requirement-parser

To parse raw product requirements into structured test-ready modules, paste the natural language text into the AI to split it into independent functional modules based on user roles, actions, and system responses for test design.

Extracting boundary conditions and exception scenarios for test design involves parsing requirement text to isolate input data and business rules, ensuring every functional module explicitly lists its edge cases and exceptions.

To structure requirement documents using Markdown tables for test design, generate a mandatory Markdown output containing a complete module list table followed by per-module detailed sections with all test elements in Chinese.

Yes, you can label priority levels for decomposed modules. The parsing process assigns P0, P1, or P2 priority labels to each functional module to clarify testing focus and module boundaries.

To avoid content overlap when splitting natural language requirements, the parsing applies a deduplication rule where small requirements only output one module, ensuring clear and independent functional boundaries.

Yes, the requirement parsing tool supports generating test points by extracting specific user roles and input data from the text, structuring them alongside system responses and business rules for comprehensive test coverage.