Are you still restricting your capabilities to rule-based automation testing? Honestly speaking, you are losing out on a lot of potential and capabilities of your testing environment. So what’s the alternative? You should start incorporating AI test automation within all your development and testing practices.
Using the capabilities of artificial intelligence-based workflows and machine learning algorithms, you can create smarter test automation scenarios, predictive analytics and also have access to multiple benefits that can help you go much beyond the capabilities of traditional automation scripts.
Unfamiliar with AI test automation? Don’t worry! We are here for you! Our article will help you understand the role of machine learning in automation testing, some of its major use cases, and the best practices that you can implement in this workflow.
What Is Machine Learning
Don’t even know what is machine learning? Fair enough! It is an AI technique where algorithms learn from historical data, recognize patterns, and make predictions or decisions without being explicitly programmed to do so. While working with automation testing, machine learning will allow your system to become smarter by dynamically learning from previous test results, changes in application behavior, and various user interaction patterns.
So, using the capabilities of machine learning in test automation, you will be able to implement the following benefits:
- Quickly find out all the high-risk areas that are present in the core infrastructure of your application. These can also consist of all the errors that have been frequently occurring within the source code.
- Optimize the overall test coverage within the environment. For this approach, ML will ensure that there is at least one dedicated test case to verify the functioning of all the elements on your application.
- Depending on the high-risk areas that you identified in the first benefit, you can prioritize your testing efforts and resources efficiently. This means with these insights, you can ensure that you are testing these areas at first to ensure that the app is functioning at all instances.
- Finally, the implementation of all the above workflows will play a very important role in reducing flaky test cases like false positives or false negatives.
Applications of Machine Learning in Test Automation
Let us now divert our attention to some of the major areas within your test automation framework that can massively benefit from the integration of machine learning algorithms:
1. Test Case Prioritization
Emil algorithms will go through the test execution history, code changes, and defect trends. Depending on all the information that it will gather from these processes, it can prioritize the most critical test cases within your application infrastructure.
If you are able to properly implement this parameter, you will have a very important part in reducing the overall defect with your app.
2. Test Suite Optimization
As the application continues to go through multiple updates, it becomes a common phenomenon for it to be bloated with redundant or obsolete test cases. With traditional practices, you have to manually go through all these test suites and ensure their validity depending on the current state of the application.
With the integration of ML algorithms, this entire model will help you to identify duplicate tests, tests with minimal coverage contribution, and even the flaky test cases.
3. Self-Healing Test Scripts
The user interface elements of an application frequently change on modern or mobile apps. The AI testing tools with ML algorithms can find element changes and update the locators dynamically depending on all the historical patterns.
So, if you are able to implement this workflow, you will be able to massively reduce the test script failures during the final execution of the application.
4. Visual Testing Using AI
It is no wonder that one of the hardest things in the app testing process is to find the visual bugs through code-based assertions. However, using AI-based visual testing tools, the machine learning algorithms can find layout shifts, rendering issues, and color errors. It can implement this parameter across multiple browsers and devices.
5. Defect Prediction
By analyzing the commit history, code complexity, and previous bug patterns, machine learning models will predict potential defect-prone areas within the application. Based on this information, you can directly guide your testing time and resources to these areas of the app.
6. Anomaly Detection
You’ll be shocked to know that ML models can directly flag unusual behavior in app performance, user journeys, or test results. In most cases, you will find that all these behaviors will not be directly achieved with predefined test cases.
7. Intelligent Test Data Generation
Finally, machine learning models can also help you to create realistic and diverse test data. It achieves this goal by learning from production data patterns which is important for simulating real-world use cases and also finding the edge cases specific to your app.
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Best Practices for Implementing ML in Test Automation
Let us turn our attention towards some of the best practices that we strongly recommend the testers to follow while using machine learning algorithms in modern automation testing:
Start Small
Since the entire idea of adding machine learning algorithms and artificial intelligence to test automation is completely new in itself, we will strongly recommend you to start small with a non-crucial test case. Depending on the success and other metrics that you achieve with this process, you can scale accordingly.
Integrate With DevOps Practices
It is very important to make sure that all your machine learning testing tools are part of your continuous integration setup. This approach will help with faster feedback and also the implementation of smooth automation testing practices.
Using this approach, you can also conduct the development and testing process at the same time. The benefit? You can find and remove critical bugs as soon as they appear in the application infrastructure.
Use Cloud Testing
While you’re looking for AI testing tools, you should also start investing in cloud-based platforms that use artificial intelligence for executing real device testing through remote device farms.
This is because, with these platforms, you can not only run the test cases on thousands of real devices from operating systems and browsers but also ensure that you are integrating the advanced capabilities of AI and ML.
A great example of one such platform is LambdaTest. It is a GenAI-native test orchestration and execution platform that lets you perform manual and automation testing at scale with over 3000+ browsers, OS combinations, and 10,000+ real devices.
Monitor And Retrain Models
You should continuously monitor the accuracy of a machine learning model and also retrain it with fresh data. Using this approach, you can help adapt the entire testing cycle to your application’s changing requirements. This also becomes important to ensure that your ML model does not develop any form of bias with time.
Collaborate With Teams
While using advanced practices like machine learning and artificial intelligence, it is a very good idea to add the testers, developers, and data scientists during the implementation process.
Using this approach, you will be able to implement cross-functional insights and alignment. It will also be important to ensure that everyone has a basic idea about the current status of the development project.
Measure ROI and KPIs
Whenever you are working with any form of new technology like ML models, it is very important to ensure that this approach is successful and approved by the key stakeholders of your company. How do you do so? You have to measure the ROI and your key KPIs during this process.
Some of the major metrics that you should track include test coverage, execution time, defect detection rates, and maintenance costs before and after the ML adoption process.
We also encourage you to understand the specific requirements of the application that you’re currently developing and create certain best practices that will be customized according to these requirements. It will be also a very important step to customizing the ML offerings and ensuring that you are utilizing all the available potential.
Future Of ML In Automation Testing
Excited about the use of ML in test automation? Then we have another good news for you! Since artificial intelligence and machine learning are progressing so rapidly, over the next decade, you will be able to see more advanced practices being integrated into this entire workflow. To give you an idea, let us divert our attention towards some of the most expected integrations in the coming years:
- Although natural language processing allows you to write test cases in plain English, you can expect these features to be integrated with even the complex automation test scripts. It will also help you to auto-generate the executable tests and ensure that even the non-technical members can participate in test automation.
- Various generative AI models like GPT will help you to auto-generate the test cases, explain test failures, and even suggest fixes depending on all the data it will get from historical test logs. These models will also constantly monitor the test action process to find detects and warn you as soon as possible.
- Over the coming years, machine learning algorithms will drive model-based test generation. These processes will be especially visible in systems that have complex test transitions of microservices. ML algorithms will also start understanding the usage patterns and create such test scripts that were previously impossible with traditional practices.
- The use of machine learning and robotic process automation will allow you to implement end-to-end automation practices across the entire QA lifecycle. This will give rise to a new technology that will be known as Hyper-automation which can cover everything from requirement analysis to final Test reporting.
The Bottom Line
Based on all the areas that we achieved in this article, we can see that machine learning is not just a simple futuristic idea, but it is the factor which is transforming the entire app testing industry. By implementing AI in test automation, you can create more intelligent, adaptive, and efficient test cases.
Although the early adoption of artificial intelligence and machine learning can have its own share of challenges, all the strategies that we have given in this article will help you to efficiently overcome these challenges and ensure the success of your entire testing environment.
You should also constantly lookout for all the fixes, new innovations, and additions in this segment so that you can ensure that you are giving nothing but the best experience to your customers. You should always remember that the future of automation testing is not about automation but about creating tests that are intelligent.
