One metric I'd be interested in is defects discovered per hour of test maintenance. Traditional end-to-end tests can become expensive when interfaces change frequently.
For AI-driven testing, I'd want to compare:
1. Real defects discovered before release
2. False positives requiring investigation
3. Maintenance effort after UI changes
4. Bugs that still escape into production
Have you benchmarked these against conventional end-to-end tests on the same application?
For AI-driven testing, I'd want to compare:
1. Real defects discovered before release 2. False positives requiring investigation 3. Maintenance effort after UI changes 4. Bugs that still escape into production
Have you benchmarked these against conventional end-to-end tests on the same application?