Still stuck with traditional code-based automation testing? We would strongly recommend you to start investing in AI testing tools. Why? The proper integration of this technology will allow you to achieve a test quality and efficiency that is impossible to even imagine with traditional practices.
With this technology, you can handle complex, adaptive, and context-rich modern web applications. This is possible due to the introduction of natural language processing, machine learning, and synthetic data processing within the app testing workflow.
Unfamiliar with GenAI in software testing? Don’t worry! We are here for you. Our article will help you understand how AI is changing the testing world by improving the efficiency of QA processes and improving the productivity throughout.
What Is Generative AI?
Don’t even know what is generative AI? Fair enough! It is an AI model that can create new content like text, images, or even entire web applications. It performs this process by analyzing the patterns learned from existing data. Unlike traditional artificial intelligence, that is primarily discriminative or predictive, GenAI is a completely creative and constructive model.
Some of the most popular generative AI models that are currently available include ChatGPT, Google Gemini, and Claude.
When we come to software testing, these models can help you interpret the natural language and create context aware test cases. During this process, they will also provide real time code suggestions and will become a very powerful tool in the entire software testing domain.
Challenges Of Traditional Test Automation
To further justify the shift in AI-based test automation, let us divert our attention towards some of the major challenges that you are currently facing with traditional automation testing practices:
- The test scripts are highly brittle as they can break with even a minor change to any user interface element of the current application.
- All the test scripts need to be maintained frequently so that they can stay up to date with the evolving code base and also all the new updates that you are introducing to the infrastructure of the application.
- If you’re creating custom scripts for your application, they are currently lacking adaptability so they cannot smoothly run on multiple operating system platforms or test environments.
- Currently, if you want to create a complex automation test script, you need to have vast knowledge about programming languages and other dependencies. Therefore, it can act as a massive limiting factor for the quality assurance participation.
Test Automation With GenAI
Now that we have a clear idea about all the challenges of test automation with traditional practices, let us divert our attention towards some of the features that you can access with generative AI for solving these challenges:
1. Automated Test Case Generation
With the capabilities of these AI models, you can automatically create functional, regression, and performance test cases. To perform this process, it will use user stories, API specifications, source code annotations, and the requirements documentation.
For example, if you want to verify that the users can log in with valid credentials on the application, the GenAI output will create a complete test script with input fields, user credentials, assertions, and error-handling logic for the test case.
The proper implementation of this process ensures that you are not wasting multiple hours of manual effort to physically analyze the functioning of all the required elements.
2. Natural Language to Test Code
Not accustomed to programming languages? With the capabilities of generative AI, you don’t even have to know these technical configurations to write your automation test scripts. How? Natural language prompts! You can convert plain English into test scripts with your chosen programming language.
This feature will be a massive boon so that even the non-technical members of the app development company can participate within the automation testing process and can also contribute to the QA processes.
3. Self-Healing Test Scripts
Are your test scripts breaking every time you change even a small element of the UI? Self-healing test scripts will help you completely eliminate this challenge! This feature can automatically identify all the broken test strips due to changes in the document object model, selectors, or the test logic.
To perform this process, generative AI will use historical context, element attributes, and suggest or apply the fixes. The proper implementation of this approach will help you to reduce the downtime and also the manual involvement in the troubleshooting steps.
Some of the most common AI testing tools that use this parameter include Testim, Mabl, and Functionize.
4. Code Review and Test Optimization
With the power of artificial intelligence and machine learning algorithms, you can analyze multiple test cases, possibly thousands, to eliminate redundancies, flaky tests, identify missing edge cases, and also recommend assertions or validations as per the requirements of the environment.
The proper implementation of this approach will ensure that your app development company is achieving the highest return on investment from the test suite to improve the overall test coverage.
Although we mentioned all the major advantages of using AI in software testing, you should also be aware that you will receive additional advantages depending on the customized requirements of your software development. For this, you must go through the official documentation of the AI testing tools that you might end up choosing.
AI Testing Tools
To further help you understand the role of generative AI in modern software testing, let us now divert our attention to some of the most popular AI testing tools that are currently available system while creating this list, we have ensured that we are covering almost all the general use cases:
● Testim.io
This tool will use the capabilities of artificial intelligence to create and maintain the user interfaces with minimal manual coding. Moreover, all the test cases that you create with this tool will automatically adapt to all the UI changes with the help of dynamic locators.
● Katalon Studio
Katalan Studio has recently gone through a major update that has added various AI-powered capabilities to its infrastructure. For example, now it can help you with AI-assisted scripting and maintenance features.
The goal of this implementation is to simplify the complex testing scenarios and allow even the non-technical members of the team to participate in these processes.
● Applitools
AppliTools uses the capabilities of visual AI to test the user interface elements of your application on multiple devices and screen sizes. The main goal of this process is to verify that the elements are not only placed properly but are also interactable irrespective of the device that the end user might end up with.
Another good news about this tool is that it has gone through a major update which now even allows the system to suggest test test improvements to achieve the goals that you have originally intended to.
● 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. The integration of artificial intelligence allows you to perform various advanced processes like AI E2E testing and AI cross-browser testing.
This platform also allows you to integrate Appium to initiate all the above test cases on mobile devices as well. Another major benefit of LambdaTest is that it will automatically provide you with a comprehensive test report with multiple media elements like screenshots and videos.
The goal of this inclusion is to allow you to quickly find all the faulty elements and take the required actions. Want to learn more about AI E2E testing with LambdaTest? We have given the code snippet for this process:
Best Practices For GenAI-Based Test Automation
Finally, let us divert our attention towards some of the best practices that we highly recommend to integrate while you are using generative AI in your test automation processes. The role of these practices is not only limited to improving the efficiency of the testing infrastructure but also boosting the quality of the application that you’re currently working on:
- Since the influence of AI in software testing is still in the learning phases, we strongly recommend you to use GenAI for low-risk test cases at first. The major areas that you can consider are UI regressions on nonproduction APIs. Depending on the results that we achieve in this process, you can scale accordingly.
- Before deploying AI and machine learning within your software testing, it is very important to implement human in the loop review process. This will help you validate that all the generated test cases are accurate and will not have a negative impact on an entire testing environment.
- We strongly recommend the testers to continuously train or fine-tune the Gen AI tools depending on the organizational test history. This will be a very important parameter to ensure that the test cases are up to date, depending on the changing requirements of the application and also the organization as a whole.
- It is a very good idea to have version control systems integrated within the generative AI testing workflow. This is because this will allow you to track the changes made by the AI testing tools using GIT or other platforms. If there is any issue in this process, you can easily roll back and audit the bug.
- Finally, we strongly recommend you to integrate the generative AI-powered testing process into DevOps tools like Jenkins, GitHub actions, or GitLab. This approach will help you achieve seamless automation while reducing the overall feedback loops. It’ll also play a very important role in allowing you to perform the development and testing processes at the same time.
The Bottom Line
Based on all the areas that we covered in this article, we can safely come to the conclusion that the use of Generative AI in software testing is a very important change to create faster, much smarter, and more inclusive testing environments. This technology will help you to achieve your quality goals without sacrificing the overall agility of your test environment.
Since the world of AI testing tools is constantly changing, we will strongly request you to keep looking for all the upcoming trends and innovations in this segment. This will be a very important parameter to ensure that you are giving nothing but the best experience to your customers.
Still confused about the role of AI in software testing? Start with a small non-crucial test case and understand how it benefits your overall test efficiency. Depending on the results that you achieve in this process, you can scale accordingly.
