How Can AI Help Optimize Which Tests to Run in CI/CD?

AI-assisted test selection offers a solution to the inefficiencies in traditional continuous integration (CI) processes by optimizing which tests to run based on the relevance to specific code changes. Instead of executing the entire test suite for every commit, AI predicts the most pertinent tests by leveraging historical CI data, including file changes, test failures, runtimes, and dependency relationships, thereby significantly reducing build times while maintaining confidence. This method addresses the issue that most tests are irrelevant to most changes, particularly in large repositories where a single change often does not impact unrelated components. AI-based test selection does not eliminate the need for validation; it complements it by ensuring smoke tests, security checks, and full test suite runs on main branches or scheduled intervals. While introducing AI into CI pipelines can pose risks such as false negatives and trust issues, these can be mitigated through strategies like confidence thresholds and transparent reporting. AI test selection is particularly beneficial for large projects with long test suites, providing faster developer feedback and reduced infrastructure costs.