Code Quality Definition Framework: A Practical Guide to Standardizing Quality Across the SDLC

Without clear code quality definitions, AI code can quickly degrade overall software quality. This framework gives engineering teams a structured approach to defining what “good” actually means across their codebase, so reviews catch what matters and ship what’s ready.

This framework is a step-by-step approach to putting this into practice. Map repos to risk profiles, set early-detection criteria, and implement dynamic rules as enforceable standards that adapt to codebase patterns and set your teams up for high-quality AI velocity.