As our software grows, we face the challenge of testing an increasing amount of functionality. Many teams I work with are managing thousands, if not hundreds of thousands, of tests. These tests are a mix of automated and manual test cases conducted across various stages and phases of testing.

Given this complexity and typical time constraints, how can we swiftly select the right tests to run at any given moment? Which tests will uncover the most bugs with minimal effort and in the shortest time?

I spent the last decade working on this challenge. In recent years, numerous AI-driven approaches have been proposed to address this question.

Our team has implemented and tested these approaches in our own development processes and in collaboration with customer teams. In this presentation, I will share both our failures and successes.