Systematic Debugging

Overview

Random fixes waste time and create new bugs. Quick patches mask underlying issues.

Core principle: ALWAYS find root cause before attempting fixes. Symptom fixes are failure.

Violating the letter of this process is violating the spirit of debugging.

When This Applies

Whenever something isn't working as expected. This is NOT optional.

Use for ANY technical issue:

• •Test failures
• •Bugs in production
• •Unexpected behavior
• •Performance problems
• •Build failures
• •Integration issues

Use this ESPECIALLY when:

• •Under time pressure (emergencies make guessing tempting)
• •"Just one quick fix" seems obvious
• •You've already tried multiple fixes
• •You don't fully understand the issue

Don't skip when:

• •Issue seems simple (simple bugs have root causes too)
• •You're in a hurry (rushing guarantees rework)

The Iron Law

NO FIXES WITHOUT ROOT CAUSE INVESTIGATION FIRST

If you haven't completed Phase 1, you cannot propose fixes.

FORBIDDEN: Random changes hoping something works.

The Four Phases

You MUST complete each phase before proceeding to the next.

Phase 1: OBSERVE

Gather evidence before forming theories.

1. •

   Reproduce the issue

   • •What exact steps trigger it?
   • •Is it consistent or intermittent?
   • •What's the exact error message?
2. •

   Collect context

   • •What was the input?
   • •What was the expected output?
   • •What was the actual output?
   • •What changed recently?
3. •

   Read error messages carefully

   • •Don't skip past errors or warnings
   • •Read stack traces completely
   • •Note line numbers, file paths, error codes
4. •

   Document observations

   code

   Issue: Upload fails with "AccessDenied"
   Reproduces: Every time with files > 1MB
   Works: Files < 1MB upload successfully
   Recent changes: Updated AWS SDK yesterday
   

5. •

   Gather evidence in multi-component systems

   BEFORE proposing fixes, add diagnostic instrumentation:

   code

   For EACH component boundary:
     - Log what data enters component
     - Log what data exits component
     - Verify environment/config propagation
     - Check state at each layer
   
   Run once to gather evidence showing WHERE it breaks
   THEN analyze evidence to identify failing component
   THEN investigate that specific component
   

Phase 2: HYPOTHESIZE

Form testable theories based on evidence.

1. •

   List possible causes

   • •Each hypothesis must be testable
   • •Rank by likelihood based on evidence
   • •Include "obvious" causes (they're often right)
2. •

   Document hypotheses

   code

   H1: S3 bucket policy changed (likelihood: low - no recent changes)
   H2: AWS SDK breaking change (likelihood: high - updated yesterday)
   H3: File size validation wrong (likelihood: medium - size-related)
   

Phase 3: TEST

Validate or eliminate hypotheses systematically.

1. •Test highest likelihood first
2. •One variable at a time
3. •Document results

Testing H2: AWS SDK breaking change
Action: Downgrade AWS SDK to previous version
Result: Upload works
Conclusion: H2 confirmed - SDK update introduced issue

1. •When you don't know
   • •Say "I don't understand X"
   • •Don't pretend to know
   • •Ask for help
   • •Research more

Phase 4: FIX

Address the ROOT CAUSE, not symptoms.

1. •

   Create failing test case

   • •Simplest possible reproduction
   • •Automated test if possible
   • •MUST have before fixing
   • •Use the kimchi:tdd skill for writing proper failing tests
2. •

   Implement single fix

   • •Address the root cause identified
   • •ONE change at a time
   • •No "while I'm here" improvements
   • •No bundled refactoring
3. •

   Verify the fix

   • •Original issue no longer reproduces
   • •No new issues introduced
   • •Tests pass
4. •

   If fix doesn't work

   • •STOP
   • •Count: How many fixes have you tried?
   • •If < 3: Return to Phase 1, re-analyze with new information
   • •If >= 3: STOP and question the architecture (step 5)
   • •DON'T attempt Fix #4 without architectural discussion
5. •

   If 3+ fixes failed: question architecture

   Pattern indicating architectural problem:

   • •Each fix reveals new shared state/coupling/problem in different place
   • •Fixes require "massive refactoring" to implement
   • •Each fix creates new symptoms elsewhere

   STOP and question fundamentals:

   • •Is this pattern fundamentally sound?
   • •Are we "sticking with it through sheer inertia"?
   • •Should we refactor architecture vs. continue fixing symptoms?

   Discuss with your human partner before attempting more fixes.

Common Rationalizations

Red Flags — STOP and Follow Process

If you catch yourself thinking:

• •"Quick fix for now, investigate later"
• •"Just try changing X and see if it works"
• •"Add multiple changes, run tests"
• •"Skip the test, I'll manually verify"
• •"It's probably X, let me fix that"
• •"I don't fully understand but this might work"
• •Proposing solutions before tracing data flow
• •"One more fix attempt" (when already tried 2+)
• •Each fix reveals new problem in different place

ALL of these mean: STOP. Return to Phase 1.

If 3+ fixes failed: Question the architecture.

Verification

• • Issue was reproduced and documented
• • Multiple hypotheses were considered
• • Root cause was identified (not just symptoms)
• • Fix addresses root cause
• • Test added to prevent recurrence

Quick Reference