AI Tip of the Week #17: AI Trace‑Notes

Commit → Requirement → Test, with rationale

Let an LLM act as your scribe, not your boss: feed it your latest PR title/description + changed file paths, along with a small export of linked Jira requirements and qTest test cases. Ask it to draft trace‑notes that (1) suggest links between the change, the relevant Jira story, and the specific qTest tests, and (2) include a one‑sentence, human‑readable reason (e.g., “touches billing/tax_calc.py → Story ABC‑123 ‘VAT rules’ → Tests tagged [Billing] VAT edge cases”). This fits neatly into normal QA flow because qTest already supports real‑time Jira traceability and lets you attach links/notes at the requirement, test case, and test run levels—so you can record the AI’s suggestion and rationale where your team actually looks during triage. It’s also timely: recent studies show LLMs can outperform classic IR methods for trace link discovery (when framed correctly) but still benefit from human-in‑the‑loop review—perfect for decision support, not automation by decree. Early research further indicates LLMs can recover links between issues and commits, reinforcing the value of using PR text and change paths as inputs for trace suggestions.

Try this week:

1. In qTest/Jira, pick one active epic or story and export 10–20 related test cases (include their IDs, titles, and any component tags you use). Then copy the last 3–5 PR titles/descriptions and the changed paths. 
2. Prompt your AI assistant: “Propose links commit → Jira story → qTest tests, add a one‑line reason per link, and a low/medium/high confidence.” Keep the AI in the assistant lane; you approve the final links. (Research recommends this human‑in‑the‑loop pattern for accuracy and trust.)
3. Paste accepted links back into qTest—either as requirement–test associations or as a short “AI Trace‑Note” comment on the test run—so future failures have context in one place.