AI bug summary from Jira to Google Sheets

The team works in Jira — managers get a clear summary without manual data transfer.

Process automation & integrations · Test automation & QA

Repository on GitHub (GitHub)

Result

Manual data transfer when preparing the summary has been eliminated

About the project

The system automatically collects bug reports from Jira into Google Sheets and turns technical descriptions into short, clear summaries. Instead of shortening tickets and updating the table by hand — a single summary for QA, managers and other interested teams.

QA engineers manage their tasks as usual. Every two hours the automation checks changed bugs, and AI states the essence of the problem in three sentences and determines the category and the affected module. New bugs are added to the table, existing ones are updated by the Jira key — no duplicate rows on each run.

How it works

  1. You create or update a bug in Jira
  2. The system finds the changes
  3. AI shortens and structures the description
  4. The summary in Google Sheets is updated

Features

  • A short summary of technical bug reports for management reporting.

  • Automatic classification of problems: functional, UI/UX and others.

  • Identifying the affected area of the system — for example, deals or agents.

  • Updating existing rows and adding new bugs by a unique key.

  • Checking changes on a schedule without setting up global Jira webhooks.

Where it can be used

  • QA and development: a shared bug summary without an extra manual report.
  • Product and project teams: an overview of problems to discuss priorities.
  • Corporate reporting: a clear presentation of technical tasks for managers who do not work in Jira.

Technologies

  • n8n
  • OpenAI
  • Jira
  • Google Sheets

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Start with one process.

Describe what is currently done manually, what data is involved and what result you would like to achieve. You do not need a technical specification or a chosen model to begin.

Please do not share trade secrets, source code, or sensitive personal data before an NDA (non-disclosure agreement) is signed.