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.

Copy this checklist and track your progress:

Debugging Progress:
- [ ] Phase 1: Root Cause Investigation
  - [ ] Read error messages carefully
  - [ ] Reproduce consistently
  - [ ] Check recent changes
  - [ ] Gather evidence at component boundaries
  - [ ] Trace data flow backward to source
- [ ] Phase 2: Pattern Analysis
  - [ ] Find working examples
  - [ ] Compare against references
  - [ ] Identify differences
- [ ] Phase 3: Hypothesis and Testing
  - [ ] Form single hypothesis
  - [ ] Test minimally (one change)
  - [ ] Verify before continuing
- [ ] Phase 4: Implementation
  - [ ] Create failing test case
  - [ ] Implement single fix at root cause
  - [ ] Apply defense-in-depth
  - [ ] Verify fix and tests pass

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):

For log-heavy investigations: When errors appear in application logs, use the reading-logs skill for efficient analysis. Never load entire log files into context - use targeted grep and filtering.

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 (Root Cause Tracing)

WHEN error is deep in call stack or unclear where invalid data originated:

Don't fix symptoms. Trace backward through the call chain to find the original trigger, then fix at the source.

Use Five Whys + Backward Tracing:

Symptom: git init creates .git in source code directory
Why? → cwd parameter is empty string, defaults to process.cwd()
Why? → projectDir variable passed to git init is ''
Why? → Session.create() received empty tempDir
Why? → Test accessed context.tempDir before beforeEach initialized it
Why? → setupCoreTest() returns object with tempDir: '' initially
Root Cause: Top-level variable initialization accessing uninitialized value

Trace the Call Chain backward:

execFileAsync('git', ['init'], { cwd: projectDir })  // Symptom
  ← WorktreeManager.createSessionWorktree(projectDir, sessionId)
  ← Session.initializeWorkspace()
  ← Session.create(tempDir)
  ← Test: Project.create('name', context.tempDir)  // Root trigger

Adding Instrumentation when call chain is unclear:

async function gitInit(directory: string) {
  const stack = new Error().stack;
  console.error("DEBUG:", { directory, cwd: process.cwd(), stack });
  await execFileAsync("git", ["init"], { cwd: directory });
}

Key points:

• Use console.error() in tests (logger may be suppressed)
• Log before the operation, not after it fails
• Include context: directory, cwd, environment variables

Verify the Root Cause:

• If you fix at the source, does the symptom disappear?
• Does the fix prevent recurrence across all code paths?
• Can you add validation to catch it early?

Tactical Debugging Techniques

When executing the four phases, use these techniques to gather evidence:

• Binary Search / Code Bisection: Systematically narrow down the problem area
• Minimal Reproduction: Strip away everything non-essential
• Strategic Logging & Instrumentation: Add diagnostic output at key points
• Runtime Assertions: Make assumptions explicit and fail fast
• Differential Analysis: Compare working vs broken states
• Multi-Component System Debugging: Add instrumentation at each boundary

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

2. Implement Single Fix

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

3. Apply Defense-in-Depth

Don't just fix the root cause - add validation at each layer:

1. Root fix: Prevent the bug at its source
2. Layer 1: Entry point validates inputs
3. Layer 2: Core logic validates preconditions
4. Layer 3: Environment guards (NODE_ENV checks, directory restrictions)

Result: Bug impossible to reintroduce, even with future code changes.

4. Verify Fix

• Test passes now?
• No other tests broken?
• Issue actually resolved?

5. 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 6 below)
• DON'T attempt Fix #4 without architectural discussion

6. 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 4.6)

Partner 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

Excuse	Reality
"Issue is simple, don't need process"	Simple issues have root causes too. Process is fast for simple bugs.
"Emergency, no time for process"	Systematic debugging is FASTER than guess-and-check thrashing.
"Just try this first, then investigate"	First fix sets the pattern. Do it right from the start.
"I'll write test after confirming fix works"	Untested fixes don't stick. Test first proves it.
"Multiple fixes at once saves time"	Can't isolate what worked. Causes new bugs.
"Reference too long, I'll adapt the pattern"	Partial understanding guarantees bugs. Read it completely.
"I see the problem, let me fix it"	Seeing symptoms ≠ understanding root cause.
"One more fix attempt" (after 2+ failures)	3+ failures = architectural problem. Question pattern, don't fix again.

Quick Reference

Phase	Key Activities	Success Criteria
1. Root Cause	Read errors, reproduce, check changes, trace data flow	Understand WHAT and WHY
2. Pattern	Find working examples, compare	Identify differences
3. Hypothesis	Form theory, test minimally	Confirmed or new hypothesis
4. Implementation	Create test, fix with defense-in-depth, verify	Bug resolved, tests pass

Reporting Your Findings

After completing the debugging process:

## Root Cause

[Explain the underlying issue in 1-3 sentences]
Located in: `file.ts:123`

## What Was Wrong

[Describe the specific problem - mutation, race condition, missing validation,
incorrect assumption, etc. Be technical and specific.]

## The Fix

[Describe the changes made and why they address the root cause]

Changes in:
- `file.ts:123-125` - [what changed and why]
- `test.ts:45` - [added regression test]

## Verification

- [x] Bug reproduced and confirmed fixed
- [x] Existing tests pass
- [x] Added regression test
- [x] Checked for similar issues in related code
- [x] No new errors or warnings introduced

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.

Integration

Complementary skills:

• writing-tests - For creating failing test case in Phase 4
• condition-based-waiting - Replace arbitrary timeouts identified in Phase 2
• verification-before-completion - Verify fix worked before claiming success
• reading-logs - Efficient log analysis for evidence gathering in Phases 1-2

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

Remember: Fixing symptoms creates technical debt. Finding root causes eliminates entire classes of bugs.