error-diagnosis

Error Diagnosis

Systematic approach to debugging errors and identifying root causes.

Diagnosis Workflow

1. Reproduce → 2. Isolate → 3. Identify → 4. Fix → 5. Verify

1. Reproduce the Error

• Get exact steps to reproduce
• Note environment (OS, versions, config)
• Check if error is consistent or intermittent
• Capture full error message and stack trace

2. Isolate the Problem

• Identify which component is failing
• Check recent changes (git log, deployments)
• Narrow down to smallest reproducible case
• Check if issue is in your code or dependencies

3. Identify Root Cause

• Read the actual error message carefully
• Follow the stack trace top-to-bottom
• Check logs at the time of failure
• Add temporary logging if needed

4. Fix the Issue

• Address the root cause, not symptoms
• Consider side effects of the fix
• Add tests to prevent regression

5. Verify the Fix

• Reproduce original steps - should work now
• Run full test suite
• Check for unintended side effects

Common Error Patterns

Python Exceptions

Exception	Common Cause	Investigation
KeyError	Missing dict key	Check if key exists before access
AttributeError	None object access	Check for None before method call
TypeError	Wrong argument type	Check function signature and input
ImportError	Missing/circular import	Check import paths and dependencies
ConnectionError	Network/DB issues	Check connectivity and credentials

Stack Trace Analysis

# Example stack trace:
# Traceback (most recent call last):
#   File "app.py", line 42, in handle_request    ← Entry point
#     result = process_data(data)
#   File "processor.py", line 15, in process_data ← Intermediate
#     return transform(data['key'])
#   File "processor.py", line 8, in transform      ← Root cause
#     return value.upper()
# AttributeError: 'NoneType' object has no attribute 'upper'

# The error is at line 8, but the fix might be:
# - Line 15: Check if 'key' exists in data
# - Line 8: Check if value is None before .upper()
# - Caller: Ensure data is properly validated