Debug Like a Pro: Improve Your Efficiency with Visual Studio & Copilot

Session Date: May 22, 2025
Duration: 1 hour
Venue: Build 2025 Conference - BRK119
Speakers: Cagri (Charlie) Aslan (Principal Software Engineering Manager, Microsoft), Harshada Hole (Sr. Product Manager, Microsoft)
Link: [Microsoft Build 2025 Session BRK119]

Visual Studio Debugging with Copilot

Executive Summary

This demo-packed session showcases the most powerful and underused debugging features in Visual Studio, enhanced by deep AI integration through GitHub Copilot. Charlie Aslan and Harshada Hole demonstrate how AI transforms traditional debugging workflows from reactive investigation to intelligent, context-aware problem-solving. The session covers essential Copilot features, smart debugging enhancements, advanced analysis capabilities, and profiling tools - all designed to boost developer confidence and productivity.

Key Topics Covered

?? 1. Essential Copilot Features for Debugging Context

Symbol Intelligence and Code Navigation

Hover-Based Documentation:

  • Instant symbol descriptions - One-line summaries for any class, method, or variable
  • Context-aware explanations - Copilot analyzes symbol usage and purpose
  • Zero documentation required - AI generates descriptions from code structure

Live Demonstration: Robot Simulation Analysis

  • WPF application with multi-robot movement simulation showing clustering issues
  • Symbol hover insights - Robot class description, BlueNextColor method explanation
  • Quick code comprehension without reading entire codebase

Semantic Code Search with @solution Context

Traditional vs. AI-Powered Search:

  • Traditional approach: Text matching, manual code exploration
  • AI semantic search: Intent-based queries understanding code purpose
  • Context element usage: #solution for whole-codebase analysis

Example Query and Results:

Query: "Where is the code that moves the robots?"
AI Response: "SimulateOneStep method handles robot movement with three phases:
1. Force calculation step
2. Direction determination  
3. Position updates"

Multi-Model AI Strategy

Model Selection for Different Tasks:

  • GPT-4.1: General-purpose debugging and code analysis
  • Claude 3.5 Sonnet: Enhanced reasoning capabilities for complex problems
  • Model switching: Real-time comparison of AI responses
  • Iterative refinement: Multiple attempts with different models for optimal results

?? 2. Advanced Context Integration and Vision Capabilities

The Importance of Targeted Context

Generic vs. Specific Bug Fixing:

  • Generic approach: “Fix bugs in [method]” produces broad, unfocused suggestions
  • Targeted approach: Specific problem descriptions with visual context
  • Context quality impact: More precise context yields better AI solutions

Visual Debugging with Copilot Vision

Screenshot-Based Problem Analysis:

  • Image integration: Direct screenshot upload to Copilot chat
  • Visual problem description: AI analyzes UI behavior and identifies issues
  • Combined context: Visual evidence + code analysis for targeted fixes

Live Demo Results:

Problem: Robots clustering at edges instead of distributed spacing
AI Analysis: "Robots cluster too close to edges. Issue in repulsion force calculation."
Solution: Minimal fix to force calculation using square distance calculation

Iterative Problem-Solving Strategy

  • Minimal fixes first - Avoid overwhelming changes
  • Incremental improvements - Guide AI toward correct solutions
  • Multiple attempts - Persistence with different prompts and contexts
  • Success through iteration - Final solution achieved through AI collaboration

??? 3. Smart Debugging Features Enhancement

Intelligent Breakpoint Management

Breakpoint Groups and Organization:

  • Grouped breakpoints for different debugging scenarios
  • Bulk enable/disable capabilities for scenario-specific debugging
  • Demo-specific organization - Visualizer demo, debugging demo separation

Force Run to Cursor:

  • Skip all breakpoints and first-chance exceptions
  • Fast-forward debugging to specific code locations
  • Efficient workflow for targeted investigation

Data Visualization and Analysis

IEnumerable Visualizer Enhancements:

  • Grid-like data presentation for complex collections
  • LINQ query filtering with real-time data manipulation
  • AI-powered LINQ suggestions via sparkle button interface

Live Demo: Car List Application

Problem: Expected 300 cars, only getting 253 cars
Investigation: Breakpoint on generateCars function
Discovery: 42 cars with negative prices being filtered out
Root cause: Discount calculation error with fixed vs. percentage values

Copilot-Enhanced LINQ Queries:

User description: "cars with negative price"
Generated LINQ: cars.Where(c => c.Price < 0)
Result: 42 negative-price cars identified and filtered

Conditional Breakpoints with AI Assistance

Smart Breakpoint Conditions:

  • Copilot suggestions for breakpoint conditions based on surrounding code
  • Context-aware recommendations - Understands variable types and logic
  • Reduced manual condition writing - AI provides starting points and syntax guidance

Tracepoint Integration:

  • Logpoint functionality without code modification
  • AI-suggested trace expressions for relevant data logging
  • Output window integration for real-time debugging information

?? 4. Advanced Analysis Features

Exception Assistant with Deep AI Integration

Comprehensive Exception Analysis:

  • Automatic context gathering - Call stack, locals, code snippets
  • Dynamic variable evaluation with user confirmation for side effects
  • Root cause identification through code and data structure analysis

Live Demo: JSON Deserialization Issue

Problem: Empty list returned from API deserialization
AI Analysis: "Response has root object with 'product' property, 
but client attempts to deserialize entire JSON into List<Product>"
Solution: Create wrapper class matching JSON structure
Result: 30 products successfully deserialized

Multi-Language Support:

  • C# exception handling with detailed API mismatch analysis
  • C++ memory management issues and race condition detection
  • Cross-language debugging capabilities

C++ Advanced Debugging Example

Memory Access Violation Analysis:

Problem: Access violation when shutting down multi-threaded log application
Initial AI Analysis: Memory-mapped buffer invalidation clues
Enhanced Context: Attached load processor file with buffer allocation
Final Analysis: "Classic race condition - main thread unmaps buffer 
while processing thread still accessing it"
Solution: Synchronization mechanism with stop signal flag

Variable Analysis for Logic Error Detection

Non-Exception Issue Investigation:

  • Logic error detection without runtime exceptions
  • Variable state analysis for unexpected values
  • API parameter validation with documentation cross-reference

Windows API Example:

Problem: CreateProcess API returning E_INVALID_ARGUMENT
Analysis: Double null-terminated environment block required
Fix: Add additional null terminator to environment variables
Result: Successful process creation

Inline Return Values:

  • Real-time return value display in Visual Studio 17.12+
  • Pre-completion value inspection during function execution
  • Elimination of temporary variables for return value checking

?? 5. Multi-Threaded Application Debugging

Parallel Stacks Window with AI Intelligence

Thread State Summarization:

  • One-line AI summaries for each thread’s current activity
  • Complex thread visualization made comprehensible
  • Real-time thread status understanding

Stock Market Simulation Demo:

Thread Summaries:

- Thread 1: "Waiting for next stock cycle in processing"  
- Thread 2: "Waiting for next stock cycle in processing"
- Garfield Thread: "Executing buy operation, waiting for next stock cycle"

Debugger Context Integration

#debugger Context Element:

  • Complete debugging state capture - Call stacks, variables, thread information
  • Conversational debugging with maintained context
  • Detailed explanations of current execution state

Deadlock Detection and Analysis

AI-Powered Deadlock Investigation:

  • Automatic deadlock detection - Red icons for problematic threads
  • Multi-thread analysis with complete source code context
  • Root cause identification - Lock ordering problems
  • Solution recommendations - Consistent lock acquisition order

Live Demo Results:

Deadlock Analysis: "Multiple locks acquired in different order by different threads"
Problem: Classic lock ordering issue
Solution: "Always acquire locks in consistent order across all threads"
Code Fix: Multiple methods updated with proper lock sequencing

?? 6. Profiling Tools Integration

CPU Usage Analysis with AI Insights

BenchmarkDotNet Integration:

  • Direct CPU trace generation from benchmark runs
  • Benchmark.Diagnosers package for automatic profiling
  • Mean execution time analysis with baseline measurements

AI-Powered Performance Optimization

Copilot Profiling Analysis:

  • Ask Copilot button in CPU usage summary
  • Deep insight analysis of performance bottlenecks
  • Optimization suggestions with code examples

SharpZipLib Optimization Example:

Baseline Performance: 1.59 milliseconds mean execution time
AI Suggestion: "Experiment with different slicing degrees for chunk sizes"
Implementation: Change slicing parameter from 16 to 32
Result: Mean execution time reduced to under 100 microseconds

Profiling Workflow Enhancement

Integrated Analysis Pipeline:

  1. Run benchmarks with automatic trace collection
  2. AI insight generation from performance data
  3. Targeted optimization recommendations
  4. Iterative performance tuning with AI guidance
  5. Quantified improvement measurement

?? 7. LINQ Expression Debugging Innovation

Interactive LINQ Visualization

Hover-Based Query Analysis:

  • Step-by-step data filtering visualization
  • IEnumerable visualizer integration for each LINQ operation
  • Real-time query result inspection at any point in the pipeline

AI-Enhanced Query Correction

Natural Language LINQ Generation:

User Description: "luxury cars, BMW or Audi, white color, price > $45k"
Generated LINQ: cars.Where(c => (c.Brand == "BMW" || c.Brand == "Audi") 
                             && c.Color == "White" 
                             && c.Price > 45000)

Query Debugging Workflow:

  1. Hover over LINQ expression - See intermediate results
  2. Identify empty results or unexpected filtering
  3. Use visualizer sparkle button for AI assistance
  4. Describe desired query in natural language
  5. Get corrected LINQ expression with proper results

Technical Architecture and Integration

AI Model Strategy

Multi-Model Approach:

  • GPT-4.1: Primary model for general debugging tasks
  • Claude 3.5 Sonnet: Enhanced reasoning for complex analysis
  • Model switching: Real-time comparison for optimal results
  • Context preservation: Conversation history maintained across model changes

Context Management System

Built-in Context Elements:

  • #solution: Whole codebase semantic search
  • #debugger: Complete debugging state capture
  • File attachments: Additional code context for targeted analysis
  • Visual context: Screenshot integration for UI behavior analysis

Integration Points

Visual Studio Feature Enhancement:

  • Hover tooltips with AI-generated descriptions
  • Breakpoint conditions with AI suggestions
  • Exception dialogs with analyze buttons
  • Variable watches with AI analysis capabilities
  • Profiling summaries with AI insights

Safety and User Control

Side Effect Management:

  • User confirmation for expressions with potential side effects
  • Safe variable evaluation for simple property access
  • Controlled execution with explicit user approval
  • Rollback capabilities for testing fixes

Live Demo Highlights and Results

Robot Simulation Fix

Problem: Robot clustering behavior at edges Process: Visual debugging with screenshot context Solution: Square distance calculation in force computation Result: Proper distributed robot spacing achieved

Car List Data Analysis

Problem: 253 cars returned instead of expected 300 Process: Breakpoint analysis with data visualization Discovery: 42 cars with negative prices from discount calculation error Result: Fixed discount calculation logic (percentage vs. fixed value)

JSON Deserialization Exception

Problem: Empty list from API response deserialization Process: Exception assistant with variable evaluation Analysis: JSON structure mismatch (root object vs. direct array) Result: Wrapper class implementation with 30 products successfully parsed

Multi-Threading Deadlock

Problem: Stock trading simulation deadlock Process: Parallel stacks analysis with AI interpretation Analysis: Lock ordering problem across multiple threads Result: Consistent lock acquisition order implementation

Performance Optimization

Problem: 1.59ms mean execution time in benchmark Process: CPU profiling with AI insight analysis Suggestion: Slicing degree parameter optimization Result: Sub-100 microsecond execution time (>90% improvement)

Session Highlights

“The more targeted data you can give to Copilot, the better answers you’re going to get from it.” - Charlie Aslan

“I feel like having these analysis features, or even having Copilot in Visual Studio is like a 24/7 expert with me, I can ask any question to it.” - Harshada Hole

“You will not get a one-shot answer or a fix right away in many cases, but you don’t have to give up. This thread you have open here, it contains all the context… you need to keep chatting with the model and working through the problem.” - Charlie Aslan

“Instead of opening up a new thread and starting from scratch… this is going to be the best chance for success.” - Charlie Aslan

Implementation Guide

Getting Started with AI-Enhanced Debugging

1. Essential Copilot Setup

Context Elements:

  • #solution for codebase-wide semantic search
  • #debugger for complete debugging state analysis
  • File attachments for additional code context
  • Visual context through screenshot integration

2. Smart Debugging Workflow

**Breakpoint Strategy:**
1. Use breakpoint groups for scenario organization
2. Leverage conditional breakpoints with AI suggestions
3. Implement tracepoints for logging without code changes
4. Utilize force run to cursor for efficient navigation

**Data Analysis:**
1. Hover over variables for AI-generated descriptions
2. Use IEnumerable visualizer for complex data inspection
3. Apply LINQ filtering with natural language descriptions
4. Analyze intermediate results in LINQ pipelines

3. Exception and Error Analysis

**Exception Workflow:**
1. Use "Analyze with Copilot" on exception dialogs
2. Provide additional context through file attachments
3. Allow variable evaluation for comprehensive analysis
4. Apply suggested fixes with rollback capabilities

**Logic Error Investigation:**
1. Use variable analysis for unexpected values
2. Inspect API parameters with documentation cross-reference
3. Leverage inline return values for function analysis

4. Multi-Threading Debugging

**Parallel Application Analysis:**
1. Use Parallel Stacks window with AI thread summaries
2. Apply #debugger context for detailed thread analysis
3. Investigate deadlocks with automatic detection
4. Implement suggested synchronization mechanisms

Best Practices for AI-Assisted Debugging

Context Management

  • Start specific rather than generic when describing problems
  • Add visual context for UI-related issues
  • Attach relevant files for better AI analysis
  • Maintain conversation threads for complex investigations

Iterative Problem Solving

  • Request minimal fixes to avoid overwhelming changes
  • Guide AI toward solutions through follow-up questions
  • Test incrementally rather than applying large changes
  • Use multiple models for different perspectives on complex problems

Performance Optimization

  • Combine profiling data with AI insights
  • Request specific optimization targets (memory, CPU, latency)
  • Benchmark before and after changes
  • Focus on bottlenecks identified through AI analysis

Advanced Applications

Educational Value

Learning Enhancement:

  • 24/7 expert assistance for new developers
  • Code comprehension through AI explanations
  • Debugging skill development with guided analysis
  • Best practices discovery through AI suggestions

Professional Development

Productivity Acceleration:

  • Faster bug resolution through intelligent analysis
  • Reduced debugging time with targeted investigation
  • Knowledge transfer from AI insights
  • Complex problem-solving assistance

Team Collaboration

Knowledge Sharing:

  • Consistent debugging approaches across team members
  • Documented analysis through conversation history
  • Shared insights from AI-generated explanations
  • Mentorship enhancement through AI-assisted guidance

About the Speakers

Cagri (Charlie) Aslan
Principal Software Engineering Manager
Microsoft
Engineering manager on Visual Studio Debugger team with focus on integrating large language models into debugging experience. Originally from Turkey, passionate about making developer lives easier through AI-enhanced tools.

Harshada Hole
Sr. Product Manager
Microsoft
Product Manager on Visual Studio Debugger and Profiler team focused on smoothing diagnostics experience. Former software engineer with 5+ years experience, committed to deep customer empathy and innovation.

Resources and Further Learning

Official Channels

  • Twitter: @VSDebugger - Tips, updates, and debugging insights
  • Blog: Visual Studio debugging and Copilot feature updates
  • YouTube: Comprehensive tutorials and feature demonstrations
  • Documentation: Complete Visual Studio debugging guide with AI features

Key Features to Explore

  • Exception Assistant with AI analysis
  • Variable Analysis for logic errors
  • Parallel Stacks with thread summaries
  • Profiling Tools with AI insights
  • LINQ Debugging with hover visualization

This session demonstrates that AI-enhanced debugging is not about replacing developer expertise, but amplifying it through intelligent analysis, contextual insights, and guided problem-solving that transforms debugging from reactive investigation to proactive, AI-assisted development.