Can AI Debug a Real Application? We Give It a Broken Project

Overview | AI Debugging

AI coding tools have become increasingly popular among developers. They can generate code, explain programming concepts, build application features, and even suggest solutions to software errors. But how well do they perform when an actual application is broken? Generating a small code snippet is one thing. Debugging an application with multiple errors, unexpected behaviour, and interconnected components is a different challenge.

In this experiment-style guide, we'll explore how an AI coding assistant can approach a deliberately broken application, identify common programming mistakes, suggest fixes, and help restore functionality. We'll also examine where AI debugging can fall short and whether developers can rely on it for real projects.

Can AI Actually Debug an Application?

Yes, Modern AI coding assistants can analyse source code, interpret error messages, identify common programming mistakes, and suggest corrections. Depending on the tool and its capabilities, AI can also inspect multiple project files, explain how different components interact, and modify code directly.

Some popular AI coding assistants include:

• ChatGPT: Can analyse code, explain errors, suggest fixes, and work with uploaded project files.

• GitHub Copilot: Provides code suggestions and debugging assistance inside supported development environments.

• Google Gemini: Can analyse code, explain programming issues, and help troubleshoot application errors.

• Claude: Can examine larger codebases and assist with debugging, refactoring, and code analysis.

However, finding a possible solution doesn't necessarily mean the application will work correctly. The real test is whether the suggested changes fix the underlying problems without introducing new ones.

Our Test: A Deliberately Broken Application

To understand AI debugging, let's use a small web application as our example. Imagine a simple task management application built with HTML, CSS, and JavaScript. Users should be able to add tasks, mark them as completed, and delete them. The application appears straightforward, but several programming mistakes have been introduced.

The application has the following problems:

Problem	Expected behaviour
Add button doesn't work	Users should be able to add new tasks
Delete button fails	Users should be able to remove tasks
Completed status doesn't update	Clicking a task should mark it completed
Empty tasks are accepted	The application should reject blank entries
Tasks disappear after refreshing	Saved tasks should remain available

These problems represent common issues developers encounter when building interactive applications. Instead of fixing everything manually, we'll explore how an AI assistant can investigate the project.

Step 1: Give the Broken Project to AI

The first step is to provide the AI coding assistant with the project files. For a small application, you can upload the relevant HTML, CSS, and JavaScript files. For a larger project, use a coding assistant that can inspect the project directory and access multiple files. The important thing is to provide enough context for the AI to understand how the application works.

You can start with a prompt like this, example debugging prompt:

"Analyse this application and identify why its main features are not working. Check the JavaScript logic, event listeners, DOM elements, and any other relevant code. Don't rewrite the entire application immediately. First, explain the errors you find and suggest the necessary fixes."

This approach encourages the AI to investigate the existing code rather than immediately replacing it with a completely different implementation.

Step 2: Let AI Identify the Errors

After analysing the source code, an AI assistant may identify issues such as:

• Incorrect element selectors that prevent JavaScript from finding HTML elements.

• Missing or incorrectly configured event listeners.

• Functions that reference undefined variables.

• Incorrect conditions that prevent buttons from performing their intended actions.

• Missing input validation.

• Storage logic that doesn't save or retrieve application data correctly.

For example, consider this JavaScript code:

const addButton = document.getElementById("add-task");

addButton.addEventListener("click", addTask);

If the actual HTML button uses a different ID, JavaScript won't find the expected element. This can cause an error when the application attempts to attach the event listener. An AI assistant can compare the HTML and JavaScript files, identify the mismatch, and suggest the correct selector.

This is one of the areas where AI debugging can save considerable time, especially when the error is spread across multiple files.

Step 3: Ask AI to Fix the Broken Code

Once the problems have been identified, the next step is to request corrections. Instead of asking the AI to rebuild the entire application, provide a more specific instruction: "Fix the identified errors while preserving the existing application structure, design, and functionality. Make only the necessary changes and explain what you modified."

A good AI coding assistant should then provide corrected code or modify the relevant files, depending on the environment.

For our example application, the expected corrections include:

1. Fixing the button selectors and event listeners.

2. Correcting the task creation and deletion functions.

3. Updating the completed status when a user clicks a task.

4. Adding validation to prevent empty entries.

5. Implementing suitable browser storage so tasks can persist after refreshing.

The important part is that the AI should fix the actual causes of the problems instead of simply hiding error messages.

Step 4: Test Whether the Fixes Actually Work

This is arguably the most important part of AI-assisted debugging. Even if an AI assistant provides corrected code, you should never assume that the application is fixed without testing it.

For our task management example, we would check the following:

Test	What to verify
Add a task	A new task appears correctly
Delete a task	The selected task disappears
Complete a task	Its status changes visually
Submit an empty task	The application rejects the input
Refresh the page	Previously saved tasks remain available

You should also check the browser's developer console for JavaScript errors and test different user interactions. If something still doesn't work, provide the new error message or unexpected behaviour to the AI and ask it to investigate further.

This creates a debugging cycle: Identify the error → Apply the fix → Run the application → Test the result → Repeat if necessary.

How Accurate Is AI at Debugging Real Applications?

AI can be very effective at identifying common programming mistakes, particularly when the error message and relevant source code are available. However, its accuracy depends on the complexity of the application, the quality of the available context, and the type of error being investigated.

Here's a practical comparison:

Type of issue	AI debugging capability
Syntax errors	Generally very good
Missing variables or functions	Generally good
Incorrect event listeners	Generally good
Basic UI logic errors	Often effective
API integration problems	Depends on available context
Database-related issues	Requires access to relevant code and error details
Complex application architecture	More challenging
Intermittent production errors	Often requires additional logs and investigation

AI is particularly useful when the problem is clearly visible in the source code. It becomes more challenging when the issue depends on external services, server configurations, database states, or conditions that are difficult to reproduce.

Where AI Debugging Can Fail

Despite its capabilities, AI debugging isn't perfect. There are several situations where an AI-generated fix may be incomplete or incorrect.

1. Fixing Symptoms Instead of Root Causes

   
   

   An AI assistant may correct the visible error without addressing the underlying problem. This can make the application appear functional while leaving other issues unresolved.

2. Introducing New Bugs

   
   

   A suggested code change can unintentionally affect another feature. This is particularly common when multiple application components depend on the same functions or variables.

3. Missing Project Context

   
   

   If you provide only one JavaScript file while the problem depends on another component, the AI may not have enough information to identify the real cause.

4. Difficulty With Complex Errors

   
   

   Some bugs only appear under specific conditions, such as unusual user input, network interruptions, concurrent requests, or production environments.

   
   

   An AI assistant may struggle to reproduce these problems without relevant logs, test cases, and access to the necessary environment.

5. Incorrect or Outdated Suggestions

   
   

   AI-generated code may rely on outdated APIs, incorrect assumptions, or programming patterns that don't match the project's dependencies. This is why developers should review generated changes and test them before deployment.

Can AI Replace a Human Developer for Debugging?

Not entirely, AI can assist with many debugging tasks, but it doesn't replace the need for human judgement, software testing, and technical understanding. A developer can evaluate whether a proposed fix makes sense within the application's architecture, identify security implications, and understand business requirements that may not be visible in the source code. The most effective approach is to use AI as a debugging assistant rather than treating it as an independent authority.

For experienced developers, it can reduce the time spent investigating routine errors. For beginners, it can explain programming mistakes and provide practical examples of how to correct them.

Conclusion: Is AI Good Enough to Debug Real Applications?

AI coding assistants have become useful debugging tools. They can analyse source code, identify common errors, explain programming problems, and suggest corrections that help restore application functionality. However, debugging isn't just about making an error message disappear. A proper fix must address the underlying problem without breaking other parts of the application. Our broken task management example illustrates how AI can approach this process, from inspecting source files to proposing fixes and preparing tests.

But a suggested correction should only be considered successful after it has been implemented and verified. The real advantage of AI debugging isn't that developers no longer need to fix bugs themselves. It's that they can investigate problems faster, understand errors more easily, and spend more time improving their applications.

Author: Kalyan Bhattacharjee

Category: Technology Reviews | AI & Machine Learning | Programming

Expertise: Technology Analyst & Digital Research Writer | 5+ Years of Experience

Source: Research-based content using publicly available technical resources and industry references

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Frequently Asked Questions About AI Debugging

Find answers to common questions about AI debugging, including how AI coding assistants identify errors, fix broken code, and how reliable their suggested solutions are.

1. Can ChatGPT debug an entire application?

   
   

   Ans: Yes, ChatGPT can analyse application files, explain errors, and suggest corrections. For larger projects, the quality of its analysis depends on the available files, context, and tools.

2. Which AI tool is best for debugging code?

   
   

   Ans: ChatGPT, GitHub Copilot, Gemini, and Claude are all useful options. The best choice depends on your programming language, development environment, project size, and whether you need code explanations or direct editing assistance.

3. Can AI find bugs without error messages?

   
   

   Ans: Yes, AI can inspect source code and identify suspicious logic, missing conditions, and potential programming mistakes even without an error message. However, logs and reproduction steps make debugging more accurate.

4. Can AI fix bugs in HTML, CSS, and JavaScript?

   
   

   Ans: Yes, AI assistants can identify and correct common HTML, CSS, and JavaScript issues, including broken layouts, event listener problems, incorrect selectors, and faulty application logic.

5. Should I trust AI-generated code fixes?

   
   

   Ans: You should review and test them before using them in a real application. AI can suggest incorrect changes or introduce new bugs, so proper testing and code review remain important.