01What it is
AI test case generation reads a source that describes behaviour — a spec, a PRD, acceptance criteria, or an issue in your tracker — and drafts test cases from it: a title, steps, and an expected result for each. Instead of authoring every case from a blank page, you start from a reviewable draft and refine.
02How it works in practice
- Pick a source. Paste text directly, or point the generator at an item in a connected tool — a Linear issue, a Notion page, a Jira ticket, or an Azure DevOps work item.
- Generate. The model reads the source and drafts candidate cases mapped to your project's template — so the fields match what your team already uses, not a flattened generic structure.
- Review and save. You see the drafts, edit or discard as needed, and save the ones worth keeping. They become normal test cases.
- Keep traceability. Cases generated from a tracked item link back to the source, so coverage traces to the requirement that justified it.
03What good output looks like
- Template-aware — titles, priority, steps, and expected result populate the fields your project defines.
- Reviewable, not auto-applied — drafts wait for a human to approve.
- Linked to the source — each case points back to the spec or issue it came from.
04Keeping humans in the loop
Generation is a drafting aid, not a decision-maker. The team still decides what matters, what to keep, and whether an expected result is correct. The value is speed and completeness on the first pass — not removing review. See AI in software testing for where that line sits.
If you'd rather your own AI client do the drafting, you can connect it directly to your workspace over the MCP server — your agent authors cases (with steps and priority) under a personal key that only does what you can. See test management for AI agents.