Testing AI-Generated Code: From Gherkin to Continuous Validation
AI agents can write code faster than humans, but how do you ensure that code actually works? More importantly, how do you validate it meets business requirements and regulatory standards? Traditional testing approaches weren't designed for AI-generated code at machine speed.
The solution lies in making tests as intelligent as the code they validate - automated, requirement-driven, and continuously evolving.
The Challenge of AI-Generated Code
When AI agents write code, traditional testing approaches break down:
Speed Mismatch: AI generates code in seconds; humans write tests in hours
Implicit Knowledge: AI doesn't inherently know your business rules or edge cases
Coverage Gaps: Without guidance, AI may implement features but miss critical test scenarios
Regression Risk: Rapid changes can break existing functionality in subtle ways
Compliance Requirements: Regulated industries need traceability from requirements to tests to code