LLM-Powered Test Automation: Are you still executing traditional code-based QA test cases? We’re sorry to say, but you are leaving a lot of potential unused. Why? By using artificial intelligence with quality assurance, you will be able to execute various advanced processes like AI E2E testing, AI-based cross-browser testing, and many more.

By using the power of large language models like GPT4, Google Gemini, you can change the way how you generate test cases, create test data, and even maintain your entire testing workflow.

Unfamiliar with LLM-powered test automation? Don’t panic! We are here for you. Our article will help you understand how large language models are changing the future of quality assurance and software testing as a whole.

What Are LLMs?

Don’t know what large language models are? Fine enough! They are AI systems trained on huge sets of test data to understand and generate human-like language. Their expertise lies in pattern recognition, contextual understanding, and semantic reasoning.

  • All the above parameters of a large language model make it one of the perfect options to integrate with quality assurance and software testing. To understand more, let us divert our attention towards some of the major areas of the software testing cycle that can benefit from the integration of a large language model:It will help you to automatically create the test cases by understanding the user stories and the historical test data.
  • If you do not have a proper understanding of programming languages, you might not be able to create the automation test scripts. But thanks to large language models, it can take care of all these steps so that you can focus on other areas of the project.
  • If you already have a lot of existing manual test codes that you want to transform into automation test data, a large language model can help you achieve this goal.
  • Using large language models, you can create synthetic test data that can emulate various forms of real-world interactions. This will be a very important part to ensure that your application remains stable and functional after deployment.
  • If you have a huge amount of legacy test code, you have to involve a lot of manual effort to ensure that it is still relevant, depending on the changing requirements of your application. However, with the integration of LLM and ML, the entire model can take care of this process without your involvement.
  • Finally, using AI-based testing, the test environment can go through all the previous test logs and suggest fixes for the bugs that already existing within the application infrastructure. It can also give you a fair understanding of all the critical areas that can develop bugs in the coming years.

Challenges Of Traditional QA

To further justify the inclusion of large language models with your QA testing processes, let us go through some of the major challenges that you will face with traditional QA that is currently operational:

  • Even if you are using automation test scripts using various frameworks, they can break with even a minor user interface or business logic change.
  • Creating the test scripts for your environment will be a highly time-consuming process and will also require domain-specific knowledge to create them at the first place.
  • In most cases, traditional testing approach hard-codes that test to the respective element. This means that you will not be able to easily reuse the existing test on different modules or applications.
  • In most cases, the human testers will miss the edge cases or paths due to cognitive bias or fatigue that is developed over a long period of time. Therefore, there will be a huge possibility the application might develop bugs after the deployment.
  • Whenever there is a test failure or a requirement to create a new automation test script, you will require some form of human involvement with traditional testing practices.

How LLM Helps Overcome The Challenges?

Now that we have a clear idea about all the challenges that you will face while using traditional QA testing, let us divert our attention towards some of the major ways in which large language models can help overcome them:

1. Automated Test Case Generation

Using the capabilities of LLMs, you can understand user stories, requirement documents, and code bases. Depending on all the knowledge that you will gather from these, you can create the test cases in plain English language.

Then? The LLM-based test environment will automatically convert this simple test script into an automation and executable file that can be understood by the system.

For example, if you want to create a test script where you want to verify if the user can log in using their e-mail and password, the LLM-based environment will create multiple test cases where it will use valid passwords, invalid passwords, empty fields, and SQL injection attempts.

2. Scriptless Automation

Are you still writing your own automation test scripts with the code? If you transition to LLMs, your team can move to a Natural Language Processing-based test creation process.

In this approach, you can give a prompt like testing the login functionality for valid and invalid credentials, and based on that, the LLM will create executable scripts for multiple automation frameworks as per your choice.

3. Smarter Test Data Generation

It is no wonder that efficient test data is one of the most important sources to ensure the accuracy of the entire test environment. Using the power of AI and LLM, you can create realistic context-aware test data. This data will also have multiple domain-specific rules and privacy compliances to ensure user privacy.

Some of the major areas that you can achieve with these processes include mocking customer profiles, testing payment scenarios, and also including each case’s datasets within the test flow.

It will also play a very important role in analyzing the test environment to generic unique datasets while ensuring that they are not interfering with the existing code of your environment.

4. Automated Maintenance and Refactoring

With the evolving and changing requirements of your application, you no longer have to manually adjust the test scripts with LLM. This is because the entire testing model will take care of the following parameters:

  • Refactoring the brittle test scapes to ensure that it is accounting for all the new element locators that you have introduced within the infrastructure.
  • Suggesting a more modular and maintainable structure, considering the evolving requirements and changing needs of your application.
  • Finding all the outdated test cases within your environment and then converting the tests from one framework to another.

This entire implementation will help you reduce the maintenance burdens while ensuring the tests remain relevant over time to maintain the scalability of your infrastructure.

5. Bug Detection and Analysis

If you can properly use the capabilities of LLMs, you can analyze various logs, stack traces, and error messages. The benefit? You can implement the following parameters within the testing environment:

  • Suggest probable causes of failure by analyzing all the testing history and the bugs that have been already detected in the previous troubleshooting steps.
  • Recommending code changes to ensure that the application remains stable even after adding the updates or deploying them to the customers.
  • Generating and executing the regression test cases to prevent future failures and also ensure that any new addition do not break the functioning of the existing elements of your application.
  • If you can combine these capabilities with observatory tools, it will become a very powerful AI-based QA assistant which can proactively monitor your application to find failures and suggest improvements.

To help you summarize the benefits of using QA in large language models while using test automation, we have created the following benefits table:

Benefit Description
Speed Test creation and maintenance are significantly faster
Accuracy LLMs help reduce human error and bias
Scalability AI scales effortlessly across large and complex apps
Cost Savings Reduced time = reduced cost of QA cycles
Adaptability LLMs learn and evolve with your codebase over time
Collaboration Developers and QA engineers can use natural language to co-create tests

Best Practices For LLM-Powered QA

Finally, you should seriously consider adding the following best practices within your QA testing processes while using large language models:

Cloud Testing

When migrating to AI test automation, you should consider adding cloud-based platforms like LambdaTest that can help you run the QA test cases on AI-powered cloud device farms.

The advantage? You can leverage advanced AI and ML capabilities, along with an AI agent for QA testing, while running your tests on thousands of different browsers, operating systems, and devices through remote servers. Let us learn more with the example of LambdaTest:

LambdaTest is an AI testing tool that lets you perform manual and automation testing at scale with over 3000+ browsers, OS combinations, and 5000+ real devices.

Start Small

Since the entire idea of artificial intelligence is still new in the context of software testing, we would recommend you to start small with a noncritical test case. Depending on the results that you achieve in this process and also the stakeholder approvals, you can scale accordingly to other areas of the environment.

Use Human Oversight

Although artificial intelligence will help you eliminate human involvement in the test creation and execution process, it is very important to constantly review the LLM-generated scripts before executing them in the terminal.

By using this approach, you will not only be verifying the accuracy of the test cases but will also be ensuring that the test data does not develop any form of bias over a longer run.

Secure The Data

Whenever you are using cloud-based platforms to run LLM-based test automation, it is very important to secure your data and also ensure compliance. This is because you must ensure that you are not sending sensitive customer information in the form of test data over these servers.

The Bottom Line

Based on all the areas that you already discovered in this article, we can easily come to the conclusion that LLM-powered test automation is changing the way of quality assurance with the help of advanced test cases like AI E2E testing. It is bringing various changes to convert the manual and code-centric testing scenario to an intelligent and conversion-driven approach.

Since it is a completely evolving market, you can expect various new trends and innovations to come in the future. So, it will be a very good practice to keep your eyes open and constantly incorporate these coming trends and provide an even better experience to your customers.