{}USER STORY:{}

As a Bot Administrator, I want to define a clear labeling mechanism for Jira issues, so that the bot can be automatically retrained based on supervised learning of refinement status (e.g., "refined enough" Y/N) and sizing.

{}DESCRIPTION:{}

A system needs to be in place for human experts (bot trainers) to easily label Jira issues with their actual refinement status and confirmed size, which will then be used as ground truth for training and retraining the bot's models.

{}Required:{}

...

{}Nice to have:{}

...

{}ACCEPTANCE CRITERIA:{}

• A defined set of labels (e.g., "BotRefined:Y/N", "BotSize:X") or a similar mechanism can be applied to Jira issues.

• The ingestion process (from story #4) can detect and consume these labels for training data.

• The labeling process is simple and integrates with existing Jira workflows or a dedicated labeling tool.

• The system can differentiate between initial training data and ongoing feedback for retraining.

{}ENGINEERING DETAILS:{}

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