The Impact of AI on QA Testing

AI and ML are the latest technologies coming to improve software development and testing. The technology is in its infancy yet can have a positive impact on test automation, defect management and test development.

Software or QA testing is a critical process that ensures applications meet customer needs and provide a positive customer experience. QA testing helps protect end users from experiencing defects by detecting them during the development cycle. Testing reduces the risk of the application failing once released to customers.

As the software development lifecycle (SDLC) continues to increase in complexity and releases occur more frequently, QA testing must innovate and adapt. AI (artificial intelligence) and ML (machine learning) are not magic silver bullets that automatically reduce costs and improve testing. Implementing AI and ML within a QA process over time leads to an autonomous QA workflow that provides a slew of positive benefits. However, with the perceived benefits comes implementation challenges and workflow interruptions.

This guide describes AI and ML technology and its possible benefits for QA testing quality and test coverage along with the challenges of implementation.

What is AI Technology?

AI and ML are areas of computer science that create technology or machines that perform tasks such as recognizing images, understanding languages and formulating decisions. ML takes AI a step further by enabling machines to learn over time and improve their knowledge and experience.

AI and ML have been dominating technology news for the past several years. Both have evolved from concepts to complex tools that enable human advancement in a variety of industries including software application development. AI and ML can play a significant role in software development including QA testing.

New tools are coming out every day that are empowering QA testing teams to leverage AI and ML to improve both test quality and coverage. The hottest sector is automated test development, but there are potential impacts to manual testing, defect management and user acceptance testing (UAT) as well.

Along with the potential to alter how QA testing is performed, the need to test them also increases. AI and ML can’t fully replace humans, but they can be used to improve QA testing quality, accuracy and coverage.

Where is AI Currently Impacting QA Testing?

AI and ML are impacting QA testing currently within automated test development. Test automation is critical for nearly all pre-release development testing. The problem with test automation is it’s difficult to develop and requires extensive script maintenance. Post-test execution also involves performing failure analysis for the test automation. Failure analysis determines if the failure was an actual defect or an issue with the test script.

Test automation has been a point of contention for many development teams because of the cost to implement and maintain a useful suite of automated tests. However, AI and ML technology provide test automation tools that can significantly simplify automated test development, execution and result analysis. Finally, the promise of test automation to enable continuous testing may be achievable.

AI- and ML-enabled test automation tools can:

• Develop automated test scripts
• Execute automated test scripts
• Perform failure analysis rapidly
• Perform test script maintenance

AI and ML technology may help organizations realize the business value of test automation by reducing the cost of test development, maintenance and failure analysis.

Why is AI Necessary for Improving Test Automation?

AI- and ML-enabled test automation tools are necessary for test automation success in the short and long term. Up to now, test automation has often fallen short of its promise to allow QA testing teams to test repetitive functions easily and effectively. Supporting test automation execution alone became a full-time job. Many organizations abandoned test automation based on its inability to handle complex tasks or run autonomously, and its constant need for script maintenance.

AI- and ML-enabled test automation tools make automated test development plausible for any size team. Continuous testing that relies heavily on test automation becomes a workable reality.

How Can AI Improve Software Testing?

AI and ML can improve software testing by adding test coverage, improving accuracy and enhancing maintainability. Both manual and automated test cases can be developed with AI and ML. The current focus is on improving test automation both in the quality and complexity of test scripts and ease of maintenance. Those two factors alone make AI and ML technology worth checking into.

AI-related technology helps both developers and testers save time and energy while adding to test quality. Many times, developers don’t have time in the development cycle to create unit or integrated unit test scripts. Even if they do, once the code changes they don’t have the time built in to create a new test.

The same applies to testing and test development. Automated testing tools are great at simple tasks, but with AI-related technology actual end-to-end test automation is possible. Not only can AI-infused automation tools help create more reliable test automation, but they can help create test automation that can simulate customer workflows.

With proper training and process implementation, AI and ML will help QA testers leave the tedious, repetitive testing to the tools. Aside from reviewing AI’s work, of course. AI-related technology is only getting started, it’s not at a state where an organization can plug it in and use it to cover all testing. The technology may eventually get there, but for now AI and ML require human oversight and review.

Even with the need for human oversight, AI-powered test automation can cover 80% or more of testing. Leaving 20% or less of testing that requires human creativity, reasoning, and judgment.

Other benefits of AI and ML technology in software testing include:

• Full regression test suite automation
• Defect analysis and prediction
• Troubleshooting to locate the root cause of defects
• Rapid test failure analysis
• Automatic script maintenance
• Automatic defect creation
• User acceptance testing (UAT)
• Customer requirement reviews
• Automated API test development and execution

Full regression testing rarely occurs due to time and resource constraints. Many regression test executions test only high-priority sections of code, or only smoke tests are executed.

AI- and ML-powered test automation can execute 100,000 tests, perform failure analysis and enter defects—all in a single day. Now, even after factoring in time for a human tester to review defects and test results to ensure accuracy—it’s still possible to fully test an application more often during rapid development cycles.

Even UAT will be leveraged with AI and ML technology in the future. Imagine having customers and product managers test the software. The AI-related technology can observe user actions and create test scripts. Defects can be reported automatically and reviewed by human testers.

Are There Challenges to Implementing AI for Testing?

AI and ML can improve almost all aspects of software testing, but they come with challenges. Many development and testing teams are hesitant to implement AI and ML.

Challenges of implementing AI and ML include:

• Time. It is time-consuming to learn new tools and incorporate them into an existing workflow.
• Learning new failure modes and how to review AI- and ML-created tests and test results.
• Cost. Many AI and ML tools are expensive.
• Re-skilling and re-learning require time and time is at a premium for most development teams.
• Trust. AI tools are not typically plug and play so an immediate ROI is not possible.
• Fear. QA testers and developers fear being replaced.

Consider creating an AI/ML development and test strategy. Involve the team in choosing where to start with AI and how to approach process integration. Be transparent with planning and implementation. Provide re-skilling or training for roles impacted by AI tools. Remember, AI and ML tools aren’t ready to run autonomously 100%. Humans are required to review the quality and accuracy of the work. Additionally, humans are required for complex testing that requires reasoning and decision-making to train the AI and ML properly.

Try out AI and ML in one ways now achievable. If you don’t have test automation, get a tool and see what the team can create with AI.

Another popular starting place is using spidering AI. Spidering AI writes tests for an application by crawling through it. As the AI/ML tool crawls, it collects data, downloads page code and even measures performance. The tool can continuously run through the steps and discover all the possible variable usage patterns for the application.

As it continues to run through, the tool identifies defects between test executions. At this point, a human with application domain knowledge would need to validate if the defect was truly a defect or an expected change.

Does implementing AI mean you can get rid of testing? No. It means you can leverage AI and ML technology to reliably take on testing all the tedious, repetitive and regression test executions continuously. It means test automation may no longer require human testers and developers spending hours or days in failure analysis and script maintenance. Test automation may become a positive ROI after all.

Additionally, AI and ML may help identify defects and provide the root cause for developers. The future of AI and ML is not fully known. It will certainly change how QA testing and software development is done. However, customers are still human and there are human traits and foibles that cause application defects. Human testers and developers won’t be replaced anytime soon, but AI and ML can certainly improve application quality for customers.

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