How to Evaluate AI-Generated Code Quality
CodeFlow Lab runs a multi-dimensional quality gate on every generation before delivery. Here is what each dimension measures, what the scores mean, and what to do when a score is below threshold.
Why Quality Scoring Matters for AI Generation
Most AI code generators present their output as a binary: you either get code or you don't. There is no quality signal, no indication of how many TypeScript errors you'll find when you open the files, no warning that 30% of the declared routes have no implementations.
CodeFlow Lab takes a different position: code quality is measurable, and users deserve to know their score before they download. The quality gate runs automatically on every generation, producing a 0–10 score across five dimensions. If the score is below threshold, the status is clearly disclosed — invalid (<5) or partial (5–8) — before the download prompt appears.
This isn't a gate that blocks you. It's a signal that tells you how much cleanup work to expect and gives you specific, actionable information about where to focus.
TypeScript first
tsc --noEmit runs on every generation. Errors are counted, categorized, and reported before delivery.
Coverage verified
Every declared module is checked for a real implementation. Stubs are excluded from the coverage count.
Disclosed, not blocked
Below-threshold code can still be downloaded with an explicit acknowledgment. Nothing is silently hidden.
Quality Dimensions and Weights
| Dimension | Weight | What it measures | Target |
|---|---|---|---|
| TypeScript error ratio | 30% | Errors per 100 generated files (from tsc --noEmit) | < 5 errors / 100 files |
| Coverage ratio | 25% | Declared modules with real implementations vs stubs | > 80% real implementations |
| Structural coherence | 20% | Routes, bindings, and props consistent across files | Zero cross-file contract violations |
| Accessibility score | 15% | WCAG annotation completeness across components | > 90% components annotated |
| Semantic HTML ratio | 10% | Semantic elements vs generic div/span usage | > 70% semantic elements |
Interpreting Your Quality Score
Production-ready. TypeScript errors below threshold, coverage above 80%, structure coherent across files. Ready to build on.
Functional but needs cleanup. Some TypeScript errors present, one or more declared modules implemented as stubs. Suitable for prototyping; audit before shipping.
Significant issues. High TypeScript error count, low coverage, or domain mismatch (wrong app type generated). Regenerate with a refined prompt or cleaner design file.
Frequently Asked Questions
How do you evaluate the quality of AI-generated code?
AI-generated code quality should be measured across multiple dimensions: TypeScript error ratio (errors per 100 files), coverage ratio (how many declared contracts have corresponding implementations), accessibility score (WCAG criteria present in generated components), structural coherence (whether routes, data bindings, and component props are consistent across files), and fidelity (how closely the output matches the input design or prompt). No single metric is sufficient.
What is CodeFlow Lab's quality scoring system?
CodeFlow Lab scores every generation on a 0–10 scale before delivery. The score is computed from: TypeScript error count (primary signal), coverage ratio of declared vs implemented contracts, accessibility annotation completeness, semantic HTML ratio, and a structural coherence check that runs tsc --noEmit across the whole project. Scores below 5 result in 'invalid' status; 5–8 result in 'partial' status; 8+ result in 'complete' status. All statuses are disclosed to the user before download.
What causes a low quality score in CodeFlow Lab?
Common causes of low quality scores: a generation domain mismatch (the wrong type of app was generated for the prompt), very low coverage ratio (fewer than 50% of declared modules have real implementations), a high TypeScript error count (above 20% error ratio), missing or incorrect route bindings, and widespread use of 'any' or stub implementations tagged with the fallback sentinel. Each of these is reported separately in the generation report so you know exactly what to fix.
Can I download code with a quality score below threshold?
Yes, but force-downloading below-threshold code is audit-logged and requires an explicit acknowledgment. We do not silently block downloads — we disclose the quality issue and let you make an informed decision. Force-downloads with scores below 5 (invalid status) are prominently flagged in your generation history.
How does CodeFlow generated code compare to manually written code in quality?
On the TypeScript error ratio and accessibility dimensions, CodeFlow's IR-based generation is often more consistent than manually written code — because consistency is enforced structurally, not by convention. Where human-written code tends to be higher quality is in semantic clarity: meaningful variable names, domain-appropriate abstractions, and well-commented edge cases. CodeFlow Lab targets the structural quality dimensions; the semantic layer is where human review adds the most value.
What should I do if my generation scores below 8?
Review the generation report, which breaks down the score by dimension. If the issue is TypeScript errors, check the error list — many are fixable with a small set of targeted edits. If the issue is coverage ratio, the report shows which declared modules don't have implementations. If the issue is accessibility, the report shows which components are missing required annotations. You can also regenerate with a refined prompt or a cleaned-up Figma file to improve the score.