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 Appendix A: Root Cause Tracing 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

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 included as appendices below:

• Appendix A: Root Cause Tracing - Trace bugs backward through call stack to find original trigger
• Appendix B: Defense-in-Depth - Add validation at multiple layers after finding root cause
• Appendix C: Condition-Based Waiting - 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

Appendix A: Root Cause Tracing

Overview

Bugs often manifest deep in the call stack (git init in wrong directory, file created in wrong location, database opened with wrong path). Your instinct is to fix where the error appears, but that's treating a symptom.

Core principle: Trace backward through the call chain until you find the original trigger, then fix at the source.

When to Use

digraph when_to_use {
    "Bug appears deep in stack?" [shape=diamond];
    "Can trace backwards?" [shape=diamond];
    "Fix at symptom point" [shape=box];
    "Trace to original trigger" [shape=box];
    "BETTER: Also add defense-in-depth" [shape=box];

    "Bug appears deep in stack?" -> "Can trace backwards?" [label="yes"];
    "Can trace backwards?" -> "Trace to original trigger" [label="yes"];
    "Can trace backwards?" -> "Fix at symptom point" [label="no - dead end"];
    "Trace to original trigger" -> "BETTER: Also add defense-in-depth";
}

Use when:

• Error happens deep in execution (not at entry point)
• Stack trace shows long call chain
• Unclear where invalid data originated
• Need to find which test/code triggers the problem

The Tracing Process

1. Observe the Symptom

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

2. Find Immediate Cause

What code directly causes this?

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

3. Ask: What Called This?

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()
• That's the source code directory!

5. Find Original Trigger

Where did empty string come from?

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

Adding Stack Traces

When you can't trace manually, add instrumentation:

// Before the problematic operation
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 });
}

Critical: Use console.error() in tests (not logger - may not show)

Run and capture:

npm test 2>&1 | grep 'DEBUG git init'

Analyze stack traces:

• Look for test file names
• Find the line number triggering the call
• Identify the pattern (same test? same parameter?)

Finding Which Test Causes Pollution

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

Use the bisection script find-polluter.sh in this directory:

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

Runs tests one-by-one, stops at first polluter. See script for usage.

Real Example: Empty projectDir

Symptom: .git created in packages/core/ (source code)

Trace chain:

1. git init runs in process.cwd() - empty cwd parameter
2. WorktreeManager called with empty projectDir
3. Session.create() passed empty string
4. Test accessed context.tempDir before beforeEach
5. setupCoreTest() returns { tempDir: '' } initially

Root cause: Top-level variable initialization accessing empty value

Fix: Made tempDir a getter that throws if accessed before beforeEach

Also added defense-in-depth:

• Layer 1: Project.create() validates directory
• Layer 2: WorkspaceManager validates not empty
• Layer 3: NODE_ENV guard refuses git init outside tmpdir
• Layer 4: Stack trace logging before git init

Key Principle

digraph principle {
    "Found immediate cause" [shape=ellipse];
    "Can trace one level up?" [shape=diamond];
    "Trace backwards" [shape=box];
    "Is this the source?" [shape=diamond];
    "Fix at source" [shape=box];
    "Add validation at each layer" [shape=box];
    "Bug impossible" [shape=doublecircle];
    "NEVER fix just the symptom" [shape=octagon, style=filled, fillcolor=red, fontcolor=white];

    "Found immediate cause" -> "Can trace one level up?";
    "Can trace one level up?" -> "Trace backwards" [label="yes"];
    "Can trace one level up?" -> "NEVER fix just the symptom" [label="no"];
    "Trace backwards" -> "Is this the source?";
    "Is this the source?" -> "Trace backwards" [label="no - keeps going"];
    "Is this the source?" -> "Fix at source" [label="yes"];
    "Fix at source" -> "Add validation at each layer";
    "Add validation at each layer" -> "Bug impossible";
}

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

Stack Trace Tips

In tests: Use console.error() not logger - logger may be suppressed Before operation: Log before the dangerous operation, not after it fails Include context: Directory, cwd, environment variables, timestamps Capture stack: new Error().stack shows complete call chain

Real-World Impact

From debugging session (2025-10-03):

• Found root cause through 5-level trace
• Fixed at source (getter validation)
• Added 4 layers of defense
• 1847 tests passed, zero pollution

Appendix B: Defense-in-Depth Validation

Overview

When you fix a bug caused by invalid data, adding validation at one place feels sufficient. But that single check can be bypassed by different code paths, refactoring, or mocks.

Core principle: 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

Purpose: Reject obviously invalid input at API boundary

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}`);
  }
  if (!statSync(workingDirectory).isDirectory()) {
    throw new Error(`workingDirectory is not a directory: ${workingDirectory}`);
  }
  // ... proceed
}

Layer 2: Business Logic Validation

Purpose: Ensure data makes sense for this operation

function initializeWorkspace(projectDir: string, sessionId: string) {
  if (!projectDir) {
    throw new Error('projectDir required for workspace initialization');
  }
  // ... proceed
}

Layer 3: Environment Guards

Purpose: Prevent dangerous operations in specific contexts

async function gitInit(directory: string) {
  // In tests, refuse git init outside temp directories
  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: ${directory}`
      );
    }
  }
  // ... proceed
}

Layer 4: Debug Instrumentation

Purpose: Capture context for forensics

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

Applying the Pattern

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

Example from Session

Bug: Empty projectDir caused git init in source code

Data flow:

1. Test setup - empty string
2. Project.create(name, '')
3. WorkspaceManager.createWorkspace('')
4. git init runs in process.cwd()

Four layers added:

• Layer 1: Project.create() validates not empty/exists/writable
• Layer 2: WorkspaceManager validates projectDir not empty
• Layer 3: WorktreeManager refuses git init outside tmpdir in tests
• Layer 4: Stack trace logging before git init

Result: All 1847 tests passed, bug impossible to reproduce

Key Insight

All four layers were necessary. During testing, each layer caught bugs the others missed:

• Different code paths bypassed entry validation
• Mocks bypassed business logic checks
• Edge cases on different platforms needed environment guards
• Debug logging identified structural misuse

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

Appendix C: Condition-Based Waiting

Overview

Flaky tests often guess at timing with arbitrary delays. This creates race conditions where tests pass on fast machines but fail under load or in CI.

Core principle: Wait for the actual condition you care about, not a guess about how long it takes.

When to Use

digraph when_to_use {
    "Test uses setTimeout/sleep?" [shape=diamond];
    "Testing timing behavior?" [shape=diamond];
    "Document WHY timeout needed" [shape=box];
    "Use condition-based waiting" [shape=box];

    "Test uses setTimeout/sleep?" -> "Testing timing behavior?" [label="yes"];
    "Testing timing behavior?" -> "Document WHY timeout needed" [label="yes"];
    "Testing timing behavior?" -> "Use condition-based waiting" [label="no"];
}

Use when:

• Tests have arbitrary delays (setTimeout, sleep, time.sleep())
• Tests are flaky (pass sometimes, fail under load)
• Tests timeout when run in parallel
• Waiting for async operations to complete

Don't use when:

• Testing actual timing behavior (debounce, throttle intervals)
• Always document WHY if using arbitrary timeout

Core Pattern

// BAD: Guessing at timing
await new Promise(r => setTimeout(r, 50));
const result = getResult();
expect(result).toBeDefined();

// GOOD: Waiting for condition
await waitFor(() => getResult() !== undefined);
const result = getResult();
expect(result).toBeDefined();

Quick Patterns

Implementation

Generic polling function:

async function waitFor<T>(
  condition: () => T | undefined | null | false,
  description: string,
  timeoutMs = 5000
): Promise<T> {
  const startTime = Date.now();

  while (true) {
    const result = condition();
    if (result) return result;

    if (Date.now() - startTime > timeoutMs) {
      throw new Error(`Timeout waiting for ${description} after ${timeoutMs}ms`);
    }

    await new Promise(r => setTimeout(r, 10)); // Poll every 10ms
  }
}

See condition-based-waiting-example.ts for complete implementation with domain-specific helpers (waitForEvent, waitForEventCount, waitForEventMatch) from actual debugging session.

Common Mistakes

Polling too fast: setTimeout(check, 1) - wastes CPU Fix: Poll every 10ms

No timeout: Loop forever if condition never met Fix: Always include timeout with clear error

Stale data: Cache state before loop Fix: Call getter inside loop for fresh data

When Arbitrary Timeout IS Correct

// Tool ticks every 100ms - need 2 ticks to verify partial output
await waitForEvent(manager, 'TOOL_STARTED'); // First: wait for condition
await new Promise(r => setTimeout(r, 200));   // Then: wait for timed behavior
// 200ms = 2 ticks at 100ms intervals - documented and justified

Requirements:

1. First wait for triggering condition
2. Based on known timing (not guessing)
3. Comment explaining WHY

Real-World Impact

From debugging session (2025-10-03):

• Fixed 15 flaky tests across 3 files
• Pass rate: 60% -> 100%
• Execution time: 40% faster
• No more race conditions