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How AI-Powered Test Automation Is Enabling Digital Transformation?

The pandemic has meant an acceleration in digital transformation by months or even years. The move to digitization has accelerated, and the benefits will be permanent.

KPMG Study

There is no doubt that the Pandemic has incrementally accelerated the push towards digital transformation for organizations across every industry. The new realities brought about by the Pandemic have made it essential for organizations to embrace next-gen digital technologies such as Automation, the Internet of Things, AI/ML, among others to ensure business growth even during these trying times. Today, advanced technologies are enabling organizations to transform operations, catalyze performance, ensure quality, and optimize resource costs.

However, digital transformation is not solely about embracing new digital technologies. It is also about delivering a seamless, digital experience to the end-users. Organizations need to fortify their digital offerings through a robust, secure, and seamless platform that provides tailored experiences to the users based on their individual needs. However, ensuring optimal product quality in a fast-paced, hyper-connected, and complex world is not an easy task. While traditional test automation has enabled test teams with a smarter and quicker means for delivering high software quality, AI-powered tools can drive its capabilities to the next level and enable smooth digital transformation for organizations.

How AI is transforming QA?

Traditional test automation solutions enable test teams to control test execution and compare test results against expected outcomes. While such solutions can test and deliver results automatically, there remains a need for human supervision. Without human supervision, traditional test automation tools fail to identify the tests to run, so they end up running every test or a predetermined set of tests.

Modern test automation solutions, powered with AI and ML, allow organizations to take a giant leap towards zero-touch QA by making testing more strategic, reliable, and less human-intensive. AI-driven algorithms can completely mimic human intelligence and allow organizations to move beyond the scope of simple rule-based automation. While ML, through self-healing, can automatically update the understanding of test scripts by interpreting the impact of the changes on associated business processes.

AI-powered test automation solutions allow organizations to test faster and better while reducing costs and human dependencies. It enables organizations to rapidly implement/release products, ensure usability/user-friendliness for new technology implementations, thereby championing the cause of digital transformation.

How AI-Powered Test Automation is Impacting Software Testing?

In 2021, artificial intelligence (AI) augmentation will create $2.9 trillion of business value and 6.2 billion hours of worker productivity globally.

Gartner Research

AI is a technology with immense transformative potential. The introduction of AI to test automation has allowed organizations to remove the limitations of traditional test automation. It delivers more value to the tester and developers alike. AI has imparted an incredibly positive impact on various software testing use cases, such as:

  • API Testing: API testing makes it easier for teams to find the root cause of a bug. It delivers deeper insights into an application. AI-powered test automation solutions enable organizations to convert manual UI tests into automated API tests. This lowers the threshold of the skills needed for API testing, allowing organizations to build a more sustainable API testing strategy.
  • UI Testing: Allows organizations to automate their UI testing instead of doing it manually. With AI-powered UI testing, teams can attain more accuracy in comparison with manual UI testing. Parameters like GUI size difference and combination of colors are hard to detect for manual testers.
  • Regression Testing: Enables organizations to run the entire test suite promptly after every change, however minor it may be. AI can prioritize and re-target regression tests to test high-risk areas within short run-times.
  • Image-Based Testing: Simplifies visual validations involved in image-based testing by leveraging the ML capability of AI. With AI-powered solutions, image-based testing becomes a breeze.
  • Unit Testing: Reduces flaky test cases while conducting unit testing. AI-powered tools also help with the maintenance of unit test scripts.

How is AI-powered Test Automation enabling Digital Transformation?

Digital transformation empowers an organization to tackle the ever-growing business user expectations, adopt new technologies to match with the fast-moving market trends, and gain a competitive advantage in the marketplace. However, to gain a competitive advantage with digital transformation, organizations need to move fast and respond to the changes more readily. But, in most instances, the pace of digital transformation initiatives is slowed by the complexity of the technology environment and the inability to swiftly implement necessary changes.

For organizations seeking to become more agile and accelerate their strategy execution, an AI-powered test automation solution becomes an effective way to speed up digital transformation. As discussed above, the utilization of AI in various testing scenarios is helping organizations accelerate speed, improve quality, reduce costs and mitigate complexities. Some of the discernable benefits of AI-powered test automation towards digital transformation include:

  • Auto-Generation of Test Scripts: Helps teams with the auto-generation of test codes that performs all required functions, such as form fills, click buttons, app logins, and more. While there will be complex test cases for which AI-powered test automation solutions can’t generate code, but they can auto-generate nearly 80% of the required code reliably, thereby enhancing the efficiency of testing teams significantly.
  • Auto Test Maintenance: Evaluates changes to the code and fixes several existing tests that don’t align with those changes, especially if those code changes are not too complex. Updates to UI elements, field names, and the like need not break the test suite anymore. Certain AI-powered solutions monitor running tests and try out modified variants for failed tests by choosing UI elements based on the best fit. Such solutions can also verify test coverage and supplement the gaps if needed.
  • Precision Testing: Leverages advanced AI and ML technologies such as Computer Vision to enable testing with higher precision. AI-powered solutions can suggest to users with precision the smallest number of tests that need to be run for a piece of changed code. Such solutions can analyze the existing test coverage and flag areas that have little/no coverage or point out areas within an application that might be at risk. Consequently, AI-powered test automation solutions strengthen the quality of new technology implementations.
  • Risk-Based Testing: Empowers testers with a smarter, faster, more cost-effective way to conduct risk-based testing. While risk-based testing done by humans is effective, but adding machine learning algorithms and automation can bring stunning results. AI-powered test automation drives better selection and prioritization with data, not tester instinct or experience, to improve accuracy and reliability. Such solutions can identify the highest-risk, highest-impact areas early so testers can put them on the top of the list.
  • Continuous Testing: With updates rolling out on almost every alternate day to facilitate rapid releases, there is a need to streamline testing by making it smarter and more efficient. AI makes the testing process smarter since QA teams can trigger unattended test cycles in which defects can be identified based on insights that are picked from historical data sets and past events. AI-driven algorithms can completely mimic human intelligence while ML automatically updates the test scripts, eliminating unstable test cases. AI-based engines can ensure that only a robust code progresses from one stage to the next and ML-specific algorithms extract patterns by accessing data to make predictions. All this helps organizations succeed with DevOps.
  • Competitive Edge: Helps organizations gain a competitive edge. Various AI capabilities such as ML and neural networks can be used to understand how various technical factors are impacting the user experience and business outcomes. For example, AI can detect whether a new implementation is negatively impacting the load times and could lower conversion rates upon release. By delivering predictions on how releases will affect the business, AI-powered solutions empower organizations to make course corrections and make their digital initiatives successful.

Embrace a Better Way of Testing

Testers spend 17% of their time dealing with false positives and another 14% on additional test maintenance tasks.

Tricentis Research

An AI-powered solution with its next-gen capabilities enables organizations to venture beyond the usual limitations of traditional test automation. Its auto-generation and auto-maintenance capabilities can help test teams save valuable time and effort and put it towards tackling more complex requirements. It helps organizations release/implement new products with more confidence while optimizing the testing costs. With AI-powered test automation, organizations can swiftly fulfill the user expectations, plug the technology gaps, meet business goals, and champion digital transformation.

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