In this interview, Geoff Meyer, a test architect in the Dell EMC infrastructure solutions group, discusses whether or not testers should be nervous about artificial intelligence, what testers can do right now to keep up with the times, and when AI is most useful for software teams.
Members of the engineering community are beginning to explore the exciting capabilities of artificial intelligence in order to remove even more mundane and manual tasks from our jobs. The next generation of automation within the software development lifecycle comes to us in the form of AI-inspired approaches: analytics, machine learning, and natural language processing. Geoff Meyer refers to this as cognitive automation, and it offers the promise of automating tasks that up until now could only be performed by humans. However, engineering practitioners going down the path of cognitive automation should proceed with caution due to the combination of excessive hype and unprecedented complexities compared to prior stages of automation. Join Geoff as he provides a framework for organizations to use when considering the application of AI to tasks within their SDLC.
The approaches to testing are continuously evolving as we try to keep up with the application needs of today’s users. Our industry is facing a new paradigm where AI is helping achieve scale, coverage, and business impact for many organizations.
People are actively engaging in civic tech, social robots are tweeting, and veteran storytellers are capturing stories in new ways using virtual and augmented reality. This explosion of tools, sources, voices, and data is indicative of a new, more collaborative era for storytelling.
There are two main challenges to testing systems that incorporate elements of artificial intelligence. First, the same input can trigger different responses as an AI system learns and adapts to new conditions, and second, it is difficult to understand what the correct response really should..