Systematic Debugging

Contents

• The Iron Law
• When to Use
• The Four Phases
  • Phase 1: Root Cause Investigation
  • Phase 2: Pattern Analysis
  • Phase 3: Hypothesis and Testing
  • Phase 4: Implementation
• Red Flags - STOP and Follow Process
• User Signals You're Doing It Wrong
• Common Rationalizations
• Quick Reference
• When Process Reveals "No Root Cause"
• Supporting Techniques
• Real-World Impact

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)

If the user wants an issue-ready fix plan after debugging, package the findings using the behavior-level template in references/issue-triage.md.

Model Selection for Debugging

Match model to bug complexity. Escalate models alongside abstraction levels.

Map to the 3-strike escalation:

Strike 1-2 (Implementation): Sonnet — fix at code level
Strike 3   (Design/Requirements): Escalate to Opus — rethink the approach
Post-fix   (Validation): Haiku — run tests, confirm green

When orchestrating multi-agent debugging:

Sonnet debugger → fix attempt → Haiku validator (run tests)
  ├── passes → done
  └── fails 3x → Opus for root cause analysis → back to Sonnet

The Four Phases

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 environment
   xcodebuild -showBuildSettings | rg "CODE_SIGN|PROVISIONING"
   
   # Layer 3: Built artifact
   codesign --display --verbose=4 "$APP" || true
   security find-identity -p codesigning -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

6. Bisect to Narrow Scope

   When the failure surface is large, cut it in half repeatedly:

   • Git bisect — find the commit that introduced the bug:

     git bisect start
     git bisect bad              # current commit is broken
     git bisect good <known-good-sha>
     # Git checks out midpoint — test and mark good/bad — repeat

   • Code path bisect — add logging at the midpoint of a call chain. If data is correct there, bug is downstream. If wrong, bug is upstream. Repeat.
   • Test pollution bisect — use find-polluter.sh in this directory to find which test creates unwanted state:

     ./find-polluter.sh '.git' 'src/**/*.test.ts'

7. Diff Environments

   WHEN it works locally but fails in CI/staging/prod:

   • Compare Node/Python/runtime versions
   • Compare env vars: env | sort on both, diff the output
   • Compare installed dependencies: lockfile differences, native module versions
   • Check filesystem differences: paths, permissions, case sensitivity
   • Check network: DNS resolution, firewall rules, proxy settings
   • Check resource limits: memory, CPU, file descriptors, disk space

   Don't assume environments are identical. Explicitly verify each layer.

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
   • Write a proper failing test that reproduces the bug
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)

User Signals You're Doing It Wrong

Watch for these redirections:

• "Is that not happening?" — you assumed without verifying
• "Will it show us...?" — 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

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
• find-polluter.sh — Bisection script to find which test creates unwanted files/state
• three-strike-escalation.md — When 3 fix attempts fail, escalate to the next abstraction level

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

Reference Files

• Condition Based Waiting Example