GitHub faces AI code quality

Short summary

GitHub's explosion of AI-generated code has created a silent quality crisis: repositories appear functional but often contain unmaintainable code with cyclomatic complexity scores above 50 (vs. safe threshold of 10), minimal test coverage, and high code duplication. Stars and forks measure interest, not code health. The solution is implementing three leading indicators: cyclomatic complexity limits, test coverage floors (80%+), and duplication bounds (15%-)—metrics that objectively predict maintainability before shipping.

• •AI-generated code quality crisis: high complexity, low test coverage, and code duplication create maintenance liability
• •GitHub's star/fork metrics are lagging signals measuring interest, not code health; quality remains invisible to users
• •Implement three leading indicators: cyclomatic complexity ≤10, test coverage ≥80%, duplication ≤15%

Generated with AI, which can make mistakes.