How Do You Debug Code You Don't Understand? A Practical Guide for Developers

Apr 4, 2026

I stared at the legacy authentication module. Three hundred lines of tangled logic, no tests, zero documentation. The bug report said “login fails randomly,” and I had exactly zero understanding of how this code worked.

I tried debugging it the usual way. I added print statements, changed random variables, and prayed. Two hours later, I had nothing but frustration.

Then I remembered something a senior developer told me: “If you don’t understand it, you can’t debug it.”

Most developers debug unfamiliar code by trial and error. We change things, see what breaks, and hope for the best. This approach wastes time and creates new bugs.

The real issue isn’t the bug. It’s that we’re trying to fix something we don’t understand.

I read a Reddit discussion that captured this perfectly:

“I friggin’ hate hate hate trying to debug stuff I don’t understand. It’s hard enough to debug stuff I do understand. So I make sure I understand stuff.”

Another comment hit harder:

“If you’re regularly accepting code you can’t recreate from memory or explain line by line, you’re borrowing confidence on credit.”

That hit home. I was borrowing confidence, hoping my random fixes would work.

Step 1: Read Before Fixing

I stopped trying to fix the bug. Instead, I started reading the code line by line.

I added comments to explain what I saw:

def process_items(items, threshold):
    results = []
    for item in items:
        # Check if item value exceeds threshold
        if item.value > threshold:
            # Add to results with a flag
            results.append({
                'id': item.id,
                'value': item.value,
                'flag': True
            })
        else:
            # Add without flag for items below threshold
            results.append({
                'id': item.id,
                'value': item.value,
                'flag': False
            })
    # Return processed results
    return results

This felt slow. I wanted to jump straight to fixing. But after 20 minutes of commenting, I understood the data flow.

Step 2: Use the Scientific Method

I treated debugging like a science experiment:

Form a hypothesis about the bug
Predict what should happen if I’m right
Test and observe the actual result
Update my understanding

For the authentication module, my hypothesis was: “The session validation fails when concurrent requests hit the same session token.”

I added logging to test this:

function validateSession(token) {
    console.log(`Validating session: ${token}`)
    console.log(`Current sessions: ${JSON.stringify(activeSessions)}`)

    const session = activeSessions[token]

    if (!session) {
        console.log(`Session not found for token: ${token}`)
        return null
    }

    console.log(`Session found. Expiry: ${session.expiry}`)
    return session
}

I ran the test. The logs showed something unexpected: the session existed, but the expiry time was wrong.

My hypothesis was wrong. But I learned something new.

Step 3: Build Mental Models

I drew a mental map of the code:

Inputs: What data comes in?
Outputs: What should come out?
State changes: What gets modified?
Dependencies: What external systems does it touch?

For a validation function I was debugging, I built this understanding:

interface ValidationResult {
    valid: boolean
    errors: string[]
}

function validateUsers(users: User[]): ValidationResult {
    const errors: string[] = []

    // Input: array of user objects
    // Expected: each user has email and age

    users.forEach((user, index) => {
        if (!user.email) {
            errors.push(`User ${index}: missing email`)
        }
        if (!user.email.includes('@')) {
            errors.push(`User ${index}: invalid email format`)
        }
        if (user.age < 0 || user.age > 150) {
            errors.push(`User ${index}: invalid age`)
        }
    })

    // Output: validation result with boolean and error list
    return {
        valid: errors.length === 0,
        errors: errors
    }
}

Now I could reason about the code. When a test failed, I knew where to look.

Step 4: Leverage Debugging Tools

I used three tools consistently:

Breakpoints: I’d pause execution and inspect variables. This showed me actual state versus expected state.

Rubber duck debugging: I explained the code out loud, line by line. Sounds silly, but I caught logic errors this way.

Logging: Strategic logging revealed the actual flow, not what I assumed was happening.

The authentication bug? It turned out to be a race condition where two threads modified the same session object. I found it because my logs showed overlapping modifications.

[Thread-1] Validating session abc123
[Thread-2] Validating session abc123
[Thread-1] Session found, updating last_access
[Thread-2] Session found, updating last_access
[Thread-1] Writing session to database
[Thread-2] Writing session to database <- Overwrites Thread-1's changes

The Real Fix

The bug wasn’t in the validation logic. It was in the concurrent access pattern. Without understanding the code first, I would have added more locks or random delays, making it worse.

I added a simple mutex lock around session modifications:

import threading

session_lock = threading.Lock()

def update_session(token, data):
    with session_lock:  # Ensure only one thread modifies at a time
        session = active_sessions.get(token)
        if session:
            session.update(data)
            save_to_database(session)

The random login failures stopped. Total debugging time: 45 minutes, including the time spent understanding the code.

What I Learned

I used to think understanding code was a luxury. Now I see it as a prerequisite for debugging.

The next time you face unfamiliar, buggy code:

Stop trying to fix it immediately
Read and comment until you can explain it
Form hypotheses and test them
Build mental models of inputs, outputs, and state
Use tools to observe actual behavior

You’ll spend more time upfront understanding, but you’ll save hours of random fixes. And you’ll stop borrowing confidence on credit.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

👨‍💻 Reddit Discussion: How do you debug code you don't understand?

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!