debugging

What this skill does

# 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

```bash
# 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:

```python
# 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**:
```python
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**:
```python
# 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:

| Pattern | Symptom | Solution |
|---------|---------|----------|
| Off-by-one | Index out of bounds | Check loop bounds |
| Null reference | NullPointerException | Add null checks |
| Race condition | Intermittent failures | Add synchronization |
| Memory leak | Gradual slowdown | Check resource cleanup |
| Type mismatch | Unexpected behavior | Validate types |

**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:

```python
# 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:

```python
# 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**:
```python
# 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**:
```python
# 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
```python
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

| Language | Debugger | Profiler |
|----------|----------|----------|
| Python | pdb, ipdb | cProfile, memory_profiler |
| JavaScript | Chrome DevTools | Performance tab |
| Java | IntelliJ Debugger | JProfiler, VisualVM |
| Go | Delve | pprof |
| Rust | rust-gdb | cargo-flamegraph |

## References

- [Debugging: The 9 Indispensable Rules](https://debuggingrules.com/)
- [How to Debug](https://blog.regehr.org/archives/199)
- [Rubber Duck Debugging](https://rubberduckdebugging.com/)