Code Review & Quality

Pull request reviews, static analysis, code coverage, and lint reports.

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What's in code review & quality.code review & quality.

Labeled examples of anti-patterns and refactoring opportunities — training data for code quality AI.

PR comments, review decisions, and approval patterns from open source projects — training data for AI code reviewers.

Test coverage data linked to source files — training data for test generation and gap detection AI.

SAST findings from tools like SonarQube and Semgrep — labeled training data for vulnerability detection AI.

ESLint, Pylint, Prettier rule violations and fixes — paired training data for code style AI.

Before/after refactoring pairs from real codebases — the supervised data for AI refactoring tools.