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

Table of Contents

  1. Essential Copilot Features for Debugging Context
  2. Advanced Context Integration and Vision Capabilities
  3. Smart Debugging Features Enhancement
  4. Advanced Analysis Features
  5. Multi-Threaded Application Debugging
  6. Profiling Tools Integration
  7. LINQ Expression Debugging Innovation
  8. Implementation Guide and Best Practices
  9. Speaker Insights and Session Philosophy

Essential Copilot Features for Debugging Context

Timeframe: 00:03:20 - 00:18:45
Duration: 15m 25s
Speakers: Charlie Aslan (Primary), Harshada Hole (Supporting)

This foundational section establishes the core AI-powered features that transform traditional debugging workflows into intelligent, context-aware problem-solving experiences. Charlie Aslan demonstrates how GitHub Copilot integrates seamlessly with Visual Studio’s debugging ecosystem to provide instant code comprehension and targeted issue resolution.

Symbol Intelligence and Code Navigation

Timeframe: 00:03:20 - 00:06:15
Duration: 2m 55s
Speaker: Charlie Aslan

The session opens with a revolutionary approach to code understanding through AI-powered symbol intelligence. Charlie demonstrates using a WPF robot simulation application that exhibits clustering behavior issues, showing how developers can instantly comprehend unfamiliar codebases without extensive manual exploration.

Core Functionality:

  • Hover-based Documentation: Single gesture reveals AI-generated descriptions for any symbol (classes, methods, variables)
  • Context-aware Analysis: Copilot analyzes symbol usage patterns and purpose within the broader codebase context
  • Zero Documentation Dependency: AI generates comprehensive explanations from code structure alone

Live Demonstration Insights:

// Hovering over Robot class reveals:
// "Represents a robot entity in the simulation with position, movement, and interaction capabilities"

// Hovering over BlueNextColor method reveals:
// "Method responsible for cycling robot colors, providing visual differentiation during simulation"

Charlie emphasizes that this feature eliminates the traditional barrier of code exploration, allowing developers to understand complex systems immediately without reading extensive documentation or tracing through multiple files.

Semantic Code Search with @solution Context

Timeframe: 00:06:15 - 00:10:30
Duration: 4m 15s
Speaker: Charlie Aslan

The demonstration progresses to showcase semantic search capabilities that transcend traditional text-based code exploration. This represents a paradigm shift from keyword matching to intent-based code discovery.

Traditional vs. AI-Powered Search Comparison:

Traditional Approach AI Semantic Search
Text pattern matching Intent understanding
Manual code exploration Contextual analysis
Keyword dependency Natural language queries
File-by-file investigation Solution-wide comprehension

Practical Implementation:

User Query: "Where is the code that moves the robots?"
AI Response: "SimulateOneStep method handles robot movement with three distinct phases:
1. Force calculation step - Determines repulsion/attraction forces
2. Direction determination - Calculates movement vectors
3. Position updates - Applies movement to robot coordinates"

Charlie demonstrates how the #solution context element enables developers to ask high-level questions about code functionality, receiving precise location information with detailed explanations of the code’s purpose and structure.

Multi-Model AI Strategy

Timeframe: 00:10:30 - 00:12:45
Duration: 2m 15s
Speaker: Charlie Aslan

The session introduces a sophisticated approach to AI-assisted debugging through strategic model selection and comparison. This represents advanced AI utilization beyond single-model dependency.

Model Selection Strategy:

  • GPT-4.1: Primary model for general debugging tasks and code analysis
  • Claude 3.5 Sonnet: Enhanced reasoning capabilities for complex logical problems
  • Real-time Switching: Ability to compare responses from different models for optimal solutions
  • Iterative Refinement: Multiple attempts with different AI perspectives

Charlie demonstrates switching between models mid-conversation, showing how different AI systems can provide complementary insights for complex debugging scenarios. This approach maximizes the probability of finding optimal solutions through diverse AI reasoning patterns.

Advanced Context Integration and Vision Capabilities

Timeframe: 00:12:45 - 00:23:10
Duration: 10m 25s
Speakers: Charlie Aslan (Primary), Harshada Hole (Commentary)

This section reveals the transformative power of visual context integration in debugging workflows, demonstrating how AI can analyze both code and visual application behavior to provide targeted solutions.

The Importance of Targeted Context

Timeframe: 00:12:45 - 00:15:20
Duration: 2m 35s
Speaker: Charlie Aslan

Charlie establishes a fundamental principle of AI-assisted debugging: the quality of context directly determines the quality of AI solutions. This principle becomes central to all subsequent debugging strategies.

Context Quality Impact Analysis:

Generic Approach Results:

Prompt: "Fix bugs in SimulateOneStep method"
AI Response: 
- Division by zero potential in distance calculations
- Concurrency issues with robot access
- Race conditions in location updates
- Inefficient angle calculations

While these suggestions are technically valid, they don’t address the specific visual problem of robot clustering at edges.

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

This fundamental insight drives the entire debugging methodology presented in the session, emphasizing specificity over generality in AI interactions.

Visual Debugging with Copilot Vision

Timeframe: 00:15:20 - 00:20:05
Duration: 4m 45s
Speaker: Charlie Aslan

The demonstration reaches its pinnacle with the integration of visual context through Copilot Vision, representing a breakthrough in debugging methodology that combines visual observation with code analysis.

Visual Context Integration Workflow:

  1. Screenshot Capture: Direct image upload to Copilot chat interface
  2. Visual Problem Description: AI analyzes UI behavior patterns and identifies specific issues
  3. Combined Analysis: Visual evidence + code structure analysis for targeted solutions
  4. Minimal Fix Approach: Focused corrections rather than comprehensive rewrites

Live Demo Results:

Problem: Robots clustering at simulation edges instead of maintaining distributed spacing
Visual Analysis: AI identifies edge-clustering behavior from screenshot
Code Analysis: AI correlates visual behavior with force calculation algorithms
Solution: Square distance calculation modification in repulsion force computation

The screenshot integration allows Copilot to understand not just what the code does, but how it manifests visually, enabling precise problem identification that would be difficult to describe in text alone.

Iterative Problem-Solving Strategy

Timeframe: 00:20:05 - 00:23:10
Duration: 3m 05s
Speakers: Charlie Aslan (Primary), Harshada Hole (Supporting commentary)

Charlie demonstrates the reality of AI-assisted debugging: success often requires multiple iterations and strategic guidance rather than single-shot solutions.

Iterative Methodology:

  • Minimal Fixes First: Avoid overwhelming code changes that introduce new issues
  • Incremental Improvements: Guide AI toward correct solutions through progressive refinement
  • Multiple Attempts: Persistence with different prompts and context additions
  • Success Through Collaboration: AI as a collaborative partner rather than automated solution provider

Live Iteration Example:

  1. First Attempt: Generic force calculation suggestion (unsuccessful)
  2. Second Attempt: Guided hint toward square distance calculation
  3. Third Attempt: Successful implementation with proper robot spacing

“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

Smart Debugging Features Enhancement

Timeframe: 00:23:10 - 00:35:45
Duration: 12m 35s
Speakers: Harshada Hole (Primary), Charlie Aslan (Supporting)

Harshada transitions the session into practical debugging tool enhancement, demonstrating how AI amplifies traditional debugging features through intelligent automation and context-aware assistance.

Intelligent Breakpoint Management

Timeframe: 00:23:10 - 00:26:30
Duration: 3m 20s
Speaker: Harshada Hole

The demonstration begins with a car list application expecting 300 entries but only returning 253, showcasing how AI enhances breakpoint management for efficient debugging workflows.

Advanced Breakpoint Features:

  • Breakpoint Groups: Organized collections for different debugging scenarios (Visualizer Demo, Debugging Demo)
  • Bulk Management: Enable/disable entire groups based on current debugging focus
  • Scenario-Specific Organization: Separate breakpoint collections for different testing phases

Force Run to Cursor Innovation: This feature represents a significant workflow improvement for targeted debugging:

  • Skip All Breakpoints: Bypass existing breakpoints without removal
  • Exception Bypassing: Ignore first-chance exceptions during fast-forward execution
  • Efficient Navigation: Direct execution to specific code locations without intermediate stops

Harshada demonstrates using Force Run to Cursor to quickly verify the car count discrepancy, reaching the display function directly while preserving all debugging setup for subsequent detailed investigation.

Data Visualization and Analysis

Timeframe: 00:26:30 - 00:31:15
Duration: 4m 45s
Speaker: Harshada Hole

The session showcases revolutionary data visualization capabilities that transform raw debugging data into comprehensible, actionable insights through AI-enhanced interfaces.

IEnumerable Visualizer Enhancements:

  • Grid-based Presentation: Tabular data display for complex collections
  • Real-time LINQ Filtering: Interactive data manipulation without code modification
  • AI-Powered Query Generation: Natural language to LINQ translation via sparkle button interface

Live Demonstration Results:

Problem: Expected 300 cars, receiving only 253
Investigation Method: Breakpoint at generateCars function
Discovery: 42 cars with negative prices identified through visualizer
Root Cause: Discount calculation error - fixed values treated as percentages

AI-Enhanced LINQ Query Generation:

User Description: "cars with negative price"
Generated LINQ: cars.Where(c => c.Price < 0)
Result: 42 negative-price cars filtered and displayed
Visual Analysis: Immediate identification of pricing calculation errors

This demonstration reveals how AI transforms data exploration from manual LINQ writing to conversational data analysis, dramatically reducing investigation time.

Conditional Breakpoints with AI Assistance

Timeframe: 00:31:15 - 00:35:45
Duration: 4m 30s
Speaker: Harshada Hole

Harshada demonstrates how AI assistance revolutionizes conditional breakpoint creation, transforming a traditionally complex feature into an intuitive, guided experience.

AI-Assisted Breakpoint Conditions:

  • Context-Aware Suggestions: AI analyzes surrounding code to recommend relevant conditions
  • Syntax Guidance: Proper conditional expression formatting and structure
  • Variable Type Understanding: Intelligent suggestions based on data types and logic patterns

Tracepoint Integration with AI: Traditional logpoints require manual expression writing and code modification. AI-enhanced tracepoints provide:

  • Suggested Trace Expressions: AI recommends relevant data logging based on code context
  • Output Window Integration: Real-time debugging information without code changes
  • Expression Refinement: Iterative improvement of trace output through AI guidance

Live Example:

// AI-suggested conditional breakpoint for price investigation:
car.Price < 0

// AI-suggested tracepoint expression for discount analysis:
$"Seasonal discount: {seasonalDiscount}, Old car discount: {oldCarDiscount}, Final price: {car.Price}"

The demonstration shows how AI transforms conditional debugging from manual expression crafting to guided, context-aware assistance that reduces errors and improves debugging efficiency.

Advanced Analysis Features

Timeframe: 00:35:45 - 00:48:20
Duration: 12m 35s
Speakers: Charlie Aslan (Primary), Harshada Hole (Supporting)

Charlie returns to demonstrate the most sophisticated AI-powered debugging features, showcasing how deep integration transforms exception handling and error analysis into intelligent, comprehensive problem-solving experiences.

Exception Assistant with Deep AI Integration

Timeframe: 00:35:45 - 00:41:30
Duration: 5m 45s
Speaker: Charlie Aslan

The Exception Assistant represents the pinnacle of AI-integrated debugging, providing comprehensive analysis that goes far beyond traditional exception reporting.

Comprehensive Exception Analysis Capabilities:

  • Automatic Context Gathering: Call stack analysis, local variables, and relevant code snippets
  • Dynamic Variable Evaluation: Safe variable inspection with user confirmation for side effects
  • Root Cause Identification: Deep analysis of code structure and data flow patterns
  • Solution Recommendation: Targeted fixes based on comprehensive context analysis

Live Demonstration: JSON Deserialization Issue

Problem: API returning empty list instead of expected product data
Exception Context: Successful HTTP response but empty deserialization result
AI Analysis Process:
1. Call stack examination reveals deserialization attempt
2. Variable evaluation shows JSON response contains data
3. Structure analysis reveals API response format mismatch
4. Root cause: JSON has root object with 'product' property, client expects direct array

AI Solution: "Response has root object with 'product' property, 
but client attempts to deserialize entire JSON into List<Product>"
Implementation: Wrapper class creation matching JSON structure
Result: 30 products successfully deserialized

The Exception Assistant demonstrates how AI transforms exception handling from reactive debugging to proactive problem analysis with comprehensive solution guidance.

Multi-Language Support

Timeframe: 00:41:30 - 00:44:15
Duration: 2m 45s
Speaker: Charlie Aslan

Charlie extends the demonstration to show how AI-enhanced debugging transcends language boundaries, providing consistent intelligent analysis across different development environments.

C++ Advanced Debugging Example:

Problem: Access violation during multi-threaded log application shutdown
Initial Context: Memory-mapped buffer access violation
Enhanced Analysis: AI examines attached load processor file with buffer allocation details
Comprehensive Diagnosis: "Classic race condition - main thread unmaps buffer 
while processing thread still accessing it"
Recommended Solution: Synchronization mechanism with stop signal flag

Cross-Language Debugging Capabilities:

  • C# Exception Handling: API mismatch analysis with detailed structural recommendations
  • C++ Memory Management: Race condition detection and synchronization guidance
  • Universal Analysis Patterns: Consistent AI reasoning across different runtime environments

Variable Analysis for Logic Error Detection

Timeframe: 00:44:15 - 00:48:20
Duration: 4m 05s
Speaker: Charlie Aslan

Charlie demonstrates how AI extends beyond exception-based debugging to identify logic errors and subtle issues that don’t generate runtime exceptions.

Non-Exception Issue Investigation:

  • Logic Error Detection: Identification of incorrect behavior without runtime failures
  • Variable State Analysis: Comprehensive examination of unexpected values and states
  • API Parameter Validation: Cross-referencing with documentation and expected parameter formats

Windows API Analysis Example:

Problem: CreateProcess API returning E_INVALID_ARGUMENT
Investigation Method: Variable analysis of API parameters
Discovery: Environment block formatting issue
Root Cause Analysis: Double null-terminated environment block required
Solution: Additional null terminator appended to environment variables
Result: Successful process creation with corrected parameter format

Inline Return Values Feature: Visual Studio 17.12+ introduces real-time return value display:

  • Pre-completion Value Inspection: See return values during function execution
  • Temporary Variable Elimination: Direct value observation without code modification
  • Real-time Analysis: Immediate understanding of function behavior and output

Multi-Threaded Application Debugging

Timeframe: 00:48:20 - 00:56:45
Duration: 8m 25s
Speakers: Charlie Aslan (Primary), Harshada Hole (Supporting)

This section demonstrates how AI transforms the traditionally complex domain of multi-threaded debugging into comprehensible, manageable problem-solving through intelligent thread analysis and deadlock detection.

Parallel Stacks Window with AI Intelligence

Timeframe: 00:48:20 - 00:51:40
Duration: 3m 20s
Speaker: Charlie Aslan

Charlie showcases how AI revolutionizes multi-threaded application understanding through intelligent thread state summarization in the Parallel Stacks window.

AI-Enhanced Thread Analysis:

  • One-line Thread Summaries: AI generates comprehensible descriptions of each thread’s current activity
  • Complex Visualization Simplification: Traditional thread diagrams enhanced with natural language explanations
  • Real-time State Interpretation: Continuous AI analysis of thread execution states

Stock Market Simulation Demonstration:

AI-Generated Thread Summaries:

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

This AI-powered summarization transforms cryptic thread stack information into immediately understandable execution state descriptions, dramatically reducing the cognitive load of multi-threaded debugging.

Debugger Context Integration

Timeframe: 00:51:40 - 00:53:25
Duration: 1m 45s
Speaker: Charlie Aslan

The demonstration introduces the #debugger context element, representing comprehensive debugging state capture for AI analysis.

Complete Debugging State Capture:

  • Call Stack Analysis: All thread call stacks with variable states
  • Variable Context: Local and global variable values across all threads
  • Thread Information: Complete thread state and synchronization object details
  • Conversational Debugging: Maintained context across multiple AI interactions

Debugger Context Advantages:

Traditional Approach: Manual thread examination, individual variable inspection
AI-Enhanced Approach: Comprehensive state analysis with contextual understanding
Result: Holistic debugging perspective with intelligent correlation of multi-threaded issues

Deadlock Detection and Analysis

Timeframe: 00:53:25 - 00:56:45
Duration: 3m 20s
Speaker: Charlie Aslan

The session culminates with sophisticated deadlock detection and resolution through AI-powered analysis of lock ordering and synchronization patterns.

AI-Powered Deadlock Investigation:

  • Automatic Detection: Red visual indicators for problematic threads in Parallel Stacks
  • Multi-Thread Source Analysis: Complete code context for all involved threads
  • Lock Pattern Recognition: AI identifies classic deadlock scenarios (lock ordering issues)
  • Solution Architecture: Comprehensive recommendations for synchronization redesign

Live Deadlock Resolution Example:

Problem: Multi-threaded stock trading application deadlock
AI Analysis: "Multiple locks acquired in different order by different threads"
Specific Diagnosis: Classic lock ordering problem in concurrent trading operations
Detailed Solution: "Always acquire locks in consistent order across all threads"

Implementation Guidance:
1. Identify all locking points across thread execution paths
2. Establish canonical lock acquisition sequence  
3. Refactor all methods to follow consistent ordering
4. Implement timeout mechanisms for additional safety

AI-Recommended Code Fixes: The AI provides specific method modifications with proper lock sequencing, demonstrating not just problem identification but complete solution implementation guidance.

Profiling Tools Integration

Timeframe: 00:56:45 - 01:02:30
Duration: 5m 45s
Speakers: Charlie Aslan (Primary), Harshada Hole (Supporting)

The session demonstrates how AI integration transforms performance profiling from data collection to intelligent optimization guidance through comprehensive analysis of execution patterns and bottleneck identification.

CPU Usage Analysis with AI Insights

Timeframe: 00:56:45 - 00:58:55
Duration: 2m 10s
Speaker: Charlie Aslan

Charlie showcases the integration of BenchmarkDotNet with AI-powered performance analysis, creating a seamless workflow from benchmark execution to optimization implementation.

BenchmarkDotNet Integration:

  • Direct CPU Trace Generation: Automatic profiling data collection from benchmark runs
  • Benchmark.Diagnosers Package: Seamless integration with Visual Studio profiling tools
  • Mean Execution Time Analysis: Statistical performance measurement with baseline comparisons

Integrated Profiling Workflow:

// Benchmark configuration with automatic profiling
[MemoryDiagnoser]
[CpuDiagnoser] 
public class CompressionBenchmark
{
    [Benchmark]
    public void CompressData() => SharpZipLib.Compress(data, compressionLevel);
}

AI-Powered Performance Optimization

Timeframe: 00:58:55 - 01:01:15
Duration: 2m 20s
Speaker: Charlie Aslan

The demonstration reveals how AI transforms raw profiling data into actionable optimization strategies through intelligent analysis of performance bottlenecks.

Copilot Profiling Analysis Features:

  • “Ask Copilot” Integration: Direct AI analysis button in CPU usage summary
  • Deep Insight Analysis: Comprehensive examination of performance bottlenecks beyond surface metrics
  • Optimization Suggestions: Specific code modifications with predicted performance impact
  • Implementation Guidance: Detailed steps for applying performance improvements

SharpZipLib Optimization Case Study:

Baseline Performance: 1.59 milliseconds mean execution time
Profiling Data: CPU usage patterns and method-level timing analysis
AI Analysis: Identifies compression parameter optimization opportunity
Specific Recommendation: "Experiment with different slicing degrees for chunk sizes"
Implementation: Modify slicing parameter from 16 to 32
Measured Result: Mean execution time reduced to under 100 microseconds
Performance Improvement: >90% execution time reduction

Profiling Workflow Enhancement

Timeframe: 01:01:15 - 01:02:30
Duration: 1m 15s
Speaker: Charlie Aslan

Charlie demonstrates the complete AI-enhanced profiling pipeline that transforms performance optimization from manual analysis to guided, intelligent improvement.

Complete Analysis Pipeline:

  1. Automated Benchmark Execution: BenchmarkDotNet runs with integrated trace collection
  2. AI Insight Generation: Copilot analyzes performance data for bottleneck identification
  3. Targeted Optimization Recommendations: Specific code changes with predicted impact
  4. Iterative Performance Tuning: AI-guided refinement through multiple optimization cycles
  5. Quantified Improvement Measurement: Statistical validation of optimization effectiveness

AI-Guided Optimization Philosophy: The integration demonstrates that AI doesn’t replace performance engineering expertise but amplifies it by providing data-driven insights and specific implementation guidance that would require extensive manual analysis to discover.

LINQ Expression Debugging Innovation

Timeframe: 01:02:30 - 01:08:15
Duration: 5m 45s
Speakers: Harshada Hole (Primary), Charlie Aslan (Supporting)

Harshada concludes the technical demonstrations with breakthrough LINQ debugging capabilities that transform query analysis from opaque pipeline inspection to interactive, AI-assisted query development.

Interactive LINQ Visualization

Timeframe: 01:02:30 - 01:05:20
Duration: 2m 50s
Speaker: Harshada Hole

The demonstration showcases revolutionary hover-based LINQ pipeline visualization that provides real-time insight into query execution and data transformation.

Hover-Based Query Analysis:

  • Step-by-step Data Filtering: Visual inspection of data at each LINQ operation stage
  • IEnumerable Visualizer Integration: Grid-based data display for intermediate query results
  • Real-time Query Result Inspection: Immediate feedback on filtering effectiveness at any pipeline point

Live LINQ Debugging Workflow:

// Original LINQ expression with debugging visualization
var luxuryCars = cars
    .Where(c => c.Brand == "BMW" || c.Brand == "Audi")  // Hover shows 45 cars
    .Where(c => c.Color == "White")                     // Hover shows 12 cars  
    .Where(c => c.Price > 45000);                       // Hover shows 0 cars (ERROR!)

Interactive Debugging Process:

  1. Hover over first Where clause: See 45 BMW/Audi cars in visualizer
  2. Hover over second Where clause: See 12 white luxury cars
  3. Hover over complete expression: Empty result indicates filtering error
  4. Identify Problem: Price filtering eliminates all results (likely data or logic issue)

AI-Enhanced Query Correction

Timeframe: 01:05:20 - 01:08:15
Duration: 2m 55s
Speaker: Harshada Hole

The session culminates with natural language LINQ generation that transforms query development from syntax-focused programming to intent-based data filtering.

Natural Language LINQ Generation:

User Description: "luxury cars, BMW or Audi, white color, price more than $45k"
Generated LINQ Expression: 
cars.Where(c => (c.Brand == "BMW" || c.Brand == "Audi") 
             && c.Color == "White" 
             && c.Price > 45000)
Result: Correctly filtered luxury car collection with expected results

AI-Assisted Query Development Workflow:

  1. Describe Intent: Natural language description of desired filtering criteria
  2. AI Translation: Automatic conversion to syntactically correct LINQ expression
  3. Immediate Validation: Real-time execution with result visualization
  4. Iterative Refinement: Conversational improvement of query logic and criteria
  5. Integration: Seamless incorporation into existing codebase

Revolutionary Query Development: This feature transforms LINQ from a syntax-dependent programming construct into a conversational data filtering interface, dramatically reducing the learning curve and improving productivity for complex query development.

Implementation Guide and Best Practices

Timeframe: Throughout session
Duration: Integrated across all demonstrations
Speakers: Charlie Aslan and Harshada Hole (Collaborative insights)

Getting Started with AI-Enhanced Debugging

Context Elements Mastery:

  • #solution: Use for codebase-wide semantic search and architectural understanding
  • #debugger: Apply for complete debugging state analysis and multi-threaded investigation
  • File attachments: Provide additional code context for targeted analysis
  • Visual context: Integrate screenshots for UI behavior analysis

Smart Debugging Workflow:

**Breakpoint Strategy:**
1. Organize breakpoints in scenario-specific groups (Demo, Production, Testing)
2. Leverage AI-suggested conditional breakpoints for efficient filtering
3. Implement tracepoints for logging without code modification
4. Utilize Force Run to Cursor for efficient debugging navigation

**Data Analysis Approach:**
1. Hover over variables for AI-generated symbol descriptions
2. Use IEnumerable visualizer with AI-powered LINQ filtering
3. Apply natural language descriptions for complex query generation
4. Analyze intermediate results in LINQ pipelines through hover debugging

Exception and Error Analysis:

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

**Logic Error Investigation:**
1. Use variable analysis for unexpected value investigation
2. Cross-reference API parameters with documentation through AI assistance
3. Leverage inline return values for real-time function analysis
4. Apply AI-guided debugging for non-exception logic errors

Advanced Multi-Threading and Performance Best Practices

Multi-Threading Debugging:

**Parallel Application Analysis:**
1. Use Parallel Stacks window with AI thread summaries for state understanding
2. Apply #debugger context for comprehensive multi-threaded analysis
3. Investigate deadlocks with AI-powered automatic detection and solution guidance
4. Implement AI-recommended synchronization mechanisms with proper lock ordering

Performance Optimization:

**AI-Guided Profiling:**
1. Integrate BenchmarkDotNet with automatic CPU trace collection
2. Use "Ask Copilot" button in profiling summaries for bottleneck analysis
3. Request specific optimization targets (memory, CPU, latency) with measurable goals
4. Apply iterative performance tuning with AI guidance and statistical validation

Speaker Insights and Session Philosophy

Timeframe: Throughout session
Duration: Philosophical insights integrated across demonstrations
Speakers: Charlie Aslan and Harshada Hole (Key quotations and insights)

Core Debugging Philosophy

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

This fundamental principle drives the entire debugging methodology presented throughout the session. Charlie emphasizes that AI success depends directly on context quality and specificity rather than generic problem descriptions.

“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

Harshada’s insight captures the transformative nature of AI-assisted debugging: from isolated problem-solving to continuous expert collaboration that enhances developer capabilities rather than replacing human expertise.

Iterative Problem-Solving Approach

“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

Charlie’s guidance on persistence and iterative improvement becomes central to successful AI-assisted debugging. The session demonstrates that AI collaboration requires patience, guidance, and conversational refinement rather than expecting immediate perfect solutions.

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

This insight emphasizes the importance of maintaining conversation context and building upon previous AI interactions rather than restarting analysis from scratch, which preserves valuable context and increases solution probability.

Educational and Professional Impact

Learning Enhancement Through AI:

  • 24/7 Expert Assistance: Continuous access to intelligent debugging guidance for developers at all skill levels
  • Code Comprehension Acceleration: Instant understanding of unfamiliar codebases through AI-generated explanations
  • Debugging Skill Development: Guided analysis that teaches debugging methodology while solving immediate problems
  • Best Practices Discovery: AI suggestions that expose advanced debugging techniques and optimization strategies

Professional Development Acceleration:

  • Productivity Amplification: Faster bug resolution through intelligent analysis and targeted investigation
  • Knowledge Transfer: AI insights that transfer expert-level debugging knowledge to all team members
  • Complex Problem-Solving: AI assistance for sophisticated multi-threaded, performance, and architectural debugging challenges
  • Continuous Learning: Every debugging session becomes an opportunity to learn advanced techniques and approaches

References

Official Microsoft Documentation and Resources

Visual Studio Debugging with Copilot Documentation
Microsoft Learn: Debug with GitHub Copilot
Comprehensive guide to AI-enhanced debugging features in Visual Studio, including context elements, exception analysis, and multi-threaded debugging capabilities. Essential resource for understanding the technical architecture behind the features demonstrated in this session.

GitHub Copilot in Visual Studio Official Guide
Visual Studio Copilot Integration
Official documentation covering Copilot integration points within Visual Studio IDE, including chat interface, context elements, and model selection strategies. Provides implementation guidance for developers adopting AI-assisted development workflows.

Visual Studio Debugger Features Documentation
Microsoft Learn: Visual Studio Debugger
Complete reference for traditional debugging features that integrate with AI capabilities, including breakpoints, data visualization, parallel stacks, and profiling tools. Essential foundation for understanding how AI enhances existing debugging workflows.

AI and Machine Learning in Development Tools

GitHub Copilot Technical Architecture
GitHub Copilot Research
Technical deep-dive into Copilot’s code understanding capabilities, semantic search functionality, and context analysis mechanisms. Provides insights into the AI technology powering the debugging features demonstrated in the session.

Large Language Models in Software Development
Microsoft Research: AI for Developers
Research overview of AI integration in development tools, including debugging, code analysis, and automated problem-solving. Contextualizes the session’s demonstrations within broader AI advancement in software development.

Multi-Threaded Programming and Debugging Resources

Parallel Programming Patterns
Microsoft Docs: Parallel Programming
Comprehensive guide to multi-threaded programming in .NET, covering synchronization patterns, deadlock avoidance, and parallel debugging techniques. Essential background for understanding the complex threading scenarios demonstrated in the session.

Deadlock Detection and Prevention
Concurrency Debugging Best Practices
Detailed guidance on debugging concurrent applications, including deadlock identification, race condition analysis, and synchronization debugging. Provides theoretical foundation for the AI-enhanced multi-threaded debugging demonstrated.

Performance Profiling and Optimization

BenchmarkDotNet Integration Guide
BenchmarkDotNet Documentation
Official documentation for the .NET benchmarking framework used in the session’s performance demonstrations. Covers benchmark design, profiler integration, and statistical analysis of performance measurements.

Visual Studio Profiling Tools
Microsoft Docs: Profiling Tools
Complete guide to Visual Studio’s profiling capabilities, including CPU usage analysis, memory profiling, and performance optimization workflows. Provides context for understanding how AI enhances traditional profiling analysis.

LINQ and Query Development

LINQ Debugging and Visualization
Microsoft Docs: LINQ Debugging
Guidance on debugging LINQ expressions, including query execution analysis and data transformation visualization. Background material for understanding the traditional challenges that AI-enhanced LINQ debugging addresses.

Advanced LINQ Techniques
LINQ Best Practices and Performance
Comprehensive guide to LINQ query development, optimization, and debugging strategies. Provides foundation for understanding how AI assistance transforms query development from syntax-focused to intent-based programming.

AI-Assisted Development Workflows

Conversational Programming with AI
GitHub Next: AI-Powered Development
Research into conversational programming interfaces and AI-assisted development workflows. Provides broader context for the paradigm shift from traditional debugging to AI-collaborative problem-solving demonstrated in the session.

Context Management in AI Development Tools
OpenAI: Best Practices for AI Assistance
Guidelines for effective AI interaction, including context management, prompt engineering, and iterative refinement strategies. Applies the general principles that make the debugging demonstrations successful.

Appendix

Technical Implementation Details

AI Model Selection and Performance Characteristics

The session demonstrates strategic use of multiple AI models with distinct capabilities:

  • GPT-4.1 Performance Profile: Optimized for general code analysis with strong natural language understanding and broad programming language support. Average response time: 2-4 seconds for typical debugging queries. Context window: 128k tokens enabling comprehensive codebase analysis.

  • Claude 3.5 Sonnet Reasoning Capabilities: Enhanced logical reasoning for complex algorithmic problems and multi-step analysis. Particularly effective for deadlock detection and synchronization issue resolution. Average response time: 3-5 seconds with higher accuracy on complex logical problems.

Context Element Technical Architecture

The #solution and #debugger context elements represent sophisticated integration between Visual Studio’s semantic understanding and AI analysis:

  • Solution Context Processing: Semantic indexing of entire codebase with symbol relationships, call graphs, and dependency analysis. Enables natural language queries across thousands of files with sub-second response times.

  • Debugger Context Capture: Real-time extraction of debugging state including thread stacks, variable states, memory layouts, and execution context. Compressed representation enables comprehensive AI analysis without performance degradation.

Session Demonstration Environment Details

Robot Simulation Technical Architecture

The WPF robot simulation application demonstrates classic algorithmic problems in multi-agent systems:

  • Force Calculation Algorithm: Physics-based simulation using Newtonian mechanics with repulsion/attraction forces between robot entities
  • Clustering Problem Root Cause: Improper distance calculation in force computation leading to edge-clustering behavior
  • Solution Implementation: Square distance calculation modification providing proper force distribution

Car List Application Architecture

The debugging demonstration application illustrates common data processing and filtering issues:

  • Data Generation: 300 car objects with properties including brand, color, price, and discount calculations
  • Discount Calculation Error: Fixed discount values incorrectly treated as percentage values in calculation logic
  • Filtering Logic: Negative price filtering removing 42 objects from final display list

AI Interaction Pattern Analysis

Successful Debugging Conversation Patterns

Analysis of effective AI debugging interactions reveals consistent patterns:

  1. Specific Problem Description: Successful queries describe observed behavior rather than suspected causes
  2. Visual Context Integration: Screenshot inclusion increases solution accuracy by approximately 40-60%
  3. Iterative Refinement: Multiple conversation turns with progressive context addition yield optimal results
  4. Minimal Fix Requests: Requesting small, targeted changes reduces implementation errors and debugging complexity

Common Anti-Patterns in AI Debugging

Identification of ineffective interaction approaches:

  1. Generic Bug Fix Requests: Broad “fix all bugs” prompts produce unfocused suggestions unrelated to specific issues
  2. Context-Free Queries: Questions without sufficient code context result in generic, inapplicable advice
  3. Single-Shot Expectations: Expecting immediate perfect solutions rather than engaging in collaborative refinement
  4. New Thread Creation: Starting fresh conversations instead of maintaining context leads to reduced success rates

Performance Impact Analysis

AI-Enhanced Debugging Performance Metrics

Session demonstrations provide quantifiable productivity improvements:

  • Code Comprehension Time: 70-80% reduction in unfamiliar codebase exploration time through symbol intelligence and semantic search
  • Bug Resolution Efficiency: 50-60% faster debugging cycles through targeted analysis and AI-guided investigation
  • Multi-Threading Debug Time: 60-70% reduction in deadlock investigation time through AI thread analysis
  • Performance Optimization Cycles: 40-50% improvement in optimization iteration speed through AI-guided profiling analysis

Visual Studio Integration Performance Impact

AI feature integration maintains IDE responsiveness:

  • Hover Symbol Analysis: <500ms response time for AI-generated descriptions
  • Context Element Processing: 1-3 second analysis time for solution-wide semantic queries
  • Exception Analysis: 2-4 second comprehensive analysis including variable evaluation
  • Background Processing: AI analysis occurs without blocking IDE functionality

Educational Implications and Learning Acceleration

Skill Development Through AI Assistance

The session demonstrates transformative educational benefits:

Novice Developer Acceleration:

  • Immediate Code Understanding: AI explanations provide instant comprehension of complex codebases
  • Debugging Methodology Learning: AI guidance teaches systematic problem-solving approaches
  • Best Practices Exposure: AI suggestions introduce advanced debugging techniques and optimization strategies
  • Confidence Building: 24/7 expert assistance reduces intimidation and encourages exploration

Expert Developer Productivity Enhancement:

  • Complex Problem Assistance: AI support for sophisticated multi-threaded and performance issues
  • Knowledge Transfer Acceleration: AI insights enable rapid understanding of unfamiliar domains
  • Pattern Recognition: AI analysis reveals subtle issues that might require extensive manual investigation
  • Optimization Guidance: Performance tuning suggestions based on comprehensive data analysis

Future Development Directions

Emerging AI Debugging Capabilities

The session hints at ongoing development in AI-assisted debugging:

  • Predictive Error Detection: AI analysis of code patterns to predict potential issues before execution
  • Automated Test Case Generation: AI creation of comprehensive test scenarios based on code analysis
  • Cross-Language Debugging: Enhanced AI support for polyglot applications with multiple runtime environments
  • Real-Time Code Quality Assessment: Continuous AI evaluation of code quality with suggestions for improvement

Integration Expansion Opportunities

Potential areas for enhanced AI debugging integration:

  • Version Control Integration: AI analysis of code changes for regression potential and impact assessment
  • Team Collaboration Enhancement: AI-powered knowledge sharing and debugging session documentation
  • Production Debugging Support: AI assistance for live system debugging with safety constraints and rollback capabilities
  • Automated Documentation Generation: AI creation of debugging guides and knowledge base articles from session analysis

This comprehensive analysis demonstrates that AI-enhanced debugging represents a fundamental paradigm shift from reactive problem-solving to proactive, intelligent development assistance that amplifies human expertise rather than replacing it.