How to Debug Your Code with AI Tools in Under 30 Minutes

Debugging code can be a frustrating and time-consuming process, especially when you’re racing against a deadline. If you’re anything like me, you’ve probably spent hours staring at lines of code, trying to decipher what went wrong. The good news is that with the rise of AI tools, debugging can be done more efficiently and, dare I say, even a bit fun. In this article, I’ll show you how to leverage AI tools to debug your code in under 30 minutes.

Prerequisites for Debugging with AI

Before you dive in, here’s what you’ll need:

• Basic coding knowledge: This guide assumes you have a working understanding of at least one programming language.
• Access to an AI debugging tool: We’ll cover several options below, so check your budget.
• An IDE or code editor: Make sure you have your coding environment set up.

Step-by-Step Guide to Debugging with AI Tools

Step 1: Choose Your AI Debugging Tool

Here’s a quick comparison of popular AI debugging tools available in 2026:

| Tool Name | Pricing | Best For | Limitations | Our Take | |------------------|---------------------------------|--------------------------------|-------------------------------------|-----------------------------------| | GitHub Copilot | $10/mo (individual) | Pair programming | Limited to GitHub environments | We use this for quick code suggestions. | | Tabnine | Free tier + $12/mo pro | Autocompletion | May suggest incorrect syntax | Great for autocompletion but check suggestions. | | Codeium | Free (with optional pro features)| General debugging | Pro version is expensive at $40/mo | Use the free version for basic needs. | | Replit | Free tier + $20/mo for teams | Collaborative debugging | Limited features in free tier | Good for team projects; not ideal for solo work. | | Sourcery | Free + $29/mo for premium | Python debugging | Limited to Python | We don’t use it since we focus on JavaScript. | | DeepCode | Free tier + $19/mo pro | Code quality improvements | Slower response time | Good for code reviews but not fast enough for debugging. | | Codex by OpenAI | $20/mo | General-purpose debugging | Requires API knowledge | Powerful tool, but complex to set up. | | AI Debugger | $15/mo | Front-end JavaScript debugging | Limited to JavaScript | Quick suggestions but lacks depth. | | Ponicode | $29/mo, no free tier | Unit testing | Not a full debugger | Use for writing tests, not debugging. | | Bugfender | $0-30/mo based on usage | Mobile app debugging | Requires app integration | Great for mobile; not for web apps. |

Step 2: Set Up the Tool

Once you’ve chosen a tool, follow these steps to set it up:

1. Install the Tool: Follow the installation instructions specific to your coding environment.
2. Integrate with Your IDE: Most AI tools offer plugins for popular IDEs. Make sure to integrate them properly.
3. Configure Settings: Adjust any settings to fit your coding style or preferences.

Step 3: Load Your Code

Open the project or file you want to debug in your IDE. Make sure to have a clear understanding of the issue at hand. Is it a syntax error, a runtime error, or a logical error? This will help the AI tool provide better suggestions.

Step 4: Use the AI Tool to Identify Issues

Run the AI tool and ask it to analyze your code.

• For GitHub Copilot: You can simply start typing your code, and it will suggest corrections or improvements.
• For Tabnine: You might need to highlight the problematic section and invoke the tool to get suggestions.

Step 5: Implement Suggested Fixes

Carefully review the suggestions made by the tool. Not every suggestion will be perfect, so use your judgment to decide what to implement.

Step 6: Test Your Code

After making changes, run your code to see if the issues are resolved. If not, repeat steps 4 and 5 until you’ve successfully debugged your code.

Troubleshooting Common Issues

• The AI tool is not recognizing errors: Ensure it’s properly integrated with your IDE and that you’re using the correct programming language.
• Suggestions don’t make sense: Remember that AI tools learn from patterns; sometimes they might suggest off-the-mark solutions. Always verify before implementing.

What’s Next?

Once you’ve debugged your code, consider using AI tools for other aspects of your development process, like writing tests or optimizing performance.

Conclusion: Start Here

To get started with debugging your code using AI tools, I recommend trying GitHub Copilot if you’re working within the GitHub ecosystem, or Tabnine for general purposes. Both can significantly cut down your debugging time and make the process less painful.

Remember, while AI tools can speed up debugging, they’re not infallible. Always keep a critical eye on their suggestions and rely on your coding instincts.