Still not using test AI within your test bench? If you are still using traditional QA test cases, you are going through a cycle of a time-consuming, labor-intensive, and open to human error procedure. On the other hand, if you start using the capabilities of generative AI, you can completely change your QA test cycle, as this new workflow can understand, learn, and generate content depending on this knowledge.
Not accustomed to using generative AI in QA testing? Don’t worry! We are here for you. Our article will help you understand how generative AI can change your QA test cases, along with its major benefits and limitations that you must be aware of.
What Is Generative AI
Since we are trying to focus on the basics of generative AI, it is only logical that we begin with what is generative AI. It is a type of artificial intelligence model that can create new content depending on its learning patterns. To perform this process, it uses techniques like deep learning and neural networks, especially generative adversarial networks.
It also uses Transformers and variational autoencoders to create content like text, image, audio, and comprehensive testing code. When we focus on generative AI, particularly for QA testing, it can help you create the test cases, produce synthetic test data, and also simulate various forms of user behavior.
You can also use the capabilities of generative AI to create automation test scripts by understanding various forms of user interactions or a comprehensive code base documentation. Therefore, it will have a very important role in reducing the technical barriers of traditional automation test cases.
How Generative AI Improves QA Testing
Let us now divert our attention to how you can use generative AI to improve your traditional test cases. While creating this list, we have paid proper attention towards some of the most common use cases that are implemented in the modern software testing cycle:
1. Test Case Generation
You can use the AI models to analyze code, user stories, or requirements. Depending on the data that it will gather from this process, it can automatically create meaningful and diverse test cases. This approach will play a very important role in reducing the load on manual input to create a higher test coverage.
For example, you can use large language models like GPT to input a user story like the requirements to reset the password using Gmail and receive the test cases to verify how your application helps perform this process. The primary structure would be to verify that the reset e-mail is sent, and then testing with invalid e-mail addresses while simulating expired reset links.
2. Synthetic Test Data Creation
You can use the power of generative AI to create realistic, privacy-compliant synthetic data. This data will help you mimic production data that will be very important for testing data-heavy applications without breaching the privacy regulations of your target audience.
A great example would be creating patient records for testing healthcare applications without compromising patient privacy by using the real data that will be present within your server.
3. Automated Script Writing
By integrating the AI model with automation testing frameworks like Selenium or Cypress, you can create test scripts depending on the described functionalities or user interface specifications.
For example, you can use a prompt to test the logging functionality with valid and invalid credentials. Depending on this form, the generative model will create an entire Selenium test script with multiple assertions, input handlers, and failure recovery mechanisms.
You can also integrate the power of artificial intelligence with Selenium to automatically analyze various forms of user interactions and create the test scripts in real-time. This is a very important part to ensure that you do not require extensive knowledge of programming languages to use these testing frameworks.
4. Bug Detection and Prediction
If you train the AI models on historical bug data, then they can identify patterns and predict potential defect zones that will be present within your new code for the application. Depending on the information that you receive from this process, you can automatically prioritize the most sensitive areas of the application to ensure their stability.
It will also have a very important role in reducing the debugging time, as you’ll be finding the errors before they can turn into a serious concern.
5. Test Maintenance Automation
You must have seen that whenever the UI elements change position or the business requirements of the application change, it becomes a very hectic process to update all the test cases to ensure that they keep up with these changing requirements.
But, gone are these days as soon as you shift to generative AI! Why? The generative AI model can automatically analyze the changing business requirements and all the elements based on the application to update the test cases accordingly.
Due to this implementation, you will have less brittle test cases, reduced manual rework, and also quicker adoption to all the upcoming changes to the application infrastructure.
Challenges Of Using Test AI In QA
Although the use of generative AI in software testing is a very advanced process with multiple benefits, you must be aware that it also has its own share of disadvantages. To ensure that you do not face any unwanted obstacles in the testing process, let us go through some of the major challenges of using test AI in your QA procedure:
- In many cases, artificial intelligence can wrongly understand your business logic or edge cases without having sufficient training data. Therefore, it is very important to properly focus on using high-quality training data that consists of user stories, historical test information, and various other business documentations.
- In certain cases, the generated tests or scripts will not have enough relevance or data, depending on the changing requirements of the application. Therefore, it is very important to have some form of human review before the test is actually deployed for the execution process.
- While using generative AI in QA testing, it is very important to align the AI-generated artifacts with the continuous integration and continuous deployment tools. It is also important to ensure that these new test cases can properly fit in within your version control systems. You must be cautious that the entire implementation for this process can be complex, especially for the new testers.
- While automation testing and the use of generative AI can be a very beneficial process, you should not completely eliminate manual testing due to this integration. The best approach in this regard should be to maintain a proper balance between human knowledge and AI productivity. How? Use AI for the repetitive testing scenarios and restrict the complex and creative areas to the human testers only.
Best Practices For Using AI In QA Testing
Finally, let us divert our attention towards some of the best practices that you must use while integrating generative AI with the QA test cases. While creating these best practices, we had proper attention to ensure that we have something for every developer and every form of requirement:
- Since the entire concept of using artificial intelligence and machine learning in your software testing processes is a completely new system, it is very important that you do not migrate all your test cases directly into this structure. The best approach would be to choose a non-critical test and closely monitor its performance when it is migrated to AI testing. Depending on the results that you achieve here, you can scale accordingly.
- Although generative AI is a self-sufficient process, it is very important to have some sort of human oversight to ensure that you have the control over the results. We also suggest you to have a quick glance at the test cases that will be created by this AI model before actually deploying them.
- While you are initially deploying the AI model in your test environment, it is very important to customize the model depending on the domain-specific data. You could also choose the training data that will customize the execution process of the test cases depending on the specific requirements of the application that you are working on.
- We would recommend you to have some sort of culture integration so that you can smoothly embed the AI model into your existing DevOps pipelines. It will help you to use the capabilities of artificial intelligence to run the development and testing processes at the same time to further reduce the feedback loop in the application testing cycle.
- While you’re initially experimenting with the use of generative AI in your testing environment, it is very important to collect feedback from not only the development and testing teams but also the business analysts as well. This process will help spread the general knowledge regarding the use of AI in your testing.
- While you are using AI in software testing, you should have equal importance on real device testing to understand the change in application behavior depending on the parameters of a real device. To perform this process without investing in an on-site device lab, you can choose cloud-based platforms like LambdaTest. LambdaTest is an AI-native test orchestration and execution platform that lets you perform manual and automation testing at scale with over 3000+ browsers, OS combinations, and 5000+ real devices.
Apart from all the practices that we mentioned above, you should also understand that there will be certain best practices which will be customized depending on your specific needs and the application that you’re working on. To properly create these practices, you must have a clear understanding of your testing habits and the angles of the application that you’re working on.
The Bottom Line
Depending on all the areas that we discovered in this article, we can easily come to the conclusion that generative AI is completely changing how you run modern QA test cases. They have a very important role in not only the test creation process but also the maintenance and execution phases as well.
While generative AI in itself might not be the complete package, if you combine it with the human oversight and the proper use of automation testing, it can become a very important part of the modern software validation process.
Moreover, you should constantly look out for all the upcoming trends and innovations in this segment, as it is a rapidly growing industry. This approach will ensure that you are constantly improving the end user experience, which will, in turn, have a very important role in maintaining a positive brand image.
