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Automation

As explained in "Looks Good to Me"

Automation is the execution of tasks by software, machines, or other systems with reduced or no direct human intervention.

In code review, automation handles tedious checks and errands so developers can reserve attention for nuance, context, and problems requiring judgment. section 5.0 section 5.1

Start with repetitive, objective work: formatting, linting, static analysis, testing, pull-request validation, reviewer assignment, and reminders. Automate team conventions when tools can enforce them consistently. section 5.5 section 5.1

A practical automation progression includes:

  • Basic: run syntax checks, compilation, formatting, linting, static analysis, and tests before review. section 2.4.1 section 5.5
  • Improved: generate pull-request templates, validate titles and labels, enforce size limits, and link related work automatically. section 5.1 section 5.5
  • Advanced: assign reviewers through CODEOWNERS, gate merges with quality checks, send reminders, and escalate unanswered review events. section 5.5

From manual conventions to faster delivery

A team first automated a rule against spare console.log() statements, then added checks for camel casing, const declarations, spacing, and indentation. It moved from releasing once or twice every two weeks to once or twice a day, although other factors also contributed. section 5.1

Automation should not replace human review. It can flag outdated libraries, transform declarations, inspect code smells, or suggest improvements, but people must weigh compatibility, history, context, and the larger system. Introduce AI cautiously and retain human validation and the final decision. section 5.1 section 13.6

Set parameters and confirm tool capabilities before expanding automation. Keep policies that cannot be automatically enforced in a visible, version-controlled team working agreement, and refine it collectively. section 5.2 section 4.6

How "Looks Good to Me" covers it

How deep it goes
Explains candidates, prerequisites, team effects, and human limits, but stops short of detailed tool configuration.
Examples
Uses Selenium functional testing, style rules, pull-request validators, CODEOWNERS assignments, quality gates, and review reminders.
What it assumes
Requires explicit parameters, suitable tool capabilities, and team agreement about which policies can or cannot be automated.
What you can do after
Apply it via testing, validation, quality gates, reviewer assignment, and reminders while retaining human review for context-dependent decisions.
How it connects
Connects code review with linting, formatting, static analysis, testing, pull requests, team working agreements, and AI.

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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.