In ATA’s robustness testing, AI instructions play a key role in guiding the generation of meaningful and targeted test cases for your API collection. By providing clear and specific AI instructions such as describing edge cases, common error scenarios, or particular validation rules you enable ATA’s AI engine to tailor the robustness tests to your business logic and technical requirements. This helps ensure that the generated test cases go beyond generic invalid values, focusing instead on the real-world situations and failure modes most relevant to your APIs. Effective management of AI instructions leads to more comprehensive, relevant, and valuable robustness testing outcomes.
