Seven Benefits of AI-Driven Test Automation

What can AI-driven test automation bring to businesses? One needs to understand its key benefits.

How would you describe an enterprise’s current testing processes? Are they manually implemented or automated, or a combination of the two? Over the past few years, more enterprises have added test automation into the mix, It's easy to see why. Industry experts share seven key benefits of AI-powered test automation.

Manual testing can take hours and makes ongoing development more difficult unless resources can be deployed indefinitely. Additionally, accuracy is a challenge - testers are only human and can easily miss small changes. In businesses that rely solely on manual testing, software testing is error-prone and often encounters bottlenecks.

Limitations of Test Automation

Many enterprises are now combining automation with manual testing to speed up the process. Teams can execute test cycles faster by automating repetitive test cases, limiting manual labor to defining use cases, reviewing output, and performing final quality assurance (QA) overviews. However, test automation is never a "set and forget" situation. Each test environment had to be set up manually, requiring significant resources from the start. Then, if the test encounters dynamic or unusual data, problems arise that require manual fixing. Therefore, the speed advantage of automation may be offset by the time it takes to investigate and resolve problems that arise.

Testing user interfaces (UI) using coded automated methods presents further challenges. For example, a test might not detect buttons that change color or overlapping user interface (UI) elements. Although automation has improved the process to a great extent, coding testing still relies on complex setup, consistent maintenance, and a team of human testers to verify and fix. There is also a limit to the number of tests that can be run, and this number is further reduced when tests need to operate across browsers.

Beyond traditional test automation

As technology continues to advance, the testing process can be improved by integrating Robotic Process Automation (RPA), Artificial Intelligence (AI), Machine Learning (ML) and Natural Language Processing (NLP) and other technologies provide more ways to accelerate the company's development. The application of these new technologies enables companies to achieve higher quality testing with fewer resources, thereby reaping many benefits. With these new developments, the testing process can not only be completed more quickly, but can also be more accurate and reliable, saving businesses time and costs. This efficient testing method helps discover and solve potential problems, improve product quality and performance, thereby enhancing the competitiveness and innovation capabilities of enterprises. In addition, using these advanced technologies for testing can improve team productivity and satisfaction, and promote teamwork and communication. In summary, the main advantages of cloud computing-based test automation driven by intelligent artificial intelligence such as RPA, AI, ML and NLP

Recent developments have made codeless testing a reality rather than an unfulfilled marketing promise. For example, combining artificial intelligence with natural language processing (NLP) to allow sprint testing in plain English – much like human test scripts. Our approach is unlike any other on the market and it might be more accurate to call it natural language scripting because it converts commands written in plain English by testers into real code. The benefit of codeless testing is that it enables anyone on the team to generate tests, making the entire process more user-friendly and accessible. For example, natural language processing (NLP) allows robotic process automation (RPA) to translate simple commands like "click 'add to package'" so that the test software understands exactly what it needs to do.

Codeless AI testing is much faster than manual testing or traditional automation solutions because Testers save time generating code. This allows companies to increase their ability to run tests and deploy faster. Codeless tests can also run in parallel across multiple browsers and devices, which makes them easier to scale. Therefore, codeless testing technology can reduce time to market, which is key in today's competitive market.

No-code software helps companies reduce costs. Rather than hiring a large team to monitor and maintain automated tests, a small number of in-house experts can easily set up smart tests to run. Additionally, cloud-based software costs much more than on-premises software due to the lack of maintenance costs since the software owner is responsible for maintenance, not the user.

Manual testing is always susceptible to human error, and traditional test automation breaks down when encountering dynamic data. Using an AI-driven approach, it’s easy to test whether elements are the right colour, size and shape and are in the right place. We call it visual regression testing, and it can significantly improve the accuracy of your tests. This also applies to functional testing - using machine learning (ML), tests can understand how all the different elements should work and reduce test authoring time. These features save your team time on inspections and repairs, while improving test accuracy and quality.