ai-test-generation

To generate test cases from a PRD or user stories, provide your project-specific context and input sources to the AI test generation pipeline. The skill extracts requirements and produces test code for human review.

AI-assisted test generation automates test creation by running a seven-step pipeline to extract requirements, perform risk analysis, generate coverage matrices, and output test code. It requires project context like user stories and code diffs to function.

Yes, you can generate tests from code diffs and API schemas using AI test generation. The pipeline accepts these input sources alongside PRDs to identify potential risks and produce comprehensive test coverage matrices.

Yes, AI test generation requires project-specific context and input sources such as PRDs, user stories, or code diffs. Without this contextual data, the pipeline cannot accurately extract requirements or perform risk analysis.

The best way to automate QA risk analysis for new features is using an AI test generation pipeline that identifies invariants and risks from your feature specifications. This ensures comprehensive test coverage before generating the final test code.

AI-generated test cases require human review and refinement before execution, serving as a baseline rather than a final product. The accuracy heavily depends on the quality of the project-specific context provided as input.