How AI is Transforming Testing Today

Currently, there is much debate about how artificial intelligence (AI) will transform our lives. One of the technology’s key benefits lies in its power to transform traditional approaches to app development and software testing. Gartner identified AI-driven development as one of its top three strategic technology trends for 2019.

Digital transformation is dependent on better-quality software, and software testing matters more than ever. However, the growing complexity of our software-driven world means that new, smarter and quicker forms of testing are critical in the race to build better software faster. But in a hyper-connected and complex environment, this is no simple task, and this is where AI-driven testing can help.

I Transforming Testing

Like any new technology, there are exaggerated claims. However, when it comes to testing, AI is already delivering real improvements and making intelligent automation a reality. AI has moved far beyond an academic concept and is driving business benefits as opposed to hints to those organizations that have adopted it. Below are four of the key ways that it’s already transforming testing.

Auto-Generation of Test Scripts

There are undoubtedly complex test cases for which AI can’t auto-generate all the code. However, AI can easily auto-generate the code that clicks the buttons on the screen, fills out a form, logs into the app and other similar functions. Organizations that have deployed AI-driven testing are able to auto-generate 80% of code reliably, delivering substantial efficiency improvements. Along with auto-generation, AI achieves auto-maintenance: Tests are rebuilt when something in the digital product changes, ensuring continuous quality is maintained and further reducing the resource burden on human testers.


Test Optimization

If you’ve ever used Amazon, then you know that recommendation engine technologies are not that futuristic. AI can be applied to testing in the same way to help companies determine which test is most likely to find a defect based on the risk information gathered. Rather than taking a haphazard approach to testing, AI enables you to home in on the areas at risk—not only delivering efficiency gains but also ensuring a quality experience that delights.

Release Impact

Neural networks, combined with test history and data from current test runs, can predict how an upcoming release will impact users. For example, will customer satisfaction go up or down? Armed with this information, companies can make any adjustments necessary to ensure their users continue to be delighted by their digital experience.

Customer Impact

This is a key benefit of deploying AI-driven testing as you can determine the impact on the business from release updates. Neural networks, machine learning and other AI techniques can be used to understand how technical factors impact business performance. For example, are load times negatively impacting conversion rates? By understanding how releases will affect the business, you can make changes that will quickly have a positive impact.

What Does the Future Hold for AI-Powered Testing?

In addition to the core benefits that AI-driven testing is already delivering, organizations can expect to see additional benefits within the next 12 months that will further accelerate their ability to fully automate the entire testing life cycle.