How is python used in testing

‘Manual testing is dead!’

The buzz has divided the world of software testing in two parts. Some make predictions about a future where all tests are automated; others think that manual testing is still the best option. So, who is right?

Manual vs. automated testing

Automating all test cases sounds like a perfect solution – now you can fire manual testers and test the application with a single click. Not true. Applications are written for us, human beings, and we often interact with software in unique ways. Therefore, the most important bugs are found when interacting and observing the application like real users do: we call this exploratory testing and this is something automation cannot do for you. Running the same test over and over won’t show if something else has changed in the application, and it will miss most of the usability bugs. To summarize, automation is useless where human thinking and judgement are needed. It cannot replace manual testers until AI reaches its pinnacle where it can replace programmers, doctors, and managers.

Nevertheless, in recent years, technologies have developed rapidly and market demands have raised new challenges for testers. As applications become more complex, we need to ensure they work on large sets of configurations and devices. Automation can be a great support by handling repetitive and technical aspects like regression, performance, and load tests.

As for the question in the epigraph, I think what should die is the separation of testing as “manual” and “automated”. Testing is only manual, but we can, and we should, use automation to be more productive whenever relevant. This pushes testers to evolve into specialists with manual testing skills to find the important bugs and also with programming skills to automate repetitive tests. In a given application, you may decide to automate some scenarios on the unit test level, some on the API level, some on the UI level, and manually test other scenarios. To define this strategy in the most optimal way, testers should have a deep understanding of the application, its domain, and the technologies involved. In a nutshell, testers should have skills in both manual and automated testing and the insight to combine them wisely.

Matching these demands is a challenge for many testers, as they may have little or no technical background. This should not be a barrier though because with the right approach, learning can be turned into an engaging and exciting process.

So, let’s first have a look at coding skills in terms of the benefits it can bring to a tester.

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Benefits of coding skills in software testing

A common misunderstanding is that manual testing is not technical and that you need to learn coding only for automation purposes. In fact, having a basic understanding of programming can substantially benefit the manual testing process alone.

When we learn to program, we learn about the elements and structure of software. We learn how data is represented inside the computer, and how bits can be interpreted and misinterpreted. We learn about flow control, decision points, looping, and how mistakes can be made. Even if we never see the source code, when we learn how programs work, we also learn how they might not work.

Essentially, I see coding skills as a technical skill that enhances manual testing and builds a foundation for automated testing, taking the tester to a new level in their profession.

There are 7 key ways that learning to code, and more specifically, learning Python (see below), will improve your software testing. They can be divided into two categories: the benefits of understanding code and the benefits of writing code.

• The ability to understand code will help enhance manual testing.
• The ability to write code will allow one to automate tests and write tools to support manual testing.

Let’s see how understanding programming concepts (points 1-5) and knowing how to write code (points 6-7) can make you a better software tester:

1. A deeper understanding of code change impact: Test case design is a challenge for black-box testers. Not knowing how the code is written, you need to identify the set of conditions to evaluate the software in a limited time. If you have a basic understanding of programming, you can talk to developers and learn how the logic is implemented. This can help you focus your functional and regression tests on the most risky areas.
2. An understanding of the boundaries and layers of the application: Software is built in different layers – business layer, service layer, database layer, etc. Knowing how code layers interact with each other can help you design better integration tests.
3. An understanding of the release process: In software release flows, developers create branches, fix bugs, and merge fixes to a master branch. If you are familiar with this process, you can identify what type of issues may occur as a result of version control.
4. The ability to speak developer’s language: Technical language is the language developers speak, and knowing the terminology can increase the quality of communication with developers.
5. Earn dev trust and gain professional reputation: If you speak developers’ language, they will have more respect for you as a skilled tester. This may open them up to share with you their concerns about the code, risky spots, and refactoring ideas – something they might not have done previously, thinking you would not understand enough.
6. Automating functional tests: A common usage of coding for testers is to automate functional tests. Most of the automation frameworks require you to write code, like Selenium WebDriver, which is the most popular framework. As automation experts state, writing test automation that is maintainable, robust, and reliable is not a simple task. So, the better your programming skills, the higher your chances are to build solid automation.
7. Handling tasks that aren’t practical to complete manually: Apart from automating functional scenarios, testing applications may also require handling various tasks which are not practical or sometimes not even possible to handle manually.

Here are a few examples:

• Generate test data
• Read data from DB, flat files, or XML
• Parse log files
• Extract and analyze performance info
• Set up test environment
• Monitor state of the application

All of these tasks can be automated via scripts and can support testers in finding issues which otherwise would be difficult to find.

Now that we understand the benefits of coding skills, let’s see which programming language we should learn.

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Which programming language is best for testing?

Java, JavaScript, C#, Ruby, Python, C++ – these are all great technologies, but is there one that is best for test scripting and automation?

Generally, all the above-mentioned programming languages would do the job, so if you already know one, just use it. But given that often testers are not technically strong, a deciding factor may be how easy the language is to pick up. Python has an advantage here. According to recent research, Python is considered the easiest language to learn, and many colleges in the US teach programming with Python. In addition to this, there are also other factors that make Python a great fit for testers:

• Readable code: Python syntax is very clear and code reads like English.

• General purpose language: Python can be used to solve almost every programming task – web applications, desktop applications, data analysis, scripting, task automation etc.

• Batteries included: Rich standard library helps you easily accomplish common programming tasks.

• Coding productivity: Being a concise language, Python allows you to accomplish a lot with less code, which can save testing time.

• Script execution: Python comes preinstalled on Mac/Linux systems and you can easily run Python script from shell on Linux servers.

Given that testers need a programming language that is easy to learn, fits a wide range of tasks, is supported by Selenium WebDriver, and is convenient for scripting, Python is a great choice for a tester to learn.

How to learn Python

So, after you have selected Python as the programming language, how do you learn it?

Here is a simple roadmap to help you get started:

1. Pass a brief course to get the basics: If you are an absolute beginner, you need to understand the basics of the language and main concepts, so you can start writing simple programs.
2. Find a mentor: When you learn something new, you may get stuck at some point. A mentor can remove the roadblocks and make your journey smoother.
3. Write a project that is fun and challenging: If you come up with an exciting idea, it keeps you motivated and learning becomes like a hobby rather than a mandatory item on your “To Do” list. Think of a small tool you would like to use, or something related to your hobbies (music, sports, etc.).
4. Google, try out ideas, ask questions to developers – One of the valuable skills for a developer is effective googling. Learn to google your questions, try various ways of doing things, ask developers questions, and use all available resources to help you learn.
5. Read articles and tutorials around your questions: There are a lot of great articles and tutorials on Python that can help you grasp unclear topics.
6. Repeat steps 1-5 for a bigger project: After you’ve completed your first project, congratulations, it’s time to move to the next one!

Learning how to program is an ongoing process. You can always be improving your skills, so keep going until you reach the desired level of competence.

Here is a list of great Python resources freely available on the net to help you continue your learning:

• Codecademy Python course: Easy-going introduction to Python

• Hackerrank.com: Small practice challenges to hone your skills

• An Introduction to Interactive Programming in Python (Part 1): Learn Python on Coursera by writing games

• Dive into Python: Lightweight book to deepen Python knowledge

• Corey Schafer Python tutorials: Highly valuable video tutorials on YouTube

• Automate the Boring Stuff with Python: This book teaches how to automate repetitive tasks

• Google’s Python Class: Free Python class for people with some coding experience

To sum things up

Being an intellectual process of questioning, interacting, and evaluating software, manual testing will be needed as long as software is written by human beings. However, there are aspects of software testing that can greatly benefit from automation and can free up testers’ time for more creative testing tasks. Thus, to be highly productive and provide great value, testers should develop coding skills to take advantage of automation. As an easy-to-learn scripting language, Python is a great choice for testers. Learning to program in Python can be fun if you pick up an exciting idea that can translate into a useful piece of software, and there are a number of great resources freely available in the web to support you in your learning.

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