systematic-debugging

name	systematic-debugging
description	4-phase root cause debugging process. Use when encountering any bug, test failure, or unexpected behavior BEFORE proposing fixes. Prevents guess-and-check thrashing. Includes root-cause tracing, defense-in-depth, and architecture questioning.

Systematic Debugging

Debug: $ARGUMENTS

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, unexpected behavior, performance problems, build failures, integration issues.

Use ESPECIALLY when:

• Under time pressure (emergencies make guessing tempting)
• "Just one quick fix" seems obvious
• You've already tried multiple fixes
• You don't fully understand the issue

Phase 1: Root Cause Investigation

BEFORE attempting ANY fix:

1. Read error messages carefully

   • Don't skip past errors or warnings
   • Read stack traces completely
   • Note line numbers, file paths, error codes

2. Reproduce consistently

   • Can you trigger it reliably?
   • What are the exact steps?
   • If not reproducible, gather more data — don't guess

3. Check recent changes

   • git diff, recent commits
   • New dependencies, config changes
   • Environmental differences

4. Gather evidence in multi-component systems

   For EACH component boundary:
     - Log what data enters component
     - Log what data exits component
     - Verify environment/config propagation
   
   Run once to gather evidence showing WHERE it breaks
   THEN analyze evidence to identify failing component
   THEN investigate that specific component
   

5. Trace data flow (root-cause tracing)

   • Where does the bad value originate?
   • What called this with the bad value?
   • Keep tracing backward until you find the source
   • Fix at source, not at symptom

Phase 2: Pattern Analysis

1. Find working examples — locate similar working code in the same codebase
2. Compare against references — read reference implementations COMPLETELY, don't skim
3. Identify differences — list every difference between working and broken, however small
4. Understand dependencies — what components, settings, config, environment does this need?

Phase 3: Hypothesis and Testing

1. Form single hypothesis — "I think X is the root cause because Y" — be specific
2. Test minimally — smallest possible change, one variable at a time
3. Verify before continuing — worked? proceed. Didn't work? new hypothesis. Don't stack fixes.
4. When you don't know — say "I don't understand X" — don't pretend

Phase 4: Implementation

1. Create failing test case — simplest reproduction, automated if possible. Use TDD skill.
2. Implement single fix — address root cause, ONE change at a time, no "while I'm here" improvements
3. Verify fix — test passes? 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 with new information
   • If >= 3: STOP and question the architecture (see below)

After 3+ Failed Fixes: Question Architecture

Pattern indicating architectural problem:

• Each fix reveals new shared state/coupling in different places
• 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 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.

Defense in Depth

After finding and fixing the root cause, add validation at multiple layers:

1. Input validation — catch bad data at the boundary where it enters
2. Internal assertions — verify invariants at key checkpoints
3. Output validation — confirm results before returning/persisting
4. Monitoring — add logging/metrics to detect recurrence

Not all layers are needed for every fix — apply proportionally to severity.

Condition-Based Waiting

When debugging timing/async issues, replace arbitrary timeouts with condition polling:

INSTEAD OF: sleep(5)  # hope it's ready
USE:        poll until condition is true, with timeout

Arbitrary timeouts mask root causes and create flaky tests.

Rationalization Red Flags

Thought	Reality
"Quick fix for now, investigate later"	Symptom fixes mask root causes
"Just try changing X and see if it works"	That's guess-and-check, not debugging
"Add multiple changes, run tests"	Can't isolate what worked
"Skip the test, I'll manually verify"	Untested fixes don't stick
"It's probably X, let me fix that"	"Probably" means you haven't investigated
"Pattern says X but I'll adapt differently"	Partial understanding guarantees bugs
"One more fix attempt" (after 2+ failures)	3+ failures = architectural problem
"Here are the main problems: [lists fixes]"	Listing fixes before investigation = guessing

ALL of these mean: STOP. Return to Phase 1.

Quick Reference

Phase	Key Activities	Gate
1. Root Cause	Read errors, reproduce, check changes, trace data	Understand WHAT and WHY
2. Pattern	Find working examples, compare, identify differences	Know what's different
3. Hypothesis	Form theory, test minimally, one variable	Confirmed or new hypothesis
4. Implementation	Create test, fix root cause, verify	Bug resolved, tests pass