Seven Benefits of AI-driven Test Automation
How would you describe your current testing processes? Are they manual, automated, or a combination of both? Over the last few years, more organizations have been adding test automation into the mix, and it’s easy to see why. Adil Mohammed, founder and CEO of Virtuoso, shares seven key benefits of AI-driven test automation.
Manual testing can take hours and make continuous development difficult unless you have access to unlimited resources. Accuracy is also an issue – testers are only human and can easily miss small changes. Software testing is subject to error in organizations that rely solely on manual testing and often presents a bottleneck.
The Limits to Test Automation
Many businesses are now combining automation with manual testing in order to speed up the process. Teams can carry out test cycles faster by automating repeated test cases, leaving the manual limited to defining the case, reviewing outputs, and carrying out a final quality assurance (QA) overview. However, test automation is never a case of ‘set and forget.’ Each test environment must be set up manually, requiring significant resources from the outset. Then, if the tests meet dynamic or unusual data, problems can occur that need humans to fix. The speed benefits of automation can therefore be canceled out by the time taken to investigate and resolve issues that arise.
Testing User Interfaces (UIs) using a coded automation approach comes with further challenges. For example, the test may not pick up on a button that’s changed color or on overlapping UI elements. Although automation has improved the process considerably, coded tests still rely on a complex setup, consistent maintenance, and a team of human testers to verify and fix. There is also a limit to how many tests can run, with this number reducing even further when tests need to operate cross-browser.
Shifting Beyond Traditional Test Automation
As technology develops, we’re seeing more ways in which testing processes can accelerate companies’ growth. For example, by combining Robotic Process Automation (RPA), Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Programming (NLP), firms can carry out better-quality testing more quickly and with fewer resources. I’ve pulled together some of the concrete benefits of these new developments below.
Key Positives of Intelligent AI-driven, Cloud-based Test Automation
1. Codeless testing means anyone can script
Recent developments have made no-code testing an actual reality rather than a marketing promise that failed to deliver. For example, at Virtuoso, we pair AI with NLP to allow for in-sprint testing authored in plain English – much like a manual test script. Our approach is unlike anything else on the market, and it’s perhaps more accurate to call it Natural Language Scripting as it takes a tester’s commands written in plain English and translates them into real code. The benefit of codeless testing is that it gives anyone in your team the power to generate tests, making the whole process more user-friendly and accessible. For example, NLP allows simple commands like “click ‘add to bag’” to be translated by RPA, so the testing software understands exactly what it needs to do.
2. Test faster, ship faster
Codeless AI testing is significantly faster than either manual testing or traditional automated solutions, as testers save time generating code. This allows companies to increase their ability to run tests and deploy more quickly. Codeless tests can also run in parallel and across a multitude of browsers and devices, making them easier to scale. No-code testing technology can therefore boost time to market, which is key in today’s competitive market.
3. Reduce costs
No-code software aids businesses with keeping costs down. Instead of hiring large teams to monitor and maintain automated tests, a small number of in-house specialists can easily set intelligent tests to run. Plus, cloud-based software is much more cost-effective than on-premise software due to a lack of maintenance costs since the software owners run maintenance, not the users.
4. Boost accuracy
Manual testing is always subject to human error, and traditional test automation falls down when it encounters dynamic data. Using an AI-driven approach, you can easily test that your elements’ colors, sizes, and shapes are correct and in the right place. We call this visual regression testing, which significantly boosts your tests’ accuracy. This also works for functional testing – using ML, the test understands how all the different elements are supposed to work and decreases test authoring time. These features can save your teams hours of checking and fixing while simultaneously improving the accuracy and quality of your tests.
5. Test continuously
AI-driven testing fits in with Continuous Integration/Continuous Delivery (CI/CD) and the Software Development Lifecycle (SDLC). Organizations can set tests to run not only intelligently but continuously. You can set conditions for your tests, for example, to trigger an action if a certain outcome occurs. And you can run multiple tests in parallel as often as you need to ensure that your website is always bug-free and of the highest quality.
6. Carry out (almost) zero maintenance
You’re unlocking the power of self-healing tests by implementing AI-driven test automation. The technology takes into consideration all of the element IDs, so if one data point changes, then it has a model to compare to and can fix itself. Crucially, the test knows the difference between data that’s supposed to change and a broken test.
7. Enhance API testing
AI can also support end-to-end testing by recognizing relationships and patterns between the front-end interface and the back-end. Virtuoso’s functional API tests ensure both parts of a website are communicating properly, with AI flagging if any wires get crossed during the exchange of information.
AI-driven Automation Delivers a Competitive Edge
At a time when rising inflation, soaring company costs, and a tight labor market are inflicting unprecedented pressure on companies, AI-driven test automation offers a golden opportunity to ship faster and improve quality. By scaling up their potential to test and develop, businesses can deploy quicker and be first to market. This is a particular advantage for firms with fewer resources that are unable or unwilling to hire large testing teams. Through AI-driven automation, any organization can tap into unparalleled business value and secure a competitive advantage.
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