What is PR Report Card?

A PR report card is a standardized assessment of code changes in a pull request, providing grades or scores across multiple quality dimensions. Rather than presenting a flat list of issues, a report card organizes findings into categories that help developers and reviewers quickly understand the overall health of a change.

Typical dimensions

PR report cards commonly evaluate changes across these categories:

• Security: Vulnerabilities, secrets exposure, authentication issues, input validation
• Reliability: Bug risks, null pointer dereferences, resource leaks, error handling
• Complexity: Cyclomatic complexity, cognitive complexity, deeply nested code
• Hygiene: Code smells, style violations, dead code, documentation gaps
• Coverage: Test coverage changes, untested code paths, coverage regressions

Each dimension receives an independent score, allowing teams to set different thresholds based on their priorities. A security-critical application might require an A in security while accepting a B in hygiene.

Why report cards matter

Traditional code review tools present issues as undifferentiated lists. This approach has problems:

1. Information overload: Reviewers see dozens of findings without clear prioritization
2. Missing context: Individual issues don't show how changes affect overall code health
3. Inconsistent standards: What's blocking for one reviewer may be acceptable to another

Report cards solve these problems by providing structure. A developer can quickly see "this PR introduces a security regression but improves reliability" rather than parsing through individual findings.

Use with AI coding assistants

PR report cards become particularly valuable when working with AI coding tools like GitHub Copilot, Cursor, or Claude. These tools generate code quickly but may introduce subtle issues. A structured report card provides:

• Clear feedback that AI assistants can interpret and act on
• Measurable quality gates that don't depend on human review capacity
• Consistent evaluation criteria across human and AI-authored code

Implementation considerations

Effective PR report cards require:

• Calibrated scoring: Grades should reflect actual risk, not just issue counts
• Clear thresholds: Teams need to define what grades are acceptable for merging
• Trend tracking: Historical data helps identify whether code quality is improving over time

See also: AI Code Review, Code Smell, Cyclomatic Complexity