The emergence of DevOps, Continuous Delivery and Agile is forcing organisations to transform their testing processes and innovate faster to retain their market-leading positions. They need to gain visibility and intelligence into their entire application ecosystem, from new dynamic cloud platforms to static legacy systems.

With release cycles shrinking from months to weeks or days and applications complexity increasing, testing teams need to test and provide feedback to the developers faster. QA can no longer be separated from development.

With the rise of AI, machine learning and robotic automation, QA and DevOps teams can leverage next generation AI-enabled solutions to orchestrate quality across the continuous testing pipeline, enabling a zero-touch and autonomous QA process.

AI transforms traditional QA

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. QA teams can trigger unattended test cycles, where the ‘AI brain’ identifies the defects and recommends remediation actions in run time, based on historical data and past events information.

Identifying the root cause of a defect is one of the reasons for delays in releasing new features. AI and ML algorithms can automatically analyse and identify the patterns of defects to find the root cause and enable the assignment to the correct team, reducing defect turnaround time and improving productivity.

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.

How AI impacts testing?

• 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 be resolved faster and so, applications can be released faster into the market.
• AI-driven test automation can manage faster and easier repetitive tasks with less or no human intervention, 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 allows 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, and can 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.