A Practical Guide on Improving QA Testing with Gen AI

What are the types of Gen AI Tools?

Gen AI is created from different algorithms that help make content including audio, code, images, text, simulations, and videos. Text generation is currently the most popular use of Gen AI, followed closely by code-generating tools. According to the Future of the Quality Assurance Report, 80% of Gen AI users generate text using tools like ChatGPT or BingChat. 44% use code generation tools like GitHub Copilot, OpenAI Codes, AlphaCode, or other options to develop code.

The most popular Gen AI models or types include:

• Autoregressive models
• Generative adversarial networks (GANS)
• Transformer based models
• Recurrent neural networks (RNNs)
• Variational autoencoders (VAEs)

Creating and managing Gen AI models requires significant computing resources to handle the mathematical computations involved. Each operates as a predictive algorithm. The current problem with Gen AI is it’s reportedly only 60% reliable. There are published reports concerning the bias and ethical issues that cannot be disregarded. Additionally, privacy is a known issue that will likely require implementing controls and regulations in the near future.

Using Gen AI in QA testing offers significant benefits, such as enhancing speed and reducing the maintenance of various testing aspects. This allows testers to focus on writing tests and maintaining test results without worrying about other testing processes, provided humans are available to monitor for bias in test results.

How is Gen AI being used in QA Testing?

Here are some of the use cases where Gen AI is used in testing can be leveraged for:

• Test Data Generation: Teams are using Gen AI to create test data on demand that includes fast testing access to varied data patterns and relationships that match production data.
• Test Case Development: QA teams use Gen AI to analyze requirements or user stories and create additional test cases or scenarios.
• Test Log Analysis: Use Gen AI to review error logs and identify defects not visible in the UI.
• Test Reporting: Generate test execution and status reporting easily without the typical overhead to gather metrics data and create reports for stakeholders and managers.
• Visual Regression Testing: Gen AI can generate automated tests to cover visual regression testing. Use it to test for defects after every code fix or between releases to ensure no defects affect the visual user experience.
• Testing Edge Cases: Create test cases for all edge conditions that human testers may not be aware of in the architecture.
• Code Generation and Automated Test Scripting: Automatically generate automated test scripts or code snippets to use. Snippets can create deeper test automation coverage or intentionally inject issues for error testing.
• Defect Identification: Once a Gen AI learns the application’s expected behavior, it can analyze the functionality and help identify defects.
• Security Testing to Identify Known Vulnerabilities: Gen AI can be useful for generating standard basic security testing for injection attacks. The potential exists to create other known security attacks on endpoints, APIs, and databases to ensure cyber security protocols are implemented correctly.

Tools like KaneAI, a GenAI native QA Agent-as-a-Service platform, provide a comprehensive testing solution that integrates seamlessly with Generative AI tools, serving as a one-stop platform for all your testing needs. KaneAI stands out by enhancing the capabilities of these Gen AI applications. It is specifically designed to address complex testing requirements, such as generating precise test cases and analyzing intricate error logs.

With the rise of AI in testing, its crucial to stay competitive by upskilling or polishing your skillsets. The KaneAI Certification proves your hands-on AI testing skills and positions you as a future-ready, high-value QA professional.

By incorporating KaneAI, teams can leverage its advanced features to streamline test data generation, improve test reporting, and identify defects with greater accuracy. KaneAI’s sophisticated algorithms and context-aware insights make it an invaluable asset for achieving thorough and effective testing across various scenarios.

Benefits of Gen AI for QA Testing Teams

Imagine QA testing processes as a dam that holds back water, like Hoover Dam outside of Las Vegas, Nevada. In most organizations, the QA testing team is a force of nature that opens the flow of releases. All the water isn’t released at once; it’s released like software code at certain times when the requirements or user story acceptance criteria for an iteration are satisfied.

Now, think of the QA testing team as a team of engineers manually turning gears and measuring water output 24/7 and 365. When demands for water increase and more rapid releases are needed, the team can become overwhelmed. Release schedules may be moved, or worse, releases are deployed to customers without adequate or effective testing.

Gen AI is a bridge that helps the QA testing team achieve greater efficiency, improve workload stress, and increase testing coverage. Faster delivery to customers, but with higher quality testing and application releases. According to a recent Future of QA report, 30% of organizations believe Gen AI can significantly boost QA team productivity and application release quality.

Benefits of integrating Gen AI into your QA testing processes:

• Comprehensive Test Coverage: Test case development based on requirements or user stories paired with AI to ensure no test scenarios are missed and to increase the team’s testing breadth and depth.
• Augmented Testing Efficiency: Leveraging AI’s predictive analytic features to optimize both manual and automated tests. Add application experience to a QA testing team by using predictive analytics to create tests for all possible executable paths.
• On-Demand Reporting Simplicity: Quick and efficient reporting is on demand and doesn’t overwhelm testing team resources.
• Streamlined Defect Analysis: Enhanced defect analysis that includes failure analysis. Human QAs can easily do both tasks, but they take excessive resource time and take vital QA testers away from focusing on quality testing.
• Accelerated Automation Mastery: Faster test automation scripting and maintenance. Cut the automation testing learning curve by using Gen AI to create scripts, assist human testers in creating valid scripts, and expand test coverage.
• Resource-Efficient Security and Performance Testing: Use Gen AI to perform security and performance testing efficiently and without extra resources.

Gen AI also improves testing team accuracy. It can keep both manual and automated tests updated. No more need to constantly update test scripts. The higher the quality of the tests, the fewer defects that escape to customers.

To fully leverage the benefits of Gen AI, it’s essential to focus on building high-quality teams that emphasize the right people, processes, and proof. Strong leadership and collaboration are key to effectively integrating AI into testing workflows.

Watch the following video to learn more about building high-quality teams for QA leadership.

Strategies for Integrating Gen-AI into Your Testing Process

If you’ve been in software testing for a while, you know Gen AI faces challenges similar to test automation. QA testers feared massive layoffs and being replaced by test automation tools. There was a lot of press about the end of QA testing as a career.

That was 25 years ago. Today, Gen AI faces the same challenge – developing trust.

Organizations have to develop trust in Gen AI and learn to use it for their benefit. The technology needs to mature and prove its value outweighs the challenges in integrating it into established testing processes. It’ll take time to fully develop Gen AI’s potential within software testing.

Rather than waiting, consider integrating one or more Gen AI features into your software testing processes now. Is your QA testing is not working? Is testing too slow, reporting not clear, test data too challenging to create? Pick one or more areas and give Gen AI a try. Start with a pilot project and measure the benefits. If it’s an improvement, gradually add it where it makes sense.

Integrate Gen AI into your QA testing process by taking the following steps:

• Analyze where there are issues with QA testing processes.
• Match your issues with the benefits of Gen AI as it is now.
• Create a pilot project with a dedicated, experienced team interested in improving the QA process.
• Provide the pilot project team with training in the selected Gen AI tools. Ensure the project objectives are clear and realistic.

Next, select where to start based on priority from your issues list. For example, if the customer continuously reports defects in tested releases, use a Gen AI tool to create test cases and scan for defects. Is the team struggling to generate enough realistic test data for testing? Use a Gen AI tool to create test data sets and populate the test environment with new, refreshed data whenever needed.

If your QA testing team struggles with automated test development, test maintenance, or failure analysis – use an automation tool with Gen AI and see if it improves. Test automated development done well goes a long way to increasing application quality release after release.

Don’t forget to have human testers monitor the accuracy of the tests and review defects based on the examples above. Determine metrics to use to measure the pilot project. Consider also having testers assess the Gen AI tools and determine if the benefits claimed are possible over time. Monitor progress and make changes if needed. If you can select one or more employees to build Gen AI skills, empower them to make changes and try new approaches.

Watch the following video to learn on more on how Gen AI impact QA testing strategies.

Gen AI and the Future of QA Testing

Gen AI is only beginning to impact the future of QA testing. As a QA tester, consider learning as much as possible about Gen AI as it matures and improves. Gen AI requires human oversight to manage results quality, reliability, and data privacy challenges. As Gen AI matures, it’ll become regulated and standardized.

As Gen AI matures, it’ll become regulated and standardized. Emerging frameworks discussed in MCP and AI Agents are already shaping how AI systems coordinate tools, maintain testing context, and automate complex QA workflows.

Now’s the time to get started and realize the benefits of integrating the parts of Gen AI that can improve your QA testing team’s productivity, accuracy, and overall quality. Gen AI is the future of QA testing and software development. Investing now is an excellent opportunity to get ahead of the curve and influence Gen AI’s future direction. By combining Gen AI with a platform like TestMu AI, you can create a future-proof QA testing process that delivers exceptional results.