With the Shift-Up series thus far, we have explored the importance of testing and thinking as a customer. The basic premise is that we need to add another dimension to Quality Assurance other than Shift-Left and Shift-Right. This new dimension focuses on how your customer is actually using your application and if the intersection of your application, customer behavior, and your company’s business objectives all align.
People make mistakes. Human behavior so often falls short of ‘expected standards’, it begs the question why we hold ourselves to such standards at all. Too often, we build systems and processes on the implicit assumption that the people using them will be rational, infallible, and consistent. Of course, the truth is that most of us are anything but.
Our general fallibility is obviously closely tied to AI and test automation. Automated testing is immune to the unintentional biases and lapses in concentration that affect human testers.
The purpose of software testing has been steadily shifting from ‘does it work?’ to ‘does it deliver the required business outcomes?’, with an increasing focus on end-user requirements. It’s no longer enough to rely on metrics such as x% of tests passed. Now we have to understand the impact on the people using the product and the wider implications for the organization. There is a need to bridge that gap between meeting testing objectives and actually meeting customer expectations.
To keep up with DevOps, testing and QA teams typically adopt a shift-up approach to move quality further up the software development lifecycle. The goal is to complete system testing, integration testing, and user acceptance testing (UAT) to ensure a bug-free release. While product quality has a direct correlation to increased revenue and positive business outcomes, this isn’t enough in the 21st-century marketplace. QA’s job isn’t just to de-risk applications by finding defects earlier but to help de-risk business strategy and potential problems with your user base by reporting customer experience defects.
Sometimes I feel as if I’m the Forrest Gump of quality assurance (QA). Since 1998, I’ve been through the beginning of automated integration testing and service virtualization through being a co-founder of Class I.Q. (now IBM Greenhat). I’ve been through the first phases of an automated testing center of excellence (ACOE). I’ve been there for the start of risk-based testing, and I’ve been a part of the transformation of QA from a somewhat necessary function to something that is now the core and chief concern of any company putting out quality software and apps.
It’s always been our mission to empower our customers to create amazing digital experiences that delight users and drive business success. We introduced our Digital Automation Intelligence approach that disrupts testing as we know it and puts the user back at the center to test the true UX. And we expanded our Digital Automation Intelligence Suite to use artificial intelligence (AI), machine learning, and analytics to predict business and user impacts across different interfaces, platforms, and devices.
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.
The focus on artificial intelligence (AI) in general, in technology, and particularly in testing, is prompting organizations worldwide to take it seriously. It’s hard to ignore AI’s potential benefits, including improved productivity and efficiency, fewer defects, a better UX, and happy customers. And with DevOps and continuous delivery here to stay, staying relevant depends on keeping pace, which is why test automation is so critical.