Last year, the global pandemic brought about a major shift in how businesses operate, introducing new remote working challenges and accelerating digital transformation at an unprecedented rate.
Organizations are now in a race against time to create high-quality software to advance their digital transformation initiatives. However, ensuring optimal software quality in a fast-paced, hyper-connected and complex world is no easy task.
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While traditional test automation has given test teams smarter and faster ways to deliver high software quality, tools powered by AI can take its capabilities to the next level.
How AI is transforming test automation
Traditional test automation provides tools to control test execution and compare test results to expected results.
While these tools can test and deliver results automatically, they still require human supervision. Without human supervision, traditional test automation tools cannot identify which tests to run, so they end up running each test or a predetermined set of tests.
When powered by AI, a test automation tool can examine the current state of tests, recent code changes, code coverage, and other related metrics to intelligently decide which tests to run, and then trigger them automatically.
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AI allows test automation to go beyond the scope of simple rule-based automation. It uses AI algorithms to efficiently train systems using large data sets.
Through the application of reasoning, problem solving and machine learning, an AI-powered test automation tool can mimic human behavior and reduce the direct involvement of software testers in tasks banal.
How AI Impacts Various Software Testing Use Cases
AI is changing software testing in several ways. It removes many limitations of traditional test automation and provides more value for testers and developers.
It enables organizations to test faster and better while reducing costs and human dependencies. AI has conferred an incredible positive impact on most software testing use cases, including:
Testers can use RPA tools (an application of AI) to reduce flaky test cases while performing unit tests. Such tools can also help with the maintenance of unit test scripts.
AI-based test automation tools can convert manual UI testing into automated API testing. This reduces the requirement for specialized testing skills for the process and allows organizations to create a more sustainable API testing strategy.
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User interface tests
Ensures greater accuracy compared to manual testing. It is difficult to manually detect parameters like GUI size difference and color combination, which can be easily identified with AI.
Allows test teams to run the entire test suite in a timely manner on every change, no matter how small. AI can prioritize and retarget regression tests to test high-risk areas with short execution times. •
The visual validations involved in image-based testing can be simplified with the ML capability of AI. Automated visual validation tools facilitate image-based testing.
How does AI-powered test automation help organizations?
Like any new technology, there is a lot of hype surrounding the testing of AI-powered software. The use of AI in various test scenarios brings significant improvements and makes intelligent test automation a reality. AI-powered test automation helps organizations reinvent software testing and deliver real business benefits. Some of its main benefits for organizations include:
1. Automatic generation of test scripts
AI-powered test automation helps teams automatically generate test codes that perform all required functions, such as click buttons, form fills, app logins, and more.
There will be complex test cases where AI-powered test automation tools cannot generate code, but they can automatically generate more than 80% of the required code reliably, dramatically improving the productivity of operators. test teams.
In addition, AI also helps with automatic maintenance to ensure continuous quality while reducing the burden on human testers.
2. Optimization of the test process
AI is the force behind product recommendations on Amazon or shows suggested by Netflix. An AI-powered recommendation engine enables marketers to deliver relevant product recommendations to customers in real time.
The same approach can be applied to simplify software testing. AI can suggest tests with the highest probability of finding bugs, based on risk information, removing guesswork from testing and allowing teams to focus on actual risk areas.
3. Measuring the impact of releases
AI-based test automation tools can predict the impact of an upcoming software release on end users.
By taking advantage of neural networks and analyzing test history and data from ongoing tests, the tool can predict whether customer satisfaction will increase or decrease. Armed with this information, organizations can similarly adapt and ensure that their customers remain happy with the user experience.
4. Offers a competitive advantage
AI-powered test automation tools help organizations gain competitive advantage. Various AI capabilities such as ML and neural networks can be used to understand the impact of various technical factors on user experience and business results.
For example, AI can detect if a new implementation is negatively impacting load times and could reduce conversion rates when released.
By providing predictions of how releases will affect the business, AI-powered tools allow organizations to make course corrections to have a positive impact.
5. Allows productivity and cost savings
A recent study found that testers spend 17% of their time processing false positives and an additional 14% on additional test maintenance tasks. An AI-powered tool with its automatic generation and maintenance capabilities can help test teams save valuable time and effort and spend them on solving complex requirements.
It can also help organizations optimize testing costs by reducing human dependence on mundane testing tasks.
Adopt AI technologies
It’s pretty clear that AI-powered test automation is not a passing fad. These tools allow organizations to better understand and adapt to constantly changing customer expectations. Rather than taking a wait-and-see approach, it’s time to embrace the innovation AI unleashed in test automation.