#10
Fixtures are something that exempts you from running the same set of code before every test run. A common example of a fixture i.e. setting up a data source before a test is given below.
The property of fixtures is that they need to be run before and after each test function to which it is applied. As you saw above, they play a significant role in setting up and demolishing resources before the test starts and ends respectively. Now, let’s check out an example of a PyTest fixture.
You can identify a method as a PyTest fixture by the following mark -
The way to use a Pytest fixture is mentioning the fixture as an input parameter.
Now, check out how to create a new file test_basic_fixture.py with the following code.
Each of those test functions has an input argument whose name is matching with an available fixture. Then the corresponding fixture method is invoked by PyTest and the returned values will be stored in the input argument that is here the list [25,35,45]. Now, you can use the list items in test methods for the comparison.
Use this command to run the test:
The result will look like following -
As zz=BB=35, the test “test_comparewithBB” is passed, and the remaining two tests are failed.
The scope of the fixture method is limited only within that test file it is defined. If any attempt is made to access the fixture in some other test file , an error will occur saying fixture ‘supply_AA_BB_CC’ not found for the test methods in other files.
Source of example: https://www.guru99.com/pytest-tutorial.html#5
As you are going through a lot of working information about PyTest, you should also know where to use it. You already know that fixtures are used in cases where some parts of code are reused multiple times. Hence, PyTest works as a great alternative to class xUnit style setup and teardown methods.
For better decision making about using fixtures, you can check for the following reasons that make it mandatory to use them.
Though Fixtures are capable of extracting the objects that are used in multiple test cases, they are not always the best choice to use. Even if your program needs a little bit of variation in the data, you must not consider using fixtures.
PyTest fixtures let you know about the scope of using them when a function is invoked.
PyTests’ Future Scopes are -
Assertions are such commands that are used to control the execution of certain methods based on a condition being true or false. That means the “assert” command can return either “True” or “False” status. In PyTest, if an assertion fails, the execution of the test method will stop at that point and the remaining code will not get executed.
You got to know a lot about PyTest, a very strong effective testing framework based on Python, one of the most popular and dynamic programming languages. This article tells you about what it is, how to install it, how to start using it like a pro, and about PyTest Fixtures & assertions, two of PyTest’s highly important aspects that can exempt you from the hassle of repeating huge chunks of code in multiple occurrences and from the worst case scenarios of executing false tests.
However, this is a vast topic and we will cover more operational aspects of this popular testing framework in the future. For that, you just have to stay tuned to our blog page.
Also, in the tech world, products are developed using various different languages and infrastructure. But, whatever the case is, testing the products faster and without investing a lot of time & resources into writing complex test codes brings a significant level of efficiency to the business. And, all that is easily possible by automating your tests with a truly codeless test automation tool like Preflight. You can get to know all the amazing features of this highly efficient tool from the article “10 Amazing Software Testing Benefits From Preflight In 2022”.