More organizations are turning to automation testing to achieve fast, reliable, and cost-effective results. With such an overwhelming amount of information, platforms, tools, and technologies it’s challenging to determine where to begin.
In this article we will go over how to get started with test automation and assess it’s actual difficulty.
Understanding Automation Testing
Automation testing refers to the use of special tools and frameworks to automate the execution of test cases. Like a manual test case, an automated test runs through some steps, and compares an expected and actual outcome to determine a pass or fail.
Unlike a manual test, once an automated test case is written, it simply runs by itself. QA testers are able to focus on more important work with the additional time saved from automation. This reduction in manual effort can improve efficiency and help ensure consistent test execution.
How Do Automated Tests Work?
Automated tests involve defining a comprehensive set of test cases that make sure to cover different use cases, scenarios, and processes. For example, if you’re testing a mobile application for booking flights, you’ll need a test for searching for flights, choosing your seat, viewing your boarding pass, and more. Along with each test case, an expected outcome is documented.
Then, they can be scripted using test automation tools and frameworks. Automation tools like Selenium, Appium, and others provide a framework for creating and executing automated tests. These tools let testers write test scripts using programming languages like Java, Python, C#, and more.
Once the test scripts are written, the automation tools can interact with the application in different ways.
- Simulating user actions by interacting with an application’s user interface elements, like clicking buttons and filling out forms.
- Interacting with APIs, sending requests, and validating responses.
- Executing SQL queries or modifying data.
- Injecting custom scripts into web pages to manipulate the DOM or trigger actions.
The specific method depends on the tool, technology stack, and test requirements.
As the test scripts run, the automation tools compare the actual results of each step with the expected outcomes defined in the test cases. If there is a mismatch, the tools detect and report the discrepancies as failures or errors.
Finally, most test automation tools can generate detailed reports and logs summarizing test results. These reports help testers and developers identify issues and track the overall quality of the software. Testers can then analyze failures to pinpoint the root causes and provide developers with specific information to fix issues.
Benefits of Automation Testing
Automation testing comes with many obvious advantages over manual testing.
- Faster: In today’s market, It’s all about time to value and testing is often the bottleneck. Automation allows for the execution of tests at a significantly faster pace than manual testing, enabling rapid feedback and shorter release cycles.
- Improved Test Coverage: As long as you’ve created the test cases you need, Automation enables comprehensive test coverage. With manually testing, it can be easy to cut corners, miss steps, or forget test cases. Automation ensures that critical functionality and edge cases are tested consistently and frequently.
- Enhanced Accuracy: Automation helps eliminate human errors that encounter manual testing. It’s run in the right context, with the right variables, the right way.
- Cost and Time Savings: Automation testing requires an upfront investment, but it ultimately saves time and resources in the long run.
But how easy is it to realize these benefits?
Is Automation Testing Difficult?
With any tool, there is a learning curve. When it comes to code-based automation, the learning curve is steeper. The good news is most frameworks, platforms, and tools have comprehensive documentation and a thriving, helpful community.
However, there is a barrier to entry. The challenge will be possessing, or finding someone who possess developer skillets. With code-based frameworks, you’ll need to leverage a programming language like Java or Python. You’ll also have to know your way around an IDE, understand version control, and know how to make API requests.
Most importantly, you’ll need to know how to architect your test suite in a way that scales. This means using consistent and reliable elements, regularly updating test cases, and making sure test cases are compatible on different devices.
The Fragmented Device Ecosystem
The most challenging thing about writing test automation, especially for mobile devices, is fragmentation.
Every year, new devices are released into the market. App functionality that works on one device, may not work on other devices. The UI might be different, and user experience could be greatly impacted.
That’s why automation needs to be updated to run on different devices, operating systems, and versions. To do this, testers can leverage emulators, simulators, and should also test on multiple real devices.
Making Automated Testing Easier
Although automated testing sounds complex, it can be made easier by leveraging two growing trends – Abstraction and AI.
Organizations are recognizing the value of abstracting their tech stack with no-code and low-code.
Some platforms let you record manual tests. These no-code tools record user interaction, log steps, and allow the tester to indicate assertions. Then you can “play back” the test on demand, schedule tests, or integrate with an external CI/CD tool.
Sofy’s no-code engine simplifies mobile automation testing. With Sofy, you can acquire real devices from a cloud-based device lab and record a test in minutes. Each time you acquire a new device, a fresh instance is provisioned with your app already installed. Once you record your test, you can easily convert it to an automated test and run it across dozens of devices.
AI is also making waves in the testing automation world.
AI and ML can identify patterns and trends in test results, provide change recommendations, and even automatically generate test cases. Some tools use predictive analytics to analyze large volumes of testing data to help predict future issues.
Sofy has been using AI to help testers record resilient UI tests, detect regression, and understand the intent behind your tests. Recently, Sofy introduced Co-Pilot – a suite of AI capabilities that make creating automated mobile tests easier. With Co-Pilot you can:
- Leverage SofyBot, a virtual companion that runs on OpenAI. SofyBot allows you to ask for real-time performance metrics for your tests, quickly debug issues, and share insights from device metrics
- Use a JIRA link to automatically create manual test steps in seconds
- Identify and detect issues with your test suite early on
Is Automation Testing Worth It?
While automation testing may initially seem daunting, with the right approach, tools, and frameworks, automation testing is paramount in delivering high-quality applications. In this competitive marketplace, you need to ensure that your user’s experiences are seamless – whatever device they are on. The best way to do that Is by automating your test cases.
Automation testing can be hard. But we think Sofy makes it easier. Get a demo with Sofy today.
