If you’re a software developer or tester, chances are you’ve used open source software at some point in your career — we know a lot of our engineers have. The pros and cons of open source are pretty clear:
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.
On May 21, 2018, Bank of America announced that it was rolling out its chatbot, Erica, to all its mobile customers. On the surface, the premise makes sense. It’s making the bank more relatable. It’s providing real-time customer support to people where artificial intelligence (AI) assistants like Siri and Alexa are becoming the norm. It doesn’t have the limitations that some phone-based IVRs have, and it aims to provide immediate assistance instead of making us wait for a human (we’ve all shouted “representative” or pressed zero dozens of times to get a real person). Erica is a great way for Bank of America to optimize the customer experience.
But let’s pull back the covers and ask some basic questions. How does Erica know the customer so well? How does Erica pull from different sources of information? How does Erica know what products and services to offer? What systems, both homegrown and third party, does Erica need to be effective?
Everything about software has changed—how it’s architected, developed and produced, what it does, what users want from it, and how often they expect new features. To keep up, organisations are turning to continuous delivery and DevOps. Yet product teams still do a lot of manual testing, which consumes a lot of time they don’t have, thanks to shrinking test windows. Incorporating automation into your testing approach is a great strategy, but figuring out where and how to start isn’t necessarily quick and easy.
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.
For a while now (about 10 years), Dev and Ops have been trying to get along. After all, collaboration between the two creates fast feedback loops and gets high-quality software into users’ hands faster. But with a new space emerging, digital experience management, Dev and Ops need to make a new BFF—the business—to stay in sync.
Pop the Champagne and celebrate with us! We're honored to be named by Gartner as a visionary in its 2017 Magic Quadrant for Software Test Automation. More specifically, the report recognized our technology-agnostic, cross-platform, automated testing approach that focuses on the user experience. Dowload your complimentary copy of the Gartner MQ report.
TestPlant CTO, Antony Edwards, was interviewed by Mobile World Live at Mobile World Congress in Barcelona this year. Antony talks about the key trends in mobile and IoT, and how testing needs to change in order to be more focused on UX.