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.

The Iron Law

NO FIXES WITHOUT ROOT CAUSE INVESTIGATION FIRST

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

When to Use

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
• Previous fix didn't work
• 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)
• Manager wants it fixed NOW (systematic is faster than thrashing)

The Four Phases

You MUST complete each phase before proceeding to the next.

Phase 1: Root Cause Investigation

BEFORE attempting ANY fix:

1. Read Error Messages Carefully
   • Don't skip past errors or warnings
   • They often contain the exact solution
   • Read stack traces completely
   • Note line numbers, file paths, error codes
2. Reproduce Consistently
   • Can you trigger it reliably?
   • What are the exact steps?
   • Does it happen every time?
   • If not reproducible → gather more data, don't guess
3. Check Recent Changes
   • What changed that could cause this?
   • Git diff, recent commits
   • New dependencies, config changes
   • Environmental differences

4. Gather Evidence in Multi-Component Systems

   WHEN system has multiple components (CI → build → signing, API → service → database):

   BEFORE proposing fixes, add diagnostic instrumentation:

   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

   Example (multi-layer system):

   # Layer 1: Workflow
   echo "=== Secrets available in workflow: ==="
   echo "IDENTITY: ${IDENTITY:+SET}${IDENTITY:-UNSET}"
   
   # Layer 2: Build script
   echo "=== Env vars in build script: ==="
   env | grep IDENTITY || echo "IDENTITY not in environment"
   
   # Layer 3: Signing script
   echo "=== Keychain state: ==="
   security list-keychains
   security find-identity -v
   
   # Layer 4: Actual signing
   codesign --sign "$IDENTITY" --verbose=4 "$APP"

   This reveals: Which layer fails (secrets → workflow ✓, workflow → build ✗)

5. Trace Data Flow

   WHEN error is deep in call stack:

   See root-cause-tracing.md in this directory for the complete backward tracing technique.

   Quick version:

   • Where does bad value originate?
   • What called this with bad value?
   • Keep tracing up until you find the source
   • Fix at source, not at symptom

Phase 2: Pattern Analysis

Find the pattern before fixing:

1. Find Working Examples
   • Locate similar working code in same codebase
   • What works that's similar to what's broken?
2. Compare Against References
   • If implementing pattern, read reference implementation COMPLETELY
   • Don't skim - read every line
   • Understand the pattern fully before applying
3. Identify Differences
   • What's different between working and broken?
   • List every difference, however small
   • Don't assume "that can't matter"
4. Understand Dependencies
   • What other components does this need?
   • What settings, config, environment?
   • What assumptions does it make?

Phase 3: Hypothesis and Testing

Scientific method:

1. Form Single Hypothesis
   • State clearly: "I think X is the root cause because Y"
   • Write it down
   • Be specific, not vague
2. Test Minimally
   • Make the SMALLEST possible change to test hypothesis
   • One variable at a time
   • Don't fix multiple things at once
3. Verify Before Continuing
   • Did it work? Yes → Phase 4
   • Didn't work? Form NEW hypothesis
   • DON'T add more fixes on top
4. When You Don't Know
   • Say "I don't understand X"
   • Don't pretend to know
   • Ask for help
   • Research more

Phase 4: Implementation

Fix the root cause, not the symptom:

1. Create Failing Test Case
   • Simplest possible reproduction
   • Automated test if possible
   • One-off test script if no framework
   • MUST have before fixing
   • Use the superpowers:test-driven-development 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 Fix
   • Test passes now?
   • No other tests broken?
   • Issue actually resolved?
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 below)
   • 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

   This is NOT a failed hypothesis - this is a wrong architecture.

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"
• "Pattern says X but I'll adapt it differently"
• "Here are the main problems: [lists fixes without investigation]"
• 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 (see Phase 4.5)

your human partner's Signals You're Doing It Wrong

Watch for these redirections:

• "Is that not happening?" - You assumed without verifying
• "Will it show us...?" - You should have added evidence gathering
• "Stop guessing" - You're proposing fixes without understanding
• "Ultrathink this" - Question fundamentals, not just symptoms
• "We're stuck?" (frustrated) - Your approach isn't working

When you see these: STOP. Return to Phase 1.

Common Rationalizations

Common Pitfalls

These are anti-patterns observed in debugging practice. Recognize them and correct course immediately. See also: Red Flags and Common Rationalizations for related guidance.

1. Jumping to Solutions Before Reproducing (Premature Fixes)

Anti-pattern:

Error log shows "timeout" → assume network issue → add retry logic
(But you never actually reproduced the timeout)

Problem:

• Fix addresses a symptom you haven't verified
• Real issue remains unfixed, masked by the "fix"
• When symptom recurs, you waste time debugging your own patch

How to avoid:

• Phase 1 requirement: Can you trigger the bug reliably with known steps?
• Document exact reproduction: "Run npm test with X set to Y" → bug happens
• If you can't reproduce it, you can't verify your fix works
• Reproducibility is non-negotiable — gather more data until you can trigger it

Recovery:

• Stop. Return to Phase 1.
• Find exact reproduction steps.
• THEN move to Phase 2.

2. Changing Multiple Things at Once (Can't Isolate Root Cause)

Anti-pattern:

Change 3 things:
  - Update dependency version
  - Refactor retry logic
  - Add fallback handler
Run tests → passes

Problem:

• Which change fixed it? Unknown.
• If one change introduces a subtle bug, you won't catch it for months
• Future bugs in that code path will be doubly confusing

How to avoid:

• Phase 3 requirement: "Test minimally. Make the SMALLEST possible change."
• ONE variable at a time
• Test after EACH change
• If you can't isolate what fixed it, the fix is incomplete knowledge

Recovery:

• Revert all changes. Start over.
• Change ONE thing. Test.
• Document: "This change alone fixed it because [reason]"
• Repeat for next hypothesis.

3. Assuming Bug Is in Recent Changes (Ignoring Environment/Data)

Anti-pattern:

Feature shipped 3 days ago → bug happened today → bug must be in that feature
(But you didn't verify the feature code against current data/environment)

Problem:

• Recent code LOOKS suspicious but environment changes are hard to see
• You waste days debugging code that's actually fine
• Real cause: data corruption, environment misconfiguration, dependency update

How to avoid:

• Phase 1 requirement: "Check Recent Changes" includes:
  • Git diffs in code? Yes.
  • Dependency updates? Yes.
  • Environment changes? Yes.
  • Data migrations? Yes.
  • Config changes? Yes.
• Test hypothesis: revert recent code change, see if bug persists
• If bug still happens without recent changes, it's environmental
• For multi-component systems, use diagnostic instrumentation (see Phase 1 Step 4)

Recovery:

• List ALL changes in last week: code, dependencies, config, environment, data
• Systematically rule out environment/data causes
• Only then focus on code

4. Not Documenting Reproduction Steps (Can't Verify Fix)

Anti-pattern:

"Fixed the bug" (but didn't write down how to trigger it)
Weeks later: "Wait, does this still happen? I don't remember how to trigger it"

Problem:

• You can't verify your fix actually works
• You can't tell colleagues how to test it
• You can't catch regression

How to avoid:

• Phase 1 requirement: "Reproduce Consistently"
  • Write down exact steps
  • Format: "To reproduce: [step 1], [step 2], [step 3]"
• Before moving to Phase 2, add these steps as a comment in your PR/issue
• After implementing fix, re-run exact steps to verify
• Convert reproduction steps to a test case (Phase 4, Step 1)

Recovery:

• Document reproduction steps NOW
• Don't claim fix is done until you've re-run exact steps
• If you documented reproduction steps and can no longer trigger the bug after the fix, that confirms the fix works

5. Debugging Without Logs/Observability (Flying Blind)

Anti-pattern:

Error happens in production
You: "Probably a race condition?"
You don't have logs showing what was happening at that moment
You guess at fixes for 3 hours

Problem:

• You're making decisions without evidence
• Guessing at fixes creates new bugs
• Same error will recur because you never understood cause

How to avoid:

• Phase 1 requirement: "Gather Evidence in Multi-Component Systems"
• Before debugging, ask: "What logs do we have?"
• If missing: add temporary logging, reproduce locally
• Don't propose fixes without evidence of what's happening
• For production issues: capture logs/metrics at time of failure

Specific strategies:

# Reproduce with logging
DEBUG=* npm test           # Enable all logs
strace node app.js         # System call tracing
tcpdump -i any -w dump     # Network tracing

Recovery:

• Pause. Add logging for key decision points.
• Reproduce the issue with enhanced logging.
• Now you have evidence to form hypothesis.
• THEN propose fix.

6. Testing Without a Failing Test Case (Verification Theater)

Anti-pattern:

Implement fix
Run manual test: "Looks good"
Commit and push
(No automated test proving bug existed)

Problem:

• You "fixed" something, but you never proved it was broken
• Regression testing later shows the bug still happens
• You can't distinguish between "I fixed it" and "I got lucky"

How to avoid:

• Phase 4 requirement: "Create Failing Test Case"
  • Test MUST fail before fix
  • Test MUST pass after fix
  • Test proves bug existed, fix eliminates bug
• Don't implement fix without a failing test
• Automated test (unit test, integration test, one-off script) > manual verification

Recovery:

• Revert your fix
• Write test that reproduces the bug
• Verify test fails
• Implement fix
• Verify test passes

7. Not Isolating the Failing Component (Blame Everything)

Anti-pattern:

Database slow? Maybe it's the query. Maybe it's the network. Maybe it's CPU.
Let me add caching, fix indexes, reduce batch size, and upgrade servers.

Problem:

• You waste resources fixing things that aren't broken
• You never understand the actual bottleneck
• Future optimization attempts fail because you're not sure what to optimize

How to avoid:

• Phase 1 requirement: "Gather Evidence in Multi-Component Systems"
  • For each component boundary, log data entering and exiting
  • Identify WHICH component is slow/failing
  • Focus investigation on that one component
  • Don't optimize 5 things, optimize the bottleneck

Example:

# API slow. Which part?
time curl /api/endpoint              # Total time
# Add logging to API entry point
# Add logging before DB query
# Add logging after DB returns
# Add logging before response

# Now you know: API fast (10ms), DB query slow (2s) → focus on DB

Recovery:

• Use diagnostic instrumentation to identify which component is actually failing
• Don't propose fixes until you've isolated the specific component

8. Assuming Your Fix Is Complete (Missing Edge Cases)

Anti-pattern:

Test case passes for happy path
Deploy without testing error cases
(Bug appears 2 weeks later under edge condition you didn't think to test)

Problem:

• Fix addresses one scenario but breaks others
• Edge cases are where 80% of bugs hide

How to avoid:

• Phase 4 requirement: "Verify Fix"
  • Test passing case
  • Test error cases
  • Test boundary conditions
  • Test with invalid/empty/extreme data
  • Test interaction with other components
• Comprehensive test coverage BEFORE deploying
• Regression tests for each bug you've fixed

Recovery:

• Add edge case tests
• Re-run comprehensive test suite
• Don't declare fix done until all cases pass

These pitfalls are patterns, not one-off mistakes. When you catch yourself in one, stop and realign to the process.

Quick Reference

When Process Reveals "No Root Cause"

If systematic investigation reveals issue is truly environmental, timing-dependent, or external:

1. You've completed the process
2. Document what you investigated
3. Implement appropriate handling (retry, timeout, error message)
4. Add monitoring/logging for future investigation

But: 95% of "no root cause" cases are incomplete investigation.

Supporting Techniques

These techniques are part of systematic debugging and available in this directory:

• root-cause-tracing.md - Trace bugs backward through call stack to find original trigger
• defense-in-depth.md - Add validation at multiple layers after finding root cause
• condition-based-waiting.md - Replace arbitrary timeouts with condition polling

Related skills:

• superpowers:test-driven-development - For creating failing test case (Phase 4, Step 1)
• superpowers:verification-before-completion - Verify fix worked before claiming success

Real-World Impact

From debugging sessions:

• Systematic approach: 15-30 minutes to fix
• Random fixes approach: 2-3 hours of thrashing
• First-time fix rate: 95% vs 40%
• New bugs introduced: Near zero vs common