Systematic Debugging

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

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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:

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   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):

   bashCopy code
   # 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:

   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

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 the user 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)

Common Rationalizations

Quick Reference

Technique: Root Cause Tracing

When bugs manifest deep in the call stack, trace backward to find the original trigger.

The Tracing Process

1. Observe the Symptom

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   Error: git init failed in /Users/jesse/project/packages/core
   

2. Find Immediate Cause - What code directly causes this?

   typescriptCopy code
   await execFileAsync('git', ['init'], { cwd: projectDir })
   

3. Ask: What Called This?

   typescriptCopy code
   WorktreeManager.createSessionWorktree(projectDir, sessionId)
     → called by Session.initializeWorkspace()
     → called by Session.create()
     → called by test at Project.create()
   

4. Keep Tracing Up - What value was passed?

   • projectDir = '' (empty string!)
   • Empty string as cwd resolves to process.cwd()

5. Find Original Trigger - Where did empty string come from?

   typescriptCopy code
   const context = setupCoreTest() // Returns { tempDir: '' }
   Project.create('name', context.tempDir) // Accessed before beforeEach!
   

Adding Stack Traces

When you can't trace manually, add instrumentation:

typescriptCopy code
async function gitInit(directory: string) {
  const stack = new Error().stack
  console.error('DEBUG git init:', {
    directory,
    cwd: process.cwd(),
    nodeEnv: process.env.NODE_ENV,
    stack,
  })
  await execFileAsync('git', ['init'], { cwd: directory })
}

Tips:

• Use console.error() in tests (logger may be suppressed)
• Log before the dangerous operation, not after it fails
• Include context: directory, cwd, environment variables
• new Error().stack shows complete call chain

Finding Which Test Causes Pollution

If something appears during tests but you don't know which test, use bisection:

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# Run tests one-by-one, stop at first polluter
for f in src/**/*.test.ts; do
  npm test "$f" && [ -d .git ] && echo "POLLUTER: $f" && break
done

NEVER fix just where the error appears. Trace back to find the original trigger.

Technique: Defense-in-Depth Validation

After finding root cause, validate at EVERY layer data passes through. Make the bug structurally impossible.

Why Multiple Layers

• Single validation: "We fixed the bug"
• Multiple layers: "We made the bug impossible"

Different layers catch different cases:

• Entry validation catches most bugs
• Business logic catches edge cases
• Environment guards prevent context-specific dangers
• Debug logging helps when other layers fail

The Four Layers

Layer 1: Entry Point Validation - Reject invalid input at API boundary

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function createProject(name: string, workingDirectory: string) {
  if (!workingDirectory || workingDirectory.trim() === '') {
    throw new Error('workingDirectory cannot be empty')
  }
  if (!existsSync(workingDirectory)) {
    throw new Error(`workingDirectory does not exist: ${workingDirectory}`)
  }
}

Layer 2: Business Logic Validation - Ensure data makes sense for operation

typescriptCopy code
function initializeWorkspace(projectDir: string, sessionId: string) {
  if (!projectDir) {
    throw new Error('projectDir required for workspace initialization')
  }
}

Layer 3: Environment Guards - Prevent dangerous operations in specific contexts

typescriptCopy code
async function gitInit(directory: string) {
  if (process.env.NODE_ENV === 'test') {
    const normalized = normalize(resolve(directory))
    const tmpDir = normalize(resolve(tmpdir()))
    if (!normalized.startsWith(tmpDir)) {
      throw new Error(`Refusing git init outside temp dir during tests`)
    }
  }
}

Layer 4: Debug Instrumentation - Capture context for forensics

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async function gitInit(directory: string) {
  logger.debug('About to git init', {
    directory,
    cwd: process.cwd(),
    stack: new Error().stack,
  })
}

Applying Defense-in-Depth

When you find a bug:

1. Trace the data flow - Where does bad value originate? Where used?
2. Map all checkpoints - List every point data passes through
3. Add validation at each layer - Entry, business, environment, debug
4. Test each layer - Try to bypass layer 1, verify layer 2 catches it

Don't stop at one validation point. Add checks at every layer.

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