Being aware of a programming language can help the automation testers in various ways. Apart from offering access to different test automation tools, the knowledge of coding also advances the scope for manual testing. Considering this thing, automation testing with the knowledge of a programming language increases the competency of a person and makes the tester prepared for all the marketing challenges. But here the important question is which programming language to learn and which one is perfect for writing tests. Generally, automation testers face a “Java vs Python” dilemma. In this blog, we will focus on Python.
What is Python and what makes it popular?
Python is one of the open-source programming languages and a large number of developers across the globe count it on as the most in-demand and the most popular language. Python has many libraries in open access. Therefore, there are fewer lines of original codes to compose on your own. The syntax of Python is simple and this thing makes this language quite easy to learn. Additionally, there is a robust community developed around Python. This ensures that you can reach out for assistance anytime.
Why it is a good idea to learn Python?
You can get the best answer to this question in the TIOBE index. But the popularity of this programming language is not the sole reason why QA companies are using it. Languages like C++, C#, Ruby, and Java are often used in test automation. Still, Python has several benefits, which make it the perfect solution.
Python is very easy to learn. Here the QA engineers need to concentrate on software testing services. Therefore, learning new things with Python never becomes tough. The availability of simple syntax makes this language easy to learn from scratch. Apart from that, different types of useful materials related to this language are available on the web, which is readable. Besides, Python codes are easily understandable.
It is supported by different tools and therefore it is very convenient for scripting. Being a general-purpose and universal language, this programming language can solve different types of tasks. Therefore, it is widely used in different desktop and web apps, scripting, data analytics, etc. This thing increases productivity. Apart from that, being concise, Python needs solving more tasks with a few lines of code and it allows the testers to leave precious time for dealing with the more complex jobs.
Here the community works as your backup. And the huge code libraries help you to save time successfully. It means there is no need to reinvent the wheel except using ready code for importing. This way, by automating the world starting from deploying the test environment to constant integration, Python script automation can make your life easy.
What are the main benefits of automation using Python?
In this blog, we have already mentioned the important features, which make Python very popular and in demand. Here are some other details related to automation testing in Python. The Zen of Python, which is the collection of guiding principles for code writing in Python, is considered to be the perfect manual for test automation. It retells you all the basic rules, which results in efficient automated test scripts. Automation testing needs to be readable and simple, relevant and obvious, difficult but not problematical. Even though all these things are evident, Zen of Python works as a manifesto, which doesn’t allow users to forget the basics.
And Pyrest is one of the most popular frameworks of Python, designed for automation testing. It can easily deal with all the functional tests, whether it is end-to-end testing, integration, or unit. Here test cases are simply written like functions and they can take some parametrized inputs.
Pyrest capabilities are extended with plugins and this thing allows the users to cover code, integrate with other frameworks like Flask and Django, and simultaneously run several tests. The pre-packaged ingredients for automation and the rich library of important packages greatly facilitate Python automation testing. As Python is functional and object-oriented, this thing allows selecting what suits the tasks better, classes or functions. Distributed functions don’t have their side effects and with simple syntax they become readable.
Command-Line can successfully drive the total workflow of test automation. Every test framework is capable of launching consoles for searching and operating tests. Besides, the availability of rich command line support can greatly ease the process of test management. So, automation with Python greatly backs exploratory testing.
This means, here the user can use Python calls for steering an app to the point when manual testing is needed. And scalability makes this programming language equally perfect for both experienced users and beginners. Here scalability can be achieved through superb structure, syntax, a huge ecosystem of tools, and modularity. This programming language also allows the integration of many processes and side tools.
How can you write and run automated tests with Python?
Python is available with a range of libraries and tools, which facilitate test writing. The path in automation testing services must begin with some simple tests. It is important to ensure that you can successfully execute things before challenging yourself with some complex jobs. Create files test.py, which will contain the first test cases. And when you will proceed with scripting, it would be reasonable to create folders with separate files for test groups or tests. Otherwise, the document would become cluttered and tough to maintain.
So, if you are working on a large project, then you are allowed to split tests into different subsidiaries depending on their purposes. Here the primary job is to decide what to test exactly. The test structures will follow a similar workflow:
- Generate inputs
- Execute code
- Capture outputs
- Compare the expected and actual results
So, if you are thinking of automating tasks, you just need to follow the simple steps:
- Thinking about how to execute things without applying technological solutions.
- List all the steps and the people involved. If required, then divide the tasks into some smaller assignments
- Solve each task with the right algorithm. Then convert the algorithms to code with the help of Python.
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