jimweller/systematic-debugging

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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 Phase 1 is incomplete, no fixes can be proposed.

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
• Multiple fixes have already been tried
• A previous fix did not work
• The issue is not fully understood

Do not skip when:

• The issue seems simple (simple bugs have root causes too)
• The deadline is tight (rushing guarantees rework)
• Manager wants it fixed NOW (systematic is faster than thrashing)

The Four Phases

Each phase must be complete before proceeding to the next.

Phase 1: Root Cause Investigation

Before attempting any fix:

1. Read error messages carefully

   • Do not skip past errors or warnings
   • Errors often contain the exact solution
   • Read stack traces completely
   • Note line numbers, file paths, error codes

2. Reproduce consistently

   • Can the failure be triggered reliably?
   • What are the exact steps?
   • Does it happen every time?
   • If not reproducible: gather more data, do not 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 the system has multiple components (CI to build to signing, API to service to 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 instrumentation:

   # 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 to workflow OK, workflow to build broken).

5. Trace data flow

   When the error is deep in the call stack, see @root-cause-tracing.md in this directory for the complete backward tracing technique.

   Quick version:

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

Phase 2: Pattern Analysis

Find the pattern before fixing.

1. Find working examples

   • Locate similar working code in the same codebase
   • What works that is similar to what is broken?

2. Compare against references

   • If implementing a pattern, read the reference implementation completely
   • Do not skim, read every line
   • Understand the pattern fully before applying

3. Identify differences

   • What is different between working and broken?
   • List every difference, however small
   • Do not assume "that cannot matter"

4. Understand dependencies

   • What other components does this need?
   • What settings, config, environment?
   • What assumptions does it make?

Phase 3: Hypothesis and Testing

Apply the scientific method.

1. Form a single hypothesis

   • State clearly: "X is the root cause because Y"
   • Write it down
   • Be specific, not vague

2. Test minimally

   • Make the smallest possible change to test the hypothesis
   • One variable at a time
   • Do not fix multiple things at once

3. Verify before continuing

   • Did it work? Yes: Phase 4
   • Did it not work? Form a NEW hypothesis
   • Do not stack more fixes on top

4. When stuck

   • State "I do not understand X"
   • Do not 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 test-driven-development skill for writing proper failing tests
   • Use the verify-claims skill to prove the test catches the bug (red-green-revert-restore)

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 does not work

   • STOP
   • Count: how many fixes have been tried?
   • If less than 3: return to Phase 1, re-analyze with new information
   • If 3 or more: STOP and question the architecture (step 5 below)
   • Do NOT attempt fix #4 without architectural discussion

5. If 3+ fixes failed: question architecture

   Patterns indicating an architectural problem:

   • Each fix reveals new shared state, coupling, or problem in a different place
   • Fixes require "massive refactoring" to implement
   • Each fix creates new symptoms elsewhere

   STOP and question fundamentals:

   • Is this pattern fundamentally sound?
   • Is the team "sticking with it through sheer inertia"?
   • Should the architecture be refactored vs. continuing to fix 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

Catch these thoughts:

• "Quick fix for now, investigate later"
• "Just try changing X and see if it works"
• "Add multiple changes, run tests"
• "Skip the test, manually verify"
• "It's probably X, fix that"
• "Don't fully understand but this might work"
• "Pattern says X but 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 step 5).

User Signals That The Approach Is Wrong

Watch for these redirections:

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

When these appear: STOP. Return to Phase 1.

Common Rationalizations

| Excuse | Reality | | ------------------------------------------- | ------------------------------------------------------------------------ | | "Issue is simple, no need for 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. | | "Write the test after confirming fix works" | Untested fixes do not stick. Test first proves it. | | "Multiple fixes at once saves time" | Cannot isolate what worked. Causes new bugs. | | "Reference too long, adapt the pattern" | Partial understanding guarantees bugs. Read it completely. | | "I see the problem, fix it" | Seeing symptoms is not understanding root cause. | | "One more fix attempt" (after 2+ failures) | 3+ failures means architectural problem. Question pattern, do not retry. |

Quick Reference

| Phase | Key Activities | Success Criteria | | ----------------- | ------------------------------------------------------ | --------------------------- | | 1. Root Cause | Read errors, reproduce, check changes, gather evidence | 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, verify | Bug resolved, tests pass |

When Process Reveals "No Root Cause"

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

1. The process is complete
2. Document what was 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 live 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:

• test-driven-development - For creating failing test case (Phase 4, Step 1)
• verify-claims - Verify fix worked before claiming success