There is a lot of hype around continuous testing these days. It seems like every product vendor has a continuous testing product and every consulting company has a continuous testing practice.

Agile testing is hard. Testers contend with terse requirements, minimal process, little documentation, continually evolving business, technical and organizational factors. Auditors demand proof of compliance. Some teams have trouble conforming to regulations while preserving agile practises..

Ensuring that each new release delivers a positive user experience is now more critical than ever. But with cloud-native apps, microservices, and other compartmentalized elements, an application involves many highly distributed components, and a performance issue in any of them could have a ripple effect across the entire application. Now that new functionality is being released weekly, daily, or even hourly, each team needs instant insight into whether their incremental changes could negatively affect performance. However, legacy performance testing approaches are too late, too heavy, and too slow, and the later you start load testing, the more difficult, time-consuming, and costly it is to debug and resolve performance problems. Developers and testers need a way to expose critical performance issues earlier in the delivery pipeline. Kevin will show you how this is being accomplished in high-performing organizations.

Speed is king in agile. In a world where most of the agile process is automated, testing is the slowest and most expensive part of getting your app or website deployed to the world. Very few app teams have a decent amount of test automation, and even they still have days of manual testing during each agile cycle before they release new versions of their app. Testing is difficult, especially at the UI level, which is why it is still relegated to humans. But all that is changing with the application of artificial intelligence and machine learning. Join Jason Arbon as he explains how agile testing is ripe for disruption because AI itself is based on examples of input and output—which sounds a whole lot like the testing activity.