DriftDetector Exposes AI-Code Debt

DriftDetector makes technical debt legible in the language engineering leaders actually need: risk, location, and remediation time. Its deterministic approach is more auditable than adding another LLM to code review, though teams will still need to validate whether its effort estimates match reality.

• –Traces findings to specific files, lines, and commits rather than offering a vague quality score
• –Weighs drift by remediation effort, helping teams prioritize expensive problems over noisy ones
• –Covers security, accessibility, reliability, maintainability, architecture, testing, UX, design consistency, and AI code governance
• –Local scanning and no-token operation reduce privacy, cost, and reproducibility concerns
• –The main open question is calibration: static rules can identify patterns consistently, but estimated fix time may vary substantially by codebase