Generative AI in Quality Assurance
The need for a new test automation model has been an imperative over the last 10 years as we have moved from waterfall to agile and agile to DevOps. Moving from test coverage to application coverage and reducing test time from months to an hour or less has created a substantial pressure for full success. Now AI in test is a reality. The first generative AI offerings in QA became available in 2018 and since then marked improvements have been made in outcomes. This has changed QA teams' focus, tasks, and work effort. With the ultimate goal of AI finding all our bugs, the advent of transformer models such as ChatGPT has brought excitement as well as challenges in accuracy. We will look at specific examples of successful outcomes and failures over the past 5 years as well as new challenges using GPT and other LLM’s. We will share hands-on tips and tricks you can put to use today, as well as new technologies and models which provide orders of magnitude improvements in application coverage and speed, making generative AI a new core method of test automation. You will walk away ready to improve your overall quality using AI.