Accelerate San Francisco 2019 is now just one month away. On May 22-23, software testing leaders from around the world will be converging on San Francisco’s Union Square to discuss all things Continuous Testing.

If you’re still not registered, here are some highlights and keynotes from last year’s conference to give you a taste of what you might be missing…

Digital Transformation Navigating the Future of Testing, Wolfgang Platz

All signs indicate that the next wave of innovation will be driven by AI, robotics, big data, and predictive analytics. Testers will face the challenge of testing applications that are simply beyond the scope of our current approaches. But you’ll also be able to take advantage of a new generation of testing technologies—enabling you to reinvent your testing process for the digital future. In this keynote, Wolfgang Platz, Tricentis Founder & Chief Strategy Officer, shares his vision of software testing in the digital future. Learn how enterprise application architectures and associated delivery models are changing, how testing must evolve to address these changes, and what testers can (and must) do to successfully navigate the road ahead.

Leadership in Testing, Sandeep Johri

In the last two years, the vast majority of industry experts have felt compelled to provide their perspective on “the future of testing.” This is unprecedented. Something must have fundamentally changed to generate so much buzz…what is it? Join Sandeep Johri, Tricentis CEO, for a deep dive into the sea change that the industry is now facing.

AI and Automation for the Intelligent Enterprise, Hans Pauley & Paul Downes

Testing plays a critical role as AI systems become more pervasive. The errors from these systems can adversely affect not just business performance, but also reputation, compliance and human life itself. In addition to ensuring reliability, these systems will also need to provide adequate transparency in the decisioning mechanism, such as explaining the logic, or the “why” behind the decision, in a way that the user can understand, and also provides an assurance of fairness and non-discrimination in the decision process. AI introduces both new opportunities and new challenges for quality engineering. AI-for-Testing leverages machine learning, cognitive computing and statistical modelling algorithms to increase efficiency over the testing life-cycle, from planning to design and execution. This includes predictive analytics, prescriptive analytics, intelligent advisors, and intelligent automation. Testing-for-AI involves teaching and testing AI applications such as chatbots and virtual assistants to guide them as they develop and learn.