Global software developers are in a double bind: They’re under pressure to release new or upgraded applications continuously, while apps have become increasingly feature-rich and complex. Traditional QA processes, including manual risk-based testing (RBT), can’t keep up, and the bottlenecks are getting bigger.
This case study-based presentation will examine how organisations can use AI and automation to transform test planning into a faster, data-driven process that:
Audience members can expect to learn, from a real-life example, how AI can be harnessed to make testing smarter and more accurate and become familiar with best practices/potential dilemmas in applying predictive analytics to risk-based testing.
The audience should have a basic understanding of software quality assurance, testing and AI concepts.