Test Case Synthesis

Test case generation is an approach for creating test datasets aligned with your AI application's requirements (AID). Using advanced AI Agent, this system creates diverse, realistic test cases that thoroughly exercise your AI's capabilities.

The generation process follows a structured process:

1. Use Case Discovery: Analyzing AID and requirements, identifying core functionalities and user intents
2. Controlled Generation: Creating variable elements within test cases, establishing logical relationships between parameters and building templates that combine fixed and variable elements
3. Distribution Optimization: Balancing coverage across different use cases, avoiding over-representation of similar patterns

Integration with Evaluation Metrics

Test case generation highly correlates with evaluation metrics as both of them are derived from the same AID. In order to efficiently utilize this tool, the major use cases section in AID must be relevant to all your business cases.