The introduction of AI and ML will help overcome increasing testing and QA challenges, streamline testing processes and make testing smarter and more efficient. AI also aims at minimizing the repetitive work but with added intelligence.

Today, enterprises must spend hours writing and maintaining test scripts, keeping them from realizing the true value of continuous testing. Using AI and Machine Learning to create and maintain testing assets, allows enterprises to realize the full benefit of autonomous testing at scale. It is estimated that around 70% of a manual tester’s work can be automated leveraging AI and without a huge initial learning curve required. It translates to profit sooner and results in an increased ROI.

In the World Quality Report 2018-19, analysts state that businesses understand the importance of AI and wish to leverage the technology. 80% of enterprises are slowly investing in AI.

How is AI impacting testing?

• AI-driven automation tools can perform the tests with less or no human intervention. When a defect is identified, the system can alert the tester, why it failed and what could be a potential fix for it. Some may even have the ability to fix the issue!
• Leveraging AI, testing becomes faster with improved quality and optimized risk, as it is able to process large amounts of data to identify defect trends and predict future events.
• DevOps and QA teams will have actionable Continuous Feedback which means that the defects will resolved faster and so, applications can be launched faster into the market.
• AI-driven test automation can manage faster and easier repetitive tasks to meet the continuous delivery demands for increased productivity.
• It is less expensive than manual testing as it reduces the reliability on manual testers by reducing the resources and also the related intensive costs.
• AI is ideal for Regression testing to compare and identify if what used to work is still working.
• AI in Test Automation allow tests to be updated automatically every time there is a change in the system, maintaining all the affected tests automatically in one go! This means that maintenance costs are reduced dramatically.
• AI drives autonomous test creation leveraging technologies such as natural language processing and advanced modeling.
• It can also recommend what tests need to run and the optimal user journeys to deliver the best user experience.
• Learning from production data. Real user data can be used to create an automated test and with the help of AI, we can learn how the customer is using an application.

AI-driven test automation (Intelligent Automation) is here to combine the best of automation approaches with AI and ML. This combination brings superior results and focuses mainly on three aspects – to eliminate test coverage overlaps, optimize efforts with more predictable testing and move from defect detection to defect prevention. 

AI helps to unlock the power of data (like test assets, defect logs, test results, production incidents, event data etc.) and drives automation and innovation, improving QA efficiencies beyond the reach of traditional test automation approaches.