AI QA Tester vs. Traditional QA Tester: What’s the Difference?

Anushree Chatterjee

Artificial Intelligence (AI) is transforming Quality Assurance (QA) just like the Industrial Revolution did to manufacturing. QA testers are no longer alone in their quest for quality. AI-powered tools are emerging as powerful allies that can automate tasks, analyze vast datasets and even predict potential defects. This has given rise to a new breed of testers – the AI QA tester. Armed with advanced AI technologies, these testers are capable of ensuring software quality at a scale and speed that was previously unimaginable.

Let’s learn more about both these types of testers.

Understanding Traditional QA Testing

Let us understand the traditional QA testing, what it means, and who does it.

What is Traditional QA Testing?

You can view traditional QA testing as a grounded series of processes.

At a high level, these processes in SDLC are:

• Getting requirements, that is, what needs to be tested
• Designing and creating test cases
• Executing said test cases
• Validating outcomes
• Reporting and acting on observations

Traditional QA has itself gone through a lot many facelifts over the years. One of the foundational methodologies in traditional QA is the Waterfall model where testing occurs at specific stages after development. This model allows for a clear structure, but it can be inflexible if changes need to be made mid-process. This led us down the road to discover other methodologies like Agile and DevOps. In all these methodologies the processes remain the same; just the way they are done and when they are done tend to change.

Traditional testing also focuses on testing at different levels of the application:

• Unit testing to verify each block or unit of code
• Integration testing to verify if different parts of the system can function with one another
• End-to-end testing to check if the end user’s workflows are running smoothly

Once you know how you want to test (methodology) and where you want to do it (level of testing), we come to what aspect of the application we want to test. You must be familiar with different types of testing like:

• Functional testing to check functionalities
• Regression testing to ensure no new bugs have appeared in the system
• API testing to validate if APIs are working properly
• Security testing to make sure that your application is secure
• Performance testing for improving different aspects of application performance

Role of QA Testers in Traditional QA Testing

Humans play an integral role in traditional QA testing. Before you question this statement, let’s dig deeper.

Though manual testing gets a lot of boohoos, it’s still one of the most powerful forms of testing. Yes, it’s error-prone for sure, but pretty powerful still.

To help testers, we’ve now got automated testing that can take care of repetitive tasks quickly and accurately. Still, what the automation system should test is dictated by QA testers. These testers use their understanding of the application and its intended user experience to create scenarios that help find bugs and issues. In fact, types of testing like exploratory testing or usability testing are still done by QA testers.

This human involvement is crucial because testers can use their intuition and contextual knowledge which automated tools may overlook. Moreover, their cognitive abilities enable them to imagine test scenarios and build really effective test cases.

To summarize, traditional QA testers need to

• Understand requirements: They carefully analyze software requirements to ensure they are clear, consistent and testable.
• Create test cases: They design test cases based on the requirements to cover various scenarios and identify potential defects.
• Execute test cases: They use manual or automation techniques to execute test cases by interacting with the software to verify its functionality.
• Identify and report defects: They document findings discovered during testing and report them to the development team.
• Collaborate with development teams: They work closely with software developers. By understanding the architecture of the application they then provide feedback on its quality.

Understanding AI QA Testing

Let us understand AI QA testing, what it means, and who does it.

What is AI QA Testing?

All was going well with the traditional way of doing QA testing. But with everything going “online”, the pressure to deliver quickly has risen. Even the slightest glitch or performance issue can divert your customers to your competitors.

This exposed the difficulties in the traditional way of doing QA testing – human involvement. The human mind is one of the most powerful machines, yet it is prone to errors and is not nearly as fast as an actual machine. As AI is taking over the industry by a sweep, it is bringing in fundamental enhancements to traditional QA.

AI models that use different aspects of AI like machine learning (ML) and natural language processing (NLP), are being used to automate the processes that traditional QA was getting done through human involvement. For starters, people are now trying to use AI to do what a human can do – create intelligent test cases and provide intelligent insights on observations. Intelligent test maintenance is already being done by many AI models.

Thus, you can see that AI QA testing is no longer a black-and-white process where you set expectations and know exactly how the tester (human or machine) came to match an outcome. Another thing to note here is the automation tools being used. In traditional QA, though automation tools are used, they are usually devoid of AI. This makes them great for repetitive tasks but not for intelligent tasks like generating test cases. But over here, there’s a shift from automation to autonomous. When you use AI agents for different QA tasks, you train the models and let them do the “thinking”.

Role of QA Testers in AI QA Testing

The above section might have given you the impression that we are done with human testers. But that is not true. This is for two reasons.

• First – we are still developing AI to be as smart as an experienced human tester.
• Second – we need specialists who can train these AI tools, set them up and monitor their activities.

We still need QA testers but in different capacities. Here’s what they usually do

• Select and implement AI tools: They choose appropriate AI tools and configure them in a way that aligns with the project’s specific needs.
• Develop AI-powered test automation: They create automated test scripts using AI techniques to improve test coverage and reduce manual effort.
• Analyze test data: They use AI algorithms to analyze vast amounts of test data, identify patterns and detect potential defects.
• Predict defects: They employ ML models to predict the likelihood of defects occurring in different parts of the software.
• Optimize test processes: They use AI to optimize testing processes by identifying areas for improvement and suggesting new approaches.
• Maintain test suites: They use AI to do test maintenance activities for them.
• Collaborate with development teams: They work closely with software developers to ensure that AI-powered testing tools are integrated effectively.

AI QA Tester vs. Traditional QA Tester

Here’s a brief comparison of the two types of testers:

Tool Choices for AI QA Testers

Finding the right tool that uses AI to reduce manual efforts can be a challenge. You want a tool that delivers what it claims while satisfying your project and budget requirements. One tool that can give you the best out of AI is testRigor.

testRigor uses generative AI to make test creation, execution and maintenance easy. With this tool, you do not need to worry about a learning curve or spend too much time training an AI model. Just use plain English to write test cases using this tool and let testRigor do the rest. In fact, you can use the generative AI feature to let testRigor build test cases for you based on a description of the application to be tested. This makes testing inclusive and less time-consuming for all parties involved.

Since testRigor does not rely on implementation details of UI elements like CSS or HTML, it gives you stable test runs in dynamic environments as well. This makes test maintenance easier since test cases no longer rely on your knowledge of the code behind a UI element seen on the screen.

You can use this cloud-based testing tool to test across platforms and browsers with ease. testRigor also supports integrations with other platforms and services like those for CI/CD and databases.

Here’s a detailed list of what testRigor can do for you.

Conclusion

The role of the QA tester has evolved beyond simple manual testing. Today, testers are expected to adapt and ensure high quality at all times. With the help of automation and AI, this expectation is slowly becoming a reality. Both types of testing, AI and traditional, have their pros and cons. There’s a need for both of them.

It is important to understand that with changing times one must evolve. Since AI is the ‘in thing’, upskilling to become a AI QA tester is going to be helpful. However, the knowledge of a traditional QA tester is indispensable as it sets the foundation for developing the right QA testing acumen.

“We spent so much time on maintenance when using Selenium, and we spend nearly zero time with maintenance using testRigor.”

Keith Powe VP Of Engineering - IDT