The Problem

Engineering teams face a critical challenge when refactoring code: understanding the full impact of their changes. Traditional approaches fall short:

Text Search is brittle and misses semantic connections
Vector Search fails to capture code relationships and dependencies
Manual Review is time-consuming and error-prone
Static Analysis often generates too many false positives

This leads to unexpected bugs, broken features, and increased technical debt.

How AttentionDB Solves It

AttentionDB brings a revolutionary approach to code impact analysis through its salience-powered memory:

1. Intelligent Code Parsing

Breaks down code into functions, classes, and modules
Builds comprehensive call graphs and dependency trees
Maintains ownership and modification history
Tracks cross-file and cross-module relationships

2. Advanced Attention Scoring

AttentionDB uses multiple attention types to rank code relationships:

call_graph: Weight: 0.8
- Direct function calls
- Interface implementations
- Event handlers
scope_proximity: Weight: 0.6
- Shared class membership
- Module co-location
- Package dependencies
ownership: Weight: 0.5
- Code author history
- Recent modifications
- Team ownership patterns

3. Deterministic Recall

When analyzing a code change, AttentionDB:

Identifies the modified code entities
Traces call graphs in both directions
Scores related code by attention weights
Returns a ranked list of potentially affected components

// Example: Analyzing auth refactor impact
const impactAnalysis = await attentionDB.analyze({
  sourceFile: "auth/middleware.ts",
  attention: {
    call_graph: 0.8,
    scope_proximity: 0.6,
    ownership: 0.5
  }
});

// Returns affected components ranked by risk

4. Integration Features

VS Code Extension
- Inline impact visualization
- One-click analysis
- Change preview with risk scores
CI/CD Pipeline
- Automated impact reports
- Change size estimation
- Breaking change detection
Code Review Tools
- PR impact summaries
- Suggested reviewers
- Risk assessment

Real World Impact

Case Study: E-commerce Platform

A major e-commerce platform used AttentionDB during their authentication system refactor:

Before: 3 production incidents from missed dependencies
After: Zero incidents, 40% faster refactor completion
ROI: Saved 120+ engineering hours per major refactor

Success Metrics

95% accuracy in dependency detection
80% reduction in unexpected side effects
60% faster impact analysis compared to manual review
40% decrease in regression bugs

Getting Started

# Install AttentionDB
npm install @attanix/attention-db

# Initialize with your codebase
npx attanix init --source=./src

# Analyze impact of changes
npx attanix analyze auth/middleware.ts

Best Practices

Run impact analysis before starting refactors
Include AttentionDB reports in PR descriptions
Set up automated CI/CD checks
Maintain up-to-date ownership metadata

Code Refactor Trace: Impact Analysis Made Simple