Debugging with AI: GitHub Copilot vs Claude vs ChatGPT

We’ve all been there – a production bug that seems impossible to track down, even with years of experience. Recently, I had a particularly tricky Python issue where the stack trace was misleading and traditional debugging approaches weren’t cutting it quickly enough.

That got me thinking: with AI coding assistants becoming mainstream, which one actually delivers when you need to solve real problems fast? I decided to put GitHub Copilot, Claude, and ChatGPT through their paces on the same challenging debugging scenarios to find out.

The Testing Setup

I collected five real bugs from my recent projects – the kind that make you question your career choices. Then I fed the exact same problem to GitHub Copilot, Claude, and ChatGPT, using each tool exactly how a normal developer would.

No cherry-picking results. No perfect prompts crafted after 10 attempts. Just real debugging sessions with real frustration.

Here’s what I learned.

Scenario 1: The Mysterious Python Exception

The Problem: A data processing script that worked fine in development but crashed in production with this beauty:

File "process_data.py", line 47, in process_batch
    batch_size = config['batch_size']
KeyError: 'batch_size'

The config file clearly had a batch_size key. I checked it manually three times.

My Prompt: “I’m getting a KeyError for ‘batch_size’ but the key exists in my config file. Here’s the code and error…”

GitHub Copilot’s Response

Copilot immediately suggested the issue might be case sensitivity or whitespace. It generated this fix:

# Before
batch_size = config['batch_size']

# Copilot's suggestion
batch_size = config.get('batch_size', 32)  # fallback to default

Copilot was fast – suggestions appeared as I typed. But it didn’t explain why this was happening. The .get() method was a decent bandaid, but I still didn’t understand the root cause.

Speed: Instant
Explanation Quality: Minimal
Solution: Workaround, not a fix

Claude’s Response

Claude asked follow-up questions about my config loading process. When I shared more context, it identified the real issue:

“The KeyError suggests your config dictionary is missing the key at runtime, even though it exists in the file. This often happens when: 1) The config file isn’t being loaded from the path you think it is, or 2) There’s an encoding issue, or 3) The config is being modified after loading.”

Claude then walked me through debugging steps and helped me discover that my production environment was loading a different config file. The actual fix was updating my file path resolution.

Speed: 30 seconds for initial response
Explanation Quality: Excellent – taught me why
Solution: Found the root cause

ChatGPT’s Response

ChatGPT gave me a comprehensive list of potential causes, ranked by likelihood. It suggested I add debugging prints to verify the config contents at runtime:

print(f"Config keys: {list(config.keys())}")
print(f"Looking for: 'batch_size'")
print(f"Config content: {config}")

This approach worked well. The debugging output revealed that my config was actually empty in production, which led me to the same path resolution issue Claude identified.

Speed: 15 seconds
Explanation Quality: Good – systematic approach
Solution: Guided me to find the root cause

Scenario 2: JavaScript Async Hell

The Problem: A race condition in my Node.js API where user data sometimes wasn’t saved before the response was sent:

async function updateUserProfile(userId, profileData) {
    const user = await User.findById(userId);
    user.profile = profileData;
    user.save(); // Sometimes this doesn't finish before return
    return { success: true, user: user };
}

My Prompt: “My user updates work inconsistently. Sometimes the data saves, sometimes it doesn’t. Here’s my async function…”

GitHub Copilot

Copilot immediately highlighted the missing await:

// Copilot's inline suggestion
await user.save(); // Added await keyword

Perfect fix, shown instantly as I was editing the code. But again, no explanation of why this mattered or what was actually happening.

Winner for speed: Got the fix before I finished typing the function.

Claude

Claude explained the race condition clearly: “You’re not waiting for the save operation to complete. Without await, your function returns immediately while the database write happens in the background.”

It also suggested additional improvements:

async function updateUserProfile(userId, profileData) {
    try {
        const user = await User.findById(userId);
        if (!user) {
            throw new Error('User not found');
        }
        user.profile = profileData;
        await user.save();
        return { success: true, user: user };
    } catch (error) {
        return { success: false, error: error.message };
    }
}

Winner for learning: I understood async patterns better after this explanation.

ChatGPT

ChatGPT identified the missing await and provided a detailed explanation of JavaScript’s event loop and how async operations work. It included multiple examples of similar bugs and how to avoid them.

The response was thorough but felt like reading documentation when I just wanted to fix my bug and move on.

Scenario 3: Java NullPointerException Mystery

The Problem: A Spring Boot application throwing NPE in a service class, but the stack trace was pointing to framework code, not my code:

java.lang.NullPointerException: null
    at com.otp.service.UserService.processUser(UserService.java:45)
    at com.otp.controller.UserController.updateUser(UserController.java:23)

Line 45 was: return userRepository.findByEmail(email).orElse(null);

My Prompt: “Getting NPE on a line that looks safe. The Optional.orElse should handle null cases. Here’s the stack trace and code…”

GitHub Copilot

Copilot suggested the email parameter might be null:

// Copilot's suggestion
if (email == null || email.isEmpty()) {
    return null;
}
return userRepository.findByEmail(email).orElse(null);

This was correct! The NPE was happening because findByEmail(null) was throwing an exception before the Optional wrapper could handle it.

Claude

Claude identified the same issue but provided more context: “JPA repository methods typically don’t handle null parameters gracefully. The NPE occurs inside the repository method, not in your Optional handling.”

It suggested both a null check and a more robust approach using Optional for the input parameter:

public User processUser(String email) {
    return Optional.ofNullable(email)
        .filter(e -> !e.isEmpty())
        .flatMap(userRepository::findByEmail)
        .orElse(null);
}

ChatGPT

ChatGPT provided a thorough analysis of Java Optional best practices and explained why JPA repositories behave this way. It suggested similar fixes but spent more time on the theory than the immediate solution.

Winner: Claude struck the right balance between fixing the bug and explaining the underlying concept.

Scenario 4: Performance Bottleneck Hunt

The Problem: A Python data processing script that was taking 20 minutes to process 10,000 records. No obvious performance issues in the code.

def process_records(records):
    results = []
    for record in records:
        if is_valid_record(record):
            processed = transform_record(record)
            results.append(processed)
            save_to_database(processed)  # This line was the killer
    return results

My Prompt: “This function is really slow with large datasets. Can you help identify bottlenecks? Here’s the code…”

GitHub Copilot

Copilot suggested using list comprehension and batch database operations:

def process_records(records):
    valid_records = [transform_record(r) for r in records if is_valid_record(r)]
    # Batch save instead of individual saves
    bulk_save_to_database(valid_records)
    return valid_records

The suggestion was solid but appeared without much explanation of why this would be faster.

Claude

Claude immediately spotted the N+1 database problem: “You’re making a database call for every record. This creates thousands of individual transactions instead of one batch operation.”

It provided a complete refactored solution with batching and explained the performance implications of each change. Claude also suggested profiling tools to confirm the bottleneck.

ChatGPT

ChatGPT gave me a comprehensive performance analysis, including time complexity explanations and multiple optimization strategies. It was thorough but overwhelming when I just needed to fix one slow function.

Winner: Claude – identified the exact issue and provided a focused solution.

Scenario 5: Logic Error in Algorithm

The Problem: A binary search implementation that worked for some inputs but failed for others:

def binary_search(arr, target):
    left, right = 0, len(arr)
    while left < right:
        mid = (left + right) // 2
        if arr[mid] == target:
            return mid
        elif arr[mid] < target:
            left = mid
        else:
            right = mid
    return -1

My Prompt: “My binary search works sometimes but returns -1 for values that are definitely in the array. Can you spot the bug?”

GitHub Copilot

Copilot suggested changing left = mid to left = mid + 1:

elif arr[mid] < target:
    left = mid + 1  # Added +1

Correct fix, but no explanation of why this caused infinite loops in certain cases.

Claude

Claude explained the infinite loop issue: “When arr[mid] < target, setting left = mid means you might check the same middle element repeatedly if mid doesn’t change between iterations.”

It provided the fix with a clear explanation of how binary search boundaries should work.

ChatGPT

ChatGPT walked through the algorithm step-by-step with example inputs, showing exactly how the infinite loop occurred. The explanation was detailed and educational.

Winner: Tie between Claude and ChatGPT – both provided excellent explanations.

The Verdict: Which Tool for Which Job

After testing these scenarios and many others over the past month, here’s when I reach for each tool:

GitHub Copilot: Best for Quick Fixes

Use when: You know what’s wrong but need the fix fast.

Copilot excels at catching obvious mistakes while you code. Missing await keywords, typos in variable names, simple logic errors – it catches these instantly.

Perfect for: Syntax errors, common patterns, boilerplate code

Limitations: Minimal explanations, doesn’t help you learn why something broke

Cost: $10/month for individuals

Claude: Best for Learning and Complex Debugging

Use when: You want to understand why something broke, not just fix it.

Claude asks good follow-up questions and explains the reasoning behind its suggestions. It’s excellent for complex bugs where understanding the root cause matters.

Perfect for: Performance issues, architectural problems, learning new patterns

Limitations: Slightly slower than the others

Cost: $20/month for Pro (free tier available)

ChatGPT: Best for Comprehensive Analysis

Use when: You have time and want to explore multiple solutions.

ChatGPT provides thorough analysis but can be overwhelming when you just need a quick fix. Great for learning sessions or when you’re stuck on a particularly tricky problem.

Perfect for: Algorithm problems, comprehensive debugging, educational explanations

Limitations: Can be verbose, sometimes over-explains simple issues

Cost: $20/month for Plus (free tier available)

Effective Prompts That Actually Work

After hundreds of debugging sessions, these prompts consistently get better results:

For Quick Fixes (Copilot):

Just paste your code and highlight the problematic line. Copilot works best with minimal context.

For Root Cause Analysis (Claude):

"I'm getting [specific error] in [language/framework]. 
Here's the minimal code that reproduces it: [code]. 
The expected behavior is [X] but I'm seeing [Y]. What's the most likely cause?"

For Comprehensive Help (ChatGPT):

"Debug this [language] code. Error: [exact error message]. 
Code: [code block]. 
Context: [what you're trying to accomplish]. 
I've tried: [what you've already attempted]."

Integration Tips for Your Workflow

Start with Copilot for immediate feedback while coding. If it catches the bug, you’re done in seconds.

Escalate to Claude when you need to understand why something broke or when dealing with complex logic issues.

Use ChatGPT for learning sessions or when you have time to explore different approaches.

Use multiple tools together for stubborn bugs: Copilot for quick fixes, Claude for explanation, ChatGPT for alternative approaches.

The Bottom Line

None of these tools will replace good debugging skills, but they’ll make you significantly faster at finding and fixing bugs.

Copilot saves me the most time day-to-day with its instant suggestions. Claude teaches me the most and helps with complex architectural issues. ChatGPT provides the most comprehensive analysis when I need to fully understand a problem.

The best strategy? Start with the fastest tool (Copilot) and escalate to the others when you need deeper analysis. Your debugging sessions will go from hours to minutes, and you’ll learn more in the process.

That 2 AM debugging session I mentioned? With these tools, I would have solved it in 10 minutes instead of 3 hours. The future of debugging is here, and it’s pretty damn helpful.

Happy Learning 🙂