STAREAST 2018

Advances in artificial intelligence (AI) and machine learning (ML) are leading to a new generation of software, which is becoming self-adaptive, autonomous, and smart. Academic researchers and industry practitioners are investigating how these new AI and ML technologies can be leveraged to improve software testing and testing services. A handful of testing-as-a-service (TaaS) vendors already offer services that use AI bots to perform some functional and performance testing. How well do they live up to their claims?

Augmented reality (AR) and virtual reality (VR) mobile apps are going to dominate the technical landscape for the next few years. Compared to current mobile apps, testing this new AR and VR technology will require more preparation, more effort, and new tools. BJ Aberle breaks down the methods and tools he uses to test AR and VR applications. Explore test environment requirements and discover the types of test data you’ll need to prepare for AR and VR testing.

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.

Even today, communication breakdowns are a primary cause of software project failures. Marcia Buzzella’s research shows that increasing the success rate of projects across agile, DevOps, and waterfall methods requires a balance of social and technical capabilities. Social interactions enable us to assess situations and course correct in ways machines cannot.