How to Document a Legacy Codebase Without Reading Every Line

You inherited a codebase. Maybe someone left the company. Maybe your team acquired a project. Maybe you're staring at code you wrote two years ago and have no memory of writing.

The documentation is either missing, outdated, or a single README that says "run npm install."

You need to understand this code. You need others to understand it too. But reading every line isn't realistic. You have actual work to do.

Here's how to document a legacy codebase efficiently, using modern tools and proven strategies.

Why Legacy Code Documentation Fails

Before diving into solutions, let's understand why most legacy documentation efforts fail:

The "document everything" trap. Teams try to document every function, every file, every decision. They burn out after two weeks with 10% coverage.

The wiki graveyard. Documentation lives in Confluence or Notion, separate from the code. It goes stale within months. Nobody trusts it.

The single-author problem. One person writes all the docs. When they leave, both the knowledge and the documentation practice leave with them.

The perfectionism paralysis. Teams wait for the "right time" to document properly. That time never comes.

The solution isn't to try harder at traditional documentation. It's to change the approach entirely.

Step 1: Map the Territory First

Before writing any documentation, understand what you're dealing with. You don't need to read every line—you need to see the shape of the codebase.

Use automated analysis tools

Static analyzers and dependency graphers can reveal structure in minutes that would take days to discover manually:

Dependency graphs show how modules connect. Look for clusters (related functionality) and bridges (critical integration points).
Complexity metrics identify the scariest parts of the codebase. High cyclomatic complexity usually means high documentation priority.
Dead code detection shows what you can ignore entirely.

Identify entry points

Every codebase has entry points, the places where execution begins:

HTTP route handlers
CLI command definitions
Event listeners and queue consumers
Scheduled job definitions

Document these first. They're the map legend for the rest of the codebase.

Find the critical paths

What are the most important user journeys? Trace them through the code:

User clicks "checkout" → what code runs?
API receives a webhook → where does it go?
Nightly job runs → what does it touch?

These paths are your documentation priorities. Everything else is secondary.

Step 2: Let AI Do the Heavy Lifting

AI tools have transformed legacy code documentation. They can analyze code and generate explanations faster than any human, and they don't get bored.

Generate baseline documentation automatically

Tools like Code Summary can analyze your entire codebase and generate documentation automatically:

Architecture overviews showing how components connect
Module documentation explaining what each part does
API documentation with endpoints, parameters, and responses
Setup guides for getting the project running

This isn't perfect documentation, but it's dramatically better than nothing. It gives you a foundation to build on.

Use AI assistants for explanation

When you encounter confusing code, ask an AI to explain it:

Explain what this function does, what inputs it expects,
and what side effects it has. Note any non-obvious behavior.

AI assistants like GitHub Copilot, Claude, and Cursor can parse complex logic and explain it in plain English. They're particularly good at:

Explaining regex patterns
Describing algorithm logic
Identifying what a function's dependencies suggest about its purpose
Summarizing long functions

Generate docstrings and comments

For critical functions lacking documentation, AI can generate docstrings:

# Before
def calc_adj_price(p, d, t, r):
    if t == 'premium':
        return p * (1 - d) * (1 + r * 0.1)
    return p * (1 - d)

# After (AI-generated docstring)
def calc_adj_price(p, d, t, r):
    """
    Calculate adjusted price based on discount and customer tier.

    Args:
        p: Base price
        d: Discount rate (0-1)
        t: Customer tier ('premium' or 'standard')
        r: Loyalty rating (0-10)

    Returns:
        Adjusted price with discount applied.
        Premium customers get additional loyalty bonus.
    """
    if t == 'premium':
        return p * (1 - d) * (1 + r * 0.1)
    return p * (1 - d)

The AI-generated docs aren't always perfect, but they're a starting point. Review them, correct any errors, and commit.

Step 3: Document Where the Code Lives

Documentation that lives separately from code dies. Put your documentation where developers actually look.

README files in each major directory

Every significant directory should have a README explaining:

What this directory contains
How it relates to other parts of the system
Any conventions specific to this area

# /services/payments

Payment processing service. Handles Stripe integration,
refunds, and subscription billing.

## Key Files
- `stripe-client.ts` - Stripe API wrapper
- `webhook-handler.ts` - Processes Stripe webhooks
- `subscription-manager.ts` - Manages recurring billing

## Dependencies
- Requires `STRIPE_SECRET_KEY` environment variable
- Writes to `payments` and `subscriptions` tables
- Publishes events to `payments.*` queue

## Testing
Run `npm test -- --grep payments` for payment-specific tests.
Requires test Stripe keys in `.env.test`.

Architecture Decision Records (ADRs)

When you discover why something was built a certain way, capture it in an ADR:

# ADR-007: Using Redis for Session Storage

## Status
Accepted (discovered during legacy review, original date unknown)

## Context
Sessions were originally stored in PostgreSQL. At some point,
the team switched to Redis.

## Decision
Sessions are stored in Redis with 24-hour TTL.

## Consequences
- Faster session lookups
- Sessions lost if Redis restarts (acceptable for this app)
- Requires Redis in all environments

## Notes
Found this pattern while investigating auth issues.
Documenting to prevent future confusion.

ADRs capture the "why" that code alone can't express.

Code comments for the non-obvious

Add comments only where behavior isn't obvious from the code:

// Good: explains WHY
// Retry with exponential backoff because Stripe
// rate-limits during high-traffic periods
await retry(stripeCall, { maxAttempts: 5, backoff: 'exponential' });

// Bad: explains WHAT (obvious from code)
// Call the Stripe API
await stripe.charges.create(chargeData);

Focus comments on:

Business logic that isn't obvious
Workarounds for bugs in dependencies
Performance optimizations that look weird
Anything that made you ask "why is this here?"

Step 4: Use Tests as Documentation

Tests are documentation that can't lie. If a test passes, the documented behavior is true.

Write characterization tests

When you don't know what code should do, write tests that capture what it does do:

describe('PriceCalculator', () => {
  it('applies 10% premium bonus for loyalty rating 5', () => {
    // Discovered this behavior while investigating pricing bugs
    const result = calcAdjPrice(100, 0.1, 'premium', 5);
    expect(result).toBe(94.5); // 100 * 0.9 * 1.05
  });

  it('ignores loyalty rating for standard tier', () => {
    const result = calcAdjPrice(100, 0.1, 'standard', 10);
    expect(result).toBe(90); // Loyalty rating has no effect
  });
});

These tests document behavior and protect against accidental changes.

Name tests as specifications

Test names should read like specifications:

// Good: describes behavior
it('rejects orders when inventory is insufficient')
it('sends email notification after successful payment')
it('retries failed webhook deliveries up to 3 times')

// Bad: describes implementation
it('calls inventoryService.check()')
it('uses nodemailer')
it('has retry loop')

Future developers will read test names to understand what the system does.

Step 5: Prioritize Ruthlessly

You can't document everything. Prioritize based on impact:

High priority (document immediately)

Entry points: Routes, commands, event handlers
Core business logic: The code that makes money
Integration points: External APIs, databases, queues
Authentication/authorization: Security-critical code
The scary parts: High complexity, no tests, everyone avoids them

Medium priority (document when you touch it)

Utility functions: Document when you use them
Configuration: Document when you change it
Build/deploy scripts: Document when they break

Low priority (don't bother)

Dead code: Delete it instead of documenting it
Deprecated features: Mark deprecated, don't over-document
Obvious code: Self-documenting code doesn't need comments

Step 6: Keep Documentation Current

Documentation that goes stale is worse than no documentation. It actively misleads.

Automate where possible

Use tools that update documentation automatically:

Code Summary regenerates documentation on every push
TypeDoc/JSDoc generates API docs from code comments
OpenAPI/Swagger generates API docs from route definitions

Automation means documentation stays current without ongoing effort.

Make updating frictionless

The harder it is to update docs, the less they get updated:

Keep docs in the repo, not a separate wiki
Use Markdown, not complex documentation systems
Review documentation changes in PRs
Make "update the docs" part of your definition of done

Delete aggressively

Outdated documentation should be updated or deleted. There's no third option.

When you find docs that don't match reality:

Can you update them quickly? Do it.
Too complex to update now? Delete them and add a TODO.
Not sure if they're wrong? Add a warning and verify later.

Getting Started Today

You don't need a documentation sprint. You need a sustainable practice:

This week:

Set up Code Summary to generate baseline documentation
Add a README to the most confusing directory
Write one ADR capturing something you learned

This month:

Map the critical paths through your system
Add characterization tests to the scariest code
Document all entry points

Ongoing:

Document as you learn. Every confusion is a documentation opportunity
Review generated docs and improve them over time
Delete outdated docs rather than letting them rot

Legacy code doesn't have to stay mysterious. With the right tools and approach, you can build understanding incrementally, without reading every line.