Python for Automation: Why Is It Great and How to Use It?

The knowledge of a programming language can help a QA specialist in different ways. Besides providing access to test automation tools, the ability to understand code also advances manual testing. One way or another, it enhances a person’s competencies and makes a software testing company better prepared for the market challenges.

The question is what programming language to learn and use for automation testing services. Usually, tech specialists face a “Python vs Java” dilemma. Let’s focus on the former in today’s post.

What is Python & Why Is It Popular?

Python is an open-source programming language. Over 70% of developers consider it the most popular and in-demand language. There are many libraries in open access, so there are fewer lines of original code to write on your own. Python syntax is simple, making the language easy to learn. Moreover, there is a strong community built around Python, and you can reach out for help online anytime.

Why Is Learning Python a Good Idea?

Nothing speaks of Python better than its rise in the TIOBE index. But the popularity isn’t the only reason why QA companies continue using it. Technologies like Java, C#, C++, and Ruby are often used in test automation services. Still, Python has a number of benefits that make it an optimal solution.

• Python is easy to learn. A QA engineer has to focus on software testing services, and learning new things shouldn’t become an obstacle. The simple syntax makes Python the best programming language to learn from scratch. 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 an (almost) universal language. Python is a general-purpose language that can solve a vast array of tasks. It is used in web and desktop apps, data analytics, scripting, etc.
• It enhances team productivity. Where Python needs one line of code, Java uses ten lines. Python is concise, so it allows solving more tasks with fewer lines of code, leaving 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 script automation makes your life easier as it can automate your entire world – from the deployment of the test environment to continuous integration.

The Pros 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.

How to Learn Python

How do you learn everything else? The strategy for Python doesn’t differ much. Find online courses, YouTube lectures and tutorials, and mobile apps to learn the basics. If you get stuck at some point, find a mentor who can clarify the complicated topics. Getting familiar with the basics usually takes 6 to 8 weeks.

Start with Python automation testing by writing simple programs from the very beginning. As your skills evolve, think about a more challenging project and start working on it.

Join online communities. Read earlier posts and ask questions. The benefit of online communities is an opportunity to get a piece of advice based on real and often recent experiences. Keep reading articles and tutorials as you learn and even after you become quite skilled.

How to Write and Run Automated Tests using 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.

Wrapping It Up

Automated software testing with Python allows optimizing QA resources so the team can devote more time to complex tasks and functionalities that require a manual check. Learn more about automation practices and QA outsource in general in our earlier blog posts.