Testing practices forged in the pre-AI era are breaking down. Traditional metrics like code coverage percentage are becoming problematic when AI assists in test generation. How do you validate that AI-assisted tests are meaningful rather than just hitting coverage targets?

The testing pyramid—many unit tests, fewer integration tests, minimal end-to-end tests—remains valid but requires reimagining. Legacy test suites often contain outdated, redundant tests accumulated over years. Modernizing these suites is a better investment than adding more AI-generated tests.

AI-assisted testing is reshaping QA practices. Tools like Macroscope enable human-in-the-loop review of AI-assisted code changes. Rather than accepting AI-generated tests blindly, teams should review them for meaningfulness and coverage of actual failure scenarios.

The shift toward outcome-focused testing is overdue. Instead of obsessing over hitting 80% code coverage—an arbitrary metric that says nothing about whether important functionality works—teams should focus on whether changes achieve desired outcomes and maintain critical system properties.

Mutation testing (introducing intentional bugs and verifying tests catch them) is gaining relevance. This approach validates that tests actually catch failures rather than just executing code. When combined with AI test generation, mutation testing ensures meaningful test suites.

Modern testing stacks leverage continuous integration pipelines that run hundreds of test variations on each commit. Orchestration tools coordinate parallel test execution. Observability platforms surface failures early, enabling fast feedback loops.

The future involves AI that understands failure modes and generates tests targeting likely bugs. Rather than blind coverage optimization, intelligent testing will focus on scenarios where systems are most likely to fail and highest-impact tests should be prioritized.