Debug Assistance

Help diagnose and fix bugs in code.

Purpose

Systematic debugging support for:

• Error message interpretation
• Root cause analysis
• Reproducing issues
• Fixing bugs
• Preventing regressions

Required Input

• Error/symptom: What's happening
• Expected behavior: What should happen
• Code context: Relevant code or function
• Steps to reproduce: How to trigger the issue

Process

1. Understand the problem

   • Parse error messages
   • Identify symptom vs. root cause
   • Clarify expected behavior

2. Reproduce the issue

   • Create minimal reproducible example
   • Identify triggers and conditions
   • Document environment

3. Diagnose

   • Trace execution flow
   • Check inputs and outputs
   • Identify where behavior diverges

4. Fix

   • Propose solution
   • Explain the fix
   • Consider side effects

5. Verify

   • Test the fix
   • Check edge cases
   • Add regression test

Output Format

## Bug Analysis

### Problem Summary
[Clear description of the issue]

### Error Details

[Error message or unexpected output]

### Root Cause
[Explanation of why this is happening]

### Minimal Reproducible Example
```r
# Code that demonstrates the issue

Suggested Fix

# Fixed code with explanation

Explanation

[Why this fix works]

Prevention

• Add test case for this scenario
• Update documentation if behavior was unclear
• Consider related edge cases

## Debugging Strategies

### For R
```r
# Tracing
traceback()
debug(function_name)
browser()

# Inspection
str(object)
class(object)
attributes(object)

# Options
options(error = recover)
options(warn = 2)  # Treat warnings as errors

For JavaScript/TypeScript

// Console debugging
console.log(variable)
console.trace()
debugger;

// Error inspection
try { } catch (e) { console.error(e.stack) }

MCP Integration

Uses these tools when available:

• r_execute - Run code snippets
• r_inspect - Examine objects
• r_session - Interactive debugging

Common Bug Patterns

Type Errors

• Wrong data type passed
• NULL/undefined where object expected
• Vector vs. scalar confusion

Logic Errors

• Off-by-one errors
• Wrong comparison operator
• Missing edge case handling

Environment Issues

• Missing dependencies
• Version conflicts
• Path problems

Data Issues

• Unexpected NA/NULL values
• Encoding problems
• Format mismatches

Examples

Request: "Help me debug this error: 'object of type closure is not subsettable'"

Request: "My function returns NULL instead of the expected data frame"

Request: "This code works in RStudio but fails in a script"

Request: "Help me understand why my test is failing"

Tips

• Start with the simplest possible reproduction
• Check assumptions explicitly
• Use version control to bisect
• Read the full error message
• Check recent changes first