AI Code Evaluation: Beyond Linting to True Understanding

Your linter doesn't understand your code. It just pattern-matches text.

This might seem obvious, but the implications are massive. Most "AI code analysis" tools are just glorified linters with machine learning sprinkled on top. They still don't understand anything.

Let me show you what I mean.

The Linting Illusion

Here's what traditional static analysis sees:

function processUserData(user: User) {
  const data = transformData(user);
  return saveToDatabase(data);
}

A linter checks:

• Are there unused variables? ✓
• Is the function too long? ✓
• Are there any obvious anti-patterns? ✓

What it completely misses:

• transformData calls 47 other functions across 12 files
• saveToDatabase triggers 3 webhooks and 2 background jobs
• This function is called by 23 different API endpoints
• Changing it will break the mobile app's sync feature

Pattern matching can't see relationships. It sees trees when your codebase is a forest.

What Real Code Evaluation Looks Like

True AI code evaluation requires understanding code as a graph of relationships, not a collection of files.

When we built the intelligence system in Glue, we started with this principle. Every symbol in your codebase becomes a node. Every call, import, and reference becomes an edge. The result is a living map of how code actually works.

// What our system sees for the same function
{
  symbol: "processUserData",
  file: "src/services/userService.ts:45",
  callers: [
    "UserController.updateProfile",
    "SyncService.processQueue",
    "WebhookHandler.onUserUpdate",
    // ... 20 more
  ],
  callees: [
    "transformData → [12 nested calls]",
    "saveToDatabase → [triggers: webhooks, jobs]"
  ],
  impactScore: 87,  // High blast radius
  lastModified: "3 days ago",
  contributors: ["alice", "bob"]
}

This isn't linting. This is understanding.

The Three Levels of Code Analysis

Level 1: Syntax Analysis (Traditional Linters)

• Pattern matching on text
• Rule-based checks
• Zero semantic understanding
• Tools: ESLint, Pylint, SonarQube rules

Level 2: Semantic Analysis (Type Checkers)

• Understands types and interfaces
• Catches type mismatches
• Limited to single-file context
• Tools: TypeScript, mypy, Flow

Level 3: Architectural Analysis (Graph Intelligence)

• Understands call relationships
• Maps dependencies across files
• Identifies blast radius
• Discovers features automatically
• Tools: This is what we built

Most teams are stuck at Level 1 and think they're doing "code quality." They're measuring syntax compliance, not actual code health.

Why This Matters for Real Work

Let me give you a concrete example.

A PM asks: "How long would it take to add multi-currency support?"

Level 1 analysis (linting): No idea. Can't answer business questions.

Level 2 analysis (types): "There are 47 places where price: number is used." Still not helpful.

Level 3 analysis (graph intelligence):

Currency Impact Analysis:
- 12 API endpoints handle prices
- 3 services contain currency logic
- 2 database tables need schema changes
- Payment integration touches Stripe SDK
- Mobile app has hardcoded USD symbols in 8 places

Estimated files affected: 34
High-risk changes: PaymentService, CheckoutFlow
Recommended approach: Start with database schema, 
  then propagate through PaymentService

Now you can actually plan the work.

The Feature Discovery Breakthrough

The most powerful application of graph-based code evaluation is automatic feature discovery.

Instead of manually documenting what your codebase does, we analyze the call graph to identify natural clusters of functionality:

// Our Louvain clustering algorithm identifies features
const features = await discoverFeatures(workspaceId);

// Returns grouped functionality:
// "Payment Processing" - 23 files, 89 symbols
// "User Authentication" - 15 files, 56 symbols
// "Search & Filtering" - 8 files, 34 symbols

No manual mapping. No stale documentation. The features emerge from the code structure itself.

What to Look for in AI Code Evaluation

If you're evaluating tools, ask these questions:

1. Does it build a symbol graph? If it only analyzes files in isolation, it's just fancy linting.

2. Can it trace call paths? "What happens when I call this function?" should be answerable.

The Bottom Line

Your codebase isn't a collection of files following syntax rules. It's a complex system of interconnected behaviors.

Evaluating code quality means understanding those connections. Linting is necessary but nowhere near sufficient. The teams shipping fastest are the ones who see the full graph — and can navigate it confidently.

That's what real AI code evaluation looks like.