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Issue validation

As explained in "Looks Good to Me"

The assessment of whether a pull request's code changes satisfy the aspects specified in a linked work issue.

Issue validation checks whether a pull request fulfills the explicit, verifiable aspects of a linked work issue. An AI review tool interprets the issue, then compares its requirements with the changed code. section 13.3

Evaluate the technique against its practical constraints:

  • Use GitHub or GitLab issues as the linked work source; other tracking systems may require duplicating work items as issues. section 13.3
  • Write issues with accurate, detailed, interpretable, and verifiable aspects; vague issues can make validation unreliable. section 13.3
  • Set team expectations for configuration, learning, participation, and whether validation runs on every pull request or only complex changes. section 13.3

A practical trade-off

A team tracking work on a Jira board would need to duplicate each relevant item in GitHub or GitLab, then maintain enough detail for the tool to verify every requirement. That added effort can outweigh the convenience of automated confirmation. section 13.3

How "Looks Good to Me" covers it

How deep it goes
Explains the interpretation-and-comparison workflow and adoption caveats, but does not detail validation internals.
Examples
Uses duplicated Jira work items, linked GitHub or GitLab issues, and pull-request code changes as concrete cases.
What it assumes
Assumes linked issues can state detailed, accurate, verifiable aspects and distinguishes GitHub or GitLab from other trackers.
What you can do after
Evaluate whether automated issue validation is suitable for a team’s tools, issue quality, and review workflow.
How it connects
Connects pull requests, code review, AI code review, GitHub issues, GitLab issues, and Jira work tracking.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of "Looks Good to Me". The section links point to the parts of the book it draws on.