How to Use Python for Automation?

The knowledge of a programming language can help a QA specialist in different ways. On the one hand, we get access to test automation tools. On the other hand, we start to understand code, and this advances manual testing. Learning a programming language allows enhancing a person’s competencies and makes an entire software testing company a bit more prepared for the market challenges.

So the dilemma is not “to learn or not to learn” but rather what exactly to learn. There are several factors that can influence your decision – starting with resources and ending up with a range of specialists in the company. So let’s focus on Python and why it is the No.1 option to consider.

Why Is Python So Popular?

Over 70% of developers consider Python the most popular and in-demand language. In addition to being an open-source programming language, it has many libraries of ready-to-use code. Python is a time-saving solution: you don’t have to reinvent the wheel every time you start a new task.

Also, Python syntax is simple, making the language easy to learn. Last but not least, there is a strong community built around Python. You can reach out for help online anytime and receive it almost instantly.

Why You Should Learn Python

Test automation engineers often use technologies like Java, C#, C++, and Ruby. Still, Python has a number of benefits that make it an optimal solution.

- Python is easy to learn. Even the most curious QA engineer has to focus on testing first. Learning Python won’t leave you too exhausted to deal with work thanks to its simplicity. Besides, you can find a bunch of useful materials on the web.
- It has readable code. Python code is easy to understand. It is convenient for scripting and supported by numerous tools.
- Python is a multi-purpose language. It is almost universal and can solve a vast array of tasks. Python is used in web and desktop apps, data analytics, scripting, etc.
- It enhances team productivity. Java uses ten lines of code where Python needs just one. Python is concise and leaves precious time to deal with more complex tasks.
- The community is your backup. Massive code libraries help you save time. You don’t have to reinvent the wheel but use ready code for import.
- Python can automate your entire world – from the deployment of the test environment to continuous integration.

The Advantages of Automation Using Python

We’ve mentioned some of the features that make Python in-demand and popular. Here are a few more words on AT in Python.

1. The Zen of Python, a collection of guiding principles for writing on Python, is an ideal manual for test automation. It reminds you about the basic rules that make automated test scripts efficient. Tests should be simple and readable, obvious and relevant, complex but not complicated. Some would say these things are evident, but The Zen of Python is a manifesto that won’t let you forget the basics.

2. Pytest is one of the best available frameworks for automation available. It can handle any functional test, whether we’re talking about unit, integration, or end-to-end testing. Test cases are written simply as functions and can take parametrized inputs. Plugins extend pytest capabilities and allow you to cover code, run several tests simultaneously, and integrate with other frameworks, like Django and Flask.

3. A rich library of useful packages and ready-to-use ingredients for automation greatly facilitates testing in Python.

4. Python is object-oriented and functional. It allows choosing what suits your tasks better – functions or classes. Distributed functions don’t have side effects, and simple syntax makes them readable.

5. Command Line can drive the entire test automation workflow. Every test framework can launch a console for searching and running tests. Rich command line support greatly simplifies test management. Moreover, automation with Python supports exploratory testing. You can use Python calls to steer an app to a point when manual testing is required.

6. Scalability makes Python equally great for beginners and experienced users. Scalability is achievable through syntax, superb structure, modularity, and a vast ecosystem of tools. It is also possible to integrate numerous side tools and processes.

Writing Automated Tests in Python

Python comes with a set of tools and libraries that facilitate test writing. Your path in automation testing services should start with simple tests. Make sure you can execute those successfully before challenging yourself with complex tasks.

Create a file test.py that will contain your first test case. As you proceed with scripting, it will be reasonable to create a folder with separate files for tests or test groups. Otherwise, the document will become cluttered and hard to maintain. If a project is large, you can split tests into more subdirectories based on their purpose.

The primary task is to decide what exactly to test. A test structure will follow a familiar workflow: generate inputs –> execute the code –> capture outputs –> compare actual and expected results.

When you decide to automate a task, all you have to do is follow five simple steps:

1. Think about how you would execute it without applying any technological solutions.
2. List the steps and people involved.
3. If needed, divide the task into a number of smaller assignments.
4. Solve each of these tasks using an appropriate algorithm.
5. Convert each of those algorithms to code using Python.

Keep in mind that automated tests should be reusable. If you have spare time and a desire to level-up your scripting things, you can create tests covering anything you want. However, this is not efficient for dealing with real-life tasks.

Python Automation Frameworks

• Python unittest (or PyUnit) is a framework from the standard Python library and a great solution to start with Python automation. It provides a basic set of tools that supports fixtures, test cases, test suites, and a test runner. Unittest is often used in test-driven development. To discover the full potential, you will also need nose2 with its system of plugins.
• pytest – the best python testing framework for small projects. It supports compact test suites and offers quick bug fixing. It can run parallel tests and integrate with other test frameworks.
• Robot – an open-source key-driven framework for acceptance testing that provides a rich collection of tools and libraries.
• Behave – a widely-used behavior-driven framework. Written in semi-formal language, it is easy to read for QA team and non-technical specialists, opening opportunities for collaboration.
• Jasmine – one more BDD framework. It is easily integrated into Django projects, allows parallel execution of server-side and client-side test cases, and makes tests more resilient to changes.

To Sum Up

Python solves a wide range of technical problems, is easy to learn, and makes you a part of the global community that gladly shares their experiences. Automation with Python saves you a lot of time. Besides automating QA resources in general, it provides lots of ready code and highly-functional frameworks.

If you already know some other programming language, switching to Python is unnecessary. As I’ve mentioned earlier, you can write scripts in Java and Ruby as well, and they will be equally good. If you only start learning, however, Python is definitely a go-for option.