Debugging

When to use this skill

• Encountering runtime errors or exceptions
• Code produces unexpected output or behavior
• Performance degradation or memory issues
• Intermittent or hard-to-reproduce bugs
• Understanding unfamiliar error messages
• Post-incident analysis and prevention

Instructions

Step 1: Gather Information

Collect all relevant context about the issue:

Error details:

• Full error message and stack trace
• Error type (syntax, runtime, logic, etc.)
• When did it start occurring?
• Is it reproducible?

Environment:

• Language and version
• Framework and dependencies
• OS and runtime environment
• Recent changes to code or config

# Check recent changes
git log --oneline -10
git diff HEAD~5

# Check dependency versions
npm list --depth=0  # Node.js
pip freeze          # Python

Step 2: Reproduce the Issue

Create a minimal, reproducible example:

# Bad: Vague description
"The function sometimes fails"

# Good: Specific reproduction steps
"""
1. Call process_data() with input: {"id": None}
2. Error occurs: TypeError at line 45
3. Expected: Return empty dict
4. Actual: Raises exception
"""

# Minimal reproduction
def test_reproduce_bug():
    result = process_data({"id": None})  # Fails here
    assert result == {}

Step 3: Isolate the Problem

Use binary search debugging to narrow down the issue:

Print/Log debugging:

def problematic_function(data):
    print(f"[DEBUG] Input: {data}")  # Entry point

    result = step_one(data)
    print(f"[DEBUG] After step_one: {result}")

    result = step_two(result)
    print(f"[DEBUG] After step_two: {result}")  # Issue here?

    return step_three(result)

Divide and conquer:

# Comment out half the code
# If error persists: bug is in remaining half
# If error gone: bug is in commented half
# Repeat until isolated

Step 4: Analyze Root Cause

Common bug patterns and solutions:

Questions to ask:

1. What changed recently?
2. Does it fail with specific inputs?
3. Is it environment-specific?
4. Are there any patterns in failures?

Step 5: Implement Fix

Apply the fix with proper verification:

# Before: Bug
def get_user(user_id):
    return users[user_id]  # KeyError if not found

# After: Fix with proper handling
def get_user(user_id):
    if user_id not in users:
        return None  # Or raise custom exception
    return users[user_id]

Fix checklist:

• Addresses root cause, not just symptom
• Doesn’t break existing functionality
• Handles edge cases
• Includes appropriate error handling
• Has test coverage

Step 6: Verify and Prevent

Ensure the fix works and prevent regression:

# Add test for the specific bug
def test_bug_fix_issue_123():
    """Regression test for issue #123: KeyError on missing user"""
    result = get_user("nonexistent_id")
    assert result is None  # Should not raise

# Add edge case tests
@pytest.mark.parametrize("input,expected", [
    (None, None),
    ("", None),
    ("valid_id", {"name": "User"}),
])
def test_get_user_edge_cases(input, expected):
    assert get_user(input) == expected

Examples

Example 1: TypeError debugging

Error:

TypeError: cannot unpack non-iterable NoneType object
  File "app.py", line 25, in process
    name, email = get_user_info(user_id)

Analysis:

# Problem: get_user_info returns None when user not found
def get_user_info(user_id):
    user = db.find_user(user_id)
    if user:
        return user.name, user.email
    # Missing: return None case!

# Fix: Handle None case
def get_user_info(user_id):
    user = db.find_user(user_id)
    if user:
        return user.name, user.email
    return None, None  # Or raise UserNotFoundError

Example 2: Race condition debugging

Symptom: Test passes locally, fails in CI intermittently

Analysis:

# Problem: Shared state without synchronization
class Counter:
    def __init__(self):
        self.value = 0

    def increment(self):
        self.value += 1  # Not atomic!

# Fix: Add thread safety
import threading

class Counter:
    def __init__(self):
        self.value = 0
        self._lock = threading.Lock()

    def increment(self):
        with self._lock:
            self.value += 1

Example 3: Memory leak debugging

Tool: Use memory profiler

from memory_profiler import profile

@profile
def process_large_data():
    results = []
    for item in large_dataset:
        results.append(transform(item))  # Memory grows
    return results

# Fix: Use generator for large datasets
def process_large_data():
    for item in large_dataset:
        yield transform(item)  # Memory efficient

Best practices

1. Reproduce first: Never fix what you can’t reproduce
2. One change at a time: Isolate variables when debugging
3. Read the error: Error messages usually point to the issue
4. Check assumptions: Verify what you think is true
5. Use version control: Easy to revert and compare changes
6. Document findings: Help future debugging efforts
7. Write tests: Prevent regression of fixed bugs

Debugging Tools

References

• Debugging: The 9 Indispensable Rules
• How to Debug
• Rubber Duck Debugging