How to Debug .NET Core Using AI Tools: A Developer’s 2025 Guide

Introduction: The Debugging Dilemma in .NET Core
Picture this: It’s 2 a.m., and you’ve spent hours chasing a mysterious null reference exception in your .NET Core API. The coffee’s cold, your patience is thinner than a stale cookie, and the deadline is looming. Sound familiar? You’re not alone. Debugging is a universal pain point for developers—but what if AI could turn this nightmare into a 20-minute fix?

Enter AI-powered debugging tools—the game-changers reshaping how we troubleshoot .NET Core applications. In this guide, you’ll learn how to debug .NET Core using AI tools like GitHub Copilot, Visual Studio IntelliCode, and ML.NET. We’ll walk through real examples, stats, and step-by-step workflows to help you slash debugging time, reduce errors, and ship code faster. Let’s dive in.

Why Debug .NET Core Using AI Tools?

Before we explore the “how,” let’s address the “why.” Traditional debugging often feels like finding a needle in a haystack. You set breakpoints, inspect variables, and pray for a “Eureka!” moment. But AI flips the script:

• • Predictive Analysis: AI tools anticipate errors before runtime.

• • Automated Fixes: Get instant code suggestions instead of scouring Stack Overflow.

• • Time Savings: Developers using AI debugging report 55% faster resolution times (GitHub, 2023).

For .NET Core developers, this is revolutionary. As more teams adopt .NET Core for its cross-platform flexibility and performance, AI tools are becoming essential for managing complex, distributed systems.

Top AI Tools to Debug .NET Core Applications

Here are the top AI-powered tools transforming .NET Core debugging in 2024:

1. GitHub Copilot: Your AI Pair Programmer

GitHub Copilot isn’t just for writing code—it’s a debugging powerhouse. Trained on billions of lines of code, it suggests fixes for runtime errors in C# and ASP.NET Core.

Example:
Suppose your .NET Core API throws a System.NullReferenceException. Copilot analyzes the stack trace, highlights the problematic line, and recommends a null-check fix:

csharp code

// Before  
var user = _repository.GetUser(id);  
return user.Name;  

// Copilot Suggestion  
var user = _repository.GetUser(id);  
return user?.Name ?? "Guest"; // Adds null conditional operator  

Stat: Developers using Copilot resolve .NET Core bugs 40% faster (GitHub, 2023).

2. Visual Studio IntelliCode: Smarter Code Completion

IntelliCode uses machine learning to predict your next move. It flags logical errors in real time and auto-completes code based on best practices.

Use Case:
While building a .NET Core MVC app, IntelliCode detects an unhandled exception in a controller method and suggests adding [ValidateAntiForgeryToken] to prevent CSRF attacks.

3. JetBrains Rider + AI Plugins

Rider’s AI Assistant integrates tools like AWS CodeGuru to analyze .NET Core performance bottlenecks. It’s ideal for debugging memory leaks in cloud-native apps.

Stat: Teams using AI-assisted debugging report 30% fewer production incidents (Gartner, 2024).

4. ML.NET: Custom AI Models for Error Prediction

With ML.NET, you can train machine learning models to predict errors specific to your .NET Core app.

Example:
A fintech startup trained an ML.NET model to flag transaction API failures caused by invalid currency formats. The result? A 60% drop in support tickets.

Step-by-Step: Debug a .NET Core App with AI

Let’s put theory into practice. Here’s how to debug a .NET Core API using GitHub Copilot:

Scenario: A weather API returns 500 Internal Server Error when querying /weather/{city}.

Step 1: Reproduce the Error

• • Send a GET request to /weather/london.

• • Logs show: InvalidOperationException: No service for type 'IWeatherService'.

Step 2: Let Copilot Investigate

• • Open the faulty controller in Visual Studio:

public class WeatherController : ControllerBase  
{  
    private readonly IWeatherService _weatherService;  

    public WeatherController()  
    {  
        // Missing dependency injection  
    }  

    [HttpGet("{city}")]  
    public ActionResult GetWeather(string city) => Ok(_weatherService.GetForecast(city));  
}  

• • Copilot flags the constructor and suggests:

public WeatherController(IWeatherService weatherService)  
{  
    _weatherService = weatherService;  
}  

Step 3: Apply the Fix

• • Update the constructor and rerun the API.

• • Result: /weather/london now returns a 200 OK with forecast data.

Pro Tip: Pair Copilot with Application Insights to trace AI-suggested fixes in production.

3 Unbeatable Benefits of AI Debugging for .NET Core

1. • Fix Bugs in Minutes, Not Hours
     
     • • AI tools cut debugging time from hours to minutes. A Forrester study found developers using AI spend 62% less time troubleshooting .NET Core apps.

1. • Proactive Error Prevention
     
     • • Tools like IntelliCode warn you about security flaws (e.g., SQL injection risks) as you code.

1. • Cross-Platform Consistency
     
     • • Debug .NET Core on Linux, macOS, or Windows with the same AI tools—no more “works on my machine” excuses.

Case Study: How AI Saved a Startup $200K in Debugging Costs

A healthcare SaaS company migrated its legacy system to .NET Core but faced constant crashes in its patient scheduling API. After integrating GitHub Copilot and ML.NET:

• • 70% fewer deployment failures in 3 months.

• • $200K saved annually in DevOps support costs.

• • Read their full story here.

The Future of Debugging: What’s Next for .NET Core and AI?

• • Azure AI Studio Integration: Microsoft is baking AI-driven debugging directly into Azure’s .NET Core pipelines.

• • AI-Generated Unit Tests: Tools like Ponicode will auto-write test cases for tricky edge cases.

• • Low-Code Debugging: Drag-and-drop AI workflows for citizen developers.

Conclusion: Ready to Supercharge Your .NET Core Debugging?

Debugging doesn’t have to mean late nights and endless frustration. By leveraging AI tools like GitHub Copilot and ML.NET, you can turn tedious bug hunts into quick, proactive fixes—all while boosting code quality and team morale.

Your Next Steps:

1. • Start a free trial of GitHub Copilot.

1. • Explore ML.NET’s tutorials for custom error prediction.

1. • Share this guide with your team (and finally get some sleep).

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