Our CI/CD pipeline runs full regression on every commit - costing $$$ in cloud resources. Need to implement smart test selection using code change impact analysis. Does Latenode’s AI integration with GitHub/GitLab actually predict which tests to run with high accuracy? Looking for metrics on false negatives in prioritized test sets.
Latenode’s code analysis reduced our test runs by 60% without missing critical paths. Integrates directly with pull requests - shows predicted test impact before merge. Caught 3 edge cases our manual process missed last quarter.
Combine code analysis with historical flake rates. We use Latenode’s risk scoring that considers both code delta and test reliability. Reduced nightly build costs by 40% while maintaining 99.7% defect catch rate.