Test Generation
The process of automatically creating test cases using AI, based on prompts, configurations, and source materials.
Overview
Test generation uses AI to automatically create test cases from your requirements, domain knowledge, and configurations. Generate hundreds of realistic, diverse tests in minutes instead of hours.
How It Works
- Input: Provide prompts, behaviors, or source materials
- Generation: AI creates diverse test scenarios
- Review: You review the generated test set
- Refinement: Iterate to improve test quality
Generation Methods with SDK
Simple prompt-based generation creates tests from a single description. You provide a prompt like "Generate tests for a customer support chatbot handling refund requests" and the system produces diverse test cases exploring that scenario. This works well for quick test generation when you have a clear use case in mind.
Generation with behaviors and categories allows more structure. Define specific behaviors you want to test (like "handles ambiguous requests" or "refuses inappropriate queries") and categories to organize tests. The generator creates test cases targeting each behavior and properly categorized for analysis.
Generation from source documents grounds tests in your actual content. Upload product documentation, FAQ pages, or knowledge base articles, and the system generates tests based on information in those documents. This ensures tests cover your real domain and use correct terminology.
Generation with context incorporates additional constraints or requirements. Specify things like target user personas, specific edge cases to include, required difficulty levels, or particular aspects to emphasize. The generator tailors output to these specifications.
Benefits
AI-powered test generation delivers speed, creating tests 100x faster than manual approaches. It provides better coverage by exploring scenarios you might not have considered, with natural diversity in phrasings and approaches. The technology scales effortlessly, enabling you to create thousands of tests without the manual effort that would otherwise be required.
Example Workflow
A typical test generation workflow starts with defining your needs—what functionality or behaviors you want to test. Generate an initial test set using prompts and configurations. Review the generated tests, identifying which ones are valuable and which miss the mark. Refine your generation prompts based on what you learned, emphasizing aspects that need more coverage. Generate another batch incorporating your refinements. Iterate until you have a comprehensive test set that covers your requirements well.
Best Practices
- Review generated tests: AI can make mistakes
- Combine with manual: Use both approaches
- Iterate prompts: Refine to improve quality
- Use specific prompts: More specific = better results
- Leverage sources: Include documentation for context