Artificial Intelligence (AI) is making it possible for computers to diagnose some medical diseases more accurately than doctors. Such systems analyze millions of patient records, recognize underlying data patterns, and generalize them for diagnosing previously unseen patients. A key challenge is determining whether a patient's symptoms and history are attributed to a known disease or other factors. Software testers face a similar problem when triaging automation failures. They investigate questions like, Is the failure due to a defect, environmental issue, or nondeterministic test script? Is there current or historical evidence to support one belief over another? Join Tariq King as he describes how test failures and flakiness can be modeled for machine learning (ML) as causal disease-symptom relations.

The idea of testing everything is a popular one—in fact many stakeholders think that’s exactly what their quality teams do. It usually isn’t and can’t be; but how can teams communicate this? Join Jenny Bramble as she helps to pave the way using the language of risk-based testing. By defining risk in two simple parts, the team and project have a tangible and usable metric. She shares how to apply this metric and use it to determine where the team should focus testing, making it more effective and efficient whilst communicating that effort through the creation of a risk matrix. As a result, risk becomes the right language for the team to communicate clearly and concisely with everyone involved in the project by using agreed-upon words and definitions. Take away a set of tools that can be used to facilitate both better testing and better communication though precise use of language and risk matrixes.

Modern software development has brought us an incredibly powerful tool: continuous integration and deployment. However, taking advantage of this new system isn’t always straightforward. With powerful new tools come powerful new ways of making mistakes that can take your product down in a heartbeat. Melissa Benua has years of experience making CI and CD work for her, with lots of insights—both good and not so good. Come and learn from her as she shares key tips and tricks for coding and testing for both forward and backward compatibility in software releases. Useful for both traditional testers as well as combined engineers, Melissa provides technical and actionable advice to enable your team to make the right trade-offs and the right time investments, allowing your product to release to your customers safely and successfully.

Today’s IT systems communicate with customers through multiple points of engagement and various interfaces, ranging from web, mobile, and voice to BOTs and apps like Alexa and Siri. Sanil Pillai says these systems need to provide seamless handoffs between different points of interaction—while at the same time providing relevant and contextual information quickly. To accomplish this, a team must be able to successfully pair device hardware capabilities and intelligent software technologies such as location intelligence, biometric sensing, and Bluetooth. Sanil shows that testing these systems and interfaces is becoming an increasingly more complex task, and traditional testing and automation processes simply don’t apply to new-generation digital interaction services. Join Sanil as he discusses the testing and automation challenges in new-generation digital interactions using hyperconnected BOTs.