We are in a Digital Testing Epoch. Are you ready?
Digital twins are a growing area of enterprise investment, with Gartner predicting that the use of digital twins will triple by 2022. The technology, a digital representation of a physical object or system, offers companies a host of benefits, among them improved productivity, reduced down time, reduced risk and improved performance.
In the Internet age, making the best use of technology investments, rapidly spotting and addressing any performance issues or other roadblocks and ensuring customers have a high-quality experience that delights are perennial goals. Yet, until very recently, companies have been going about it all wrong.
Yupp, you’ve guessed it, HTTP. HyperText Transfer Protocol (HTTP/HTTPS) is the go-to language for IoT devices, in that it’s the protocol used when devices talk to each other via the internet. I know what you are thinking: what about everything we’ve heard about security in the news? Surely there is nothing more important than security in IoT? Security was a close second for me when deciding the most important thing about testing, but here’s why HTTP pipped it to the finish line…
We operate in a continuous delivery world in which a seamless customer experience is paramount. Regardless of whether you’re a global Fortune 500 organization or a fast-growing startup, failing to deliver a digital experience that delights your users is a critical mistake you can’t afford to make.
Businesses want software that delights end users. Customer experience is the priority. But even as the requirement for ever better experiences grows, actually delivering on that requirement is getting harder.
There are two things you should know about the latest release of Eggplant AI, our intelligent test automation solution.
1: It will give you much greater insight into the quality of your releases.
2: It will make your testing process more focused and more efficient.
Quality assurance (QA) used to be a compliance activity. You were releasing a product and needed to test it and stamp it “approved.” QA was about testing that the code worked. You might manually test the code. You might have even tried some automation — coding a set of test scripts that would try to capture regressions or errors that you had eradicated in the past, but which somehow crept back in. All in all, you were reasonably satisfied that you achieved a level of test coverage that met your goals. Then, you put your code into production and crossed your fingers that nothing went wrong. And if it did, you tried to fix it as quickly as humanly possible.
It used to be that software testers could test their applications on just one platform, and only have to worry about testing that the code worked.
We recently co-hosted a webinar with Bloor Research about the Future of Testing, and in it, we conducted an informal poll about artificial intelligence (AI) and testing. When we asked what everyone thought the biggest advantage was to incorporating AI into a test automation strategy, attendees overwhelmingly selected team productivity and efficiency.