Turn a Jira story into test cases without outsourcing judgment.

The goal is not “paste ticket, accept 40 cases.” The goal is to expose ambiguity first, then expand coverage after the rules are clear.

Best-suited tools

Step 1 — Extract facts only

Give the model the story and ask it to list actors, states, inputs, outputs, constraints, dependencies and explicit acceptance criteria. Reject anything it added.

Step 2 — Find questions before cases

Run the requirement-risk prompt. Resolve material ambiguities with product/business stakeholders before generating final expectations.

Step 3 — Build a human risk map

Identify what would be expensive if it failed: money movement, authorization, irreversible state changes, data loss, timing, concurrency and downstream integrations.

Step 4 — Generate coverage by risk type

Ask for positive, negative, boundary, state, timing, concurrency and dependency-failure cases. Require every proposed case to map back to a known rule or an explicitly labeled assumption.

Step 5 — Prune and prioritize

Remove duplicates and unsupported scenarios. Tag tests by business impact and likelihood. A short high-value regression set is more useful than a giant undifferentiated list.

Step 6 — Convert stable scenarios to automation

Only automate behavior whose expected result is known. Use AI to scaffold code, then review synchronization, selectors, data isolation and assertions.

Step 7 — Preserve traceability

Keep story ID → business rule → scenario → automated test mappings so AI-generated coverage can still be audited.