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)
• Someone 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

Action: Use read on relevant source files. Use bash/symbol_search to find the error and its callers.

2. Build a Tight Feedback Loop

Create one fast, deterministic command that fails on the exact symptom and passes after the fix. Prefer, in order: a focused test, HTTP/CLI repro, a small harness, then a scripted manual flow. Assert the wrong behavior, not merely "doesn't crash". If the issue is flaky, raise reproduction rate before forming theories.

Action: Run the loop at least once before proposing a fix:

pytest tests/test_module.py::test_name -v
# or: python scripts/repro_bug.py

3. Check Recent Changes

• What changed that could cause this?
• Git diff, recent commits
• New dependencies, config changes

Action:

# Recent commits
git log --oneline -10

# Uncommitted changes
git diff

# Changes in specific file
git log -p --follow src/problematic_file.py | head -100

4. Gather Evidence in Multi-Component Systems

WHEN system has multiple components (API → service → database, CI → build → deploy):

BEFORE proposing fixes, add diagnostic instrumentation:

For EACH component boundary:

• Log what data enters the component
• Log what data exits the component
• Verify environment/config propagation
• Check state at each layer

Run once to gather evidence showing WHERE it breaks. THEN analyze evidence to identify the failing component. THEN investigate that specific component.

5. Trace Data Flow

WHEN error is deep in the call stack:

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

Action: Use symbol_search for identifiers or bash/grep for raw strings to trace references:

# Find where the function is called
grep -R "function_name(" src/ --include='*.py'

# Find where the variable is set
grep -R "variable_name[[:space:]]*=" src/ --include='*.py'

Phase 1 Completion Checklist

• Error messages fully read and understood
• Tight loop exists, was run, and asserts the exact symptom
• Recent changes identified and reviewed
• Evidence gathered (logs, state, data flow)
• Problem isolated to specific component/code
• Ranked root-cause hypotheses can be stated and tested

STOP: Do not proceed to Phase 2 until you understand WHY it's happening.

Phase 2: Pattern Analysis

Find the pattern before fixing:

0. Minimize the Reproduction

Remove inputs, callers, config, and steps one at a time, rerunning the tight loop after each removal. Done when removing any remaining element makes the loop pass.

1. Find Working Examples

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

Action: Use symbol_search or bash/grep to find comparable patterns:

grep -R "similar_pattern" src/ --include='*.py'

2. Compare Against References

• If implementing a pattern, read the 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. Rank Falsifiable Hypotheses

• Generate 3–5 plausible causes and rank by likelihood plus cheapness to test.
• State each prediction: "If X causes this, Y should be observable and Z should change."
• Try to disprove the highest-ranked hypothesis before changing production code.

2. Test Minimally

• Use the smallest probe and change one variable at a time.
• Don't fix multiple things at once.

3. Verify Before Continuing

• Did it work? → 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 the user 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
• MUST have before fixing
• Use the test-driven-development skill

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

# Run the specific regression test
pytest tests/test_module.py::test_regression -v

# Run full suite — no regressions
pytest tests/ -q

4. If Fix Doesn't Work — The Rule of Three

• 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 an architectural problem:

• Each fix reveals new shared state/coupling 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?
• Are we "sticking with it through sheer inertia"?
• Should we refactor the architecture vs. continue fixing symptoms?

Discuss with the user before attempting more fixes.

This is NOT a failed hypothesis — this is a wrong architecture.

Framework-Specific Pitfalls

See also references/laravel-filament-pitfalls.md for expanded detail and verification patterns. See also references/mac-dev-environment-fixes.md for macOS dev environment issues (pyenv locks, slow zsh startup).

Filament: Single-Page Resource Route Key

A Filament resource with only a custom Page (no ListRecords) must register it as 'index' in getPages(), not 'view'. Filament navigates to index by default — a view key at path / causes LogicException: does not have an [index] page.

Laravel: Eloquent Relationship Method vs Dynamic Property

A common source of Undefined property errors after "fixing" a missing-method error:

// WRONG — returns BelongsTo relationship object (never null), then ?->id 
// accesses property on the relationship object, not the model
$entityId = $user->entity()?->id;   // Error: Undefined property: BelongsTo::$id

// CORRECT — dynamic property access returns the related model (or null)
$entityId = $user->entity?->id;     // Returns model->id or null

Why this happens: Calling the relationship as a method (entity()) returns the Eloquent relationship instance (BelongsTo, HasMany, etc.). The nullsafe operator ?-> doesn't convert it to the model — it just short-circuits if the left side is null, but a relationship object is never null. Use dynamic property syntax (no parentheses) to get the actual related model.

When this surfaces: Often appears as a two-step error:

1. First error: Call to undefined method User::ifrsEntity() → developer renames to correct method name
2. Second error: Undefined property: BelongsTo::$id → developer used method call syntax with ?->

Phase 3 checkpoint: After any fix that changes a method name on an Eloquent model, verify the call pattern (method vs property) before declaring success.

Language-Specific Debug References

Python Debugging → references/python-debugging.md

pdb REPL quick reference, debugpy remote attach (DAP), remote-pdb for terminal agents, pytest debugging tips, and common pitfalls (xdist, asyncio, ptrace).

Node.js Debugging → references/nodejs-debugging.md

node inspect REPL reference, CDP scripting via chrome-remote-interface, attaching to running processes (SIGUSR1), Vitest debugging, heap snapshots, CPU profiles, and UI debugging patterns.

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 a new problem in a different place

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

If 3+ fixes failed: Question the architecture (Phase 4 step 5).

Common Rationalizations

Quick Reference

Tool Routing

Use available tools, not copied framework names:

• read for complete error/source context.
• bash for tests, repros, git history, and narrow probes.
• symbol_search/module_report for callers and structure when available.
• lsp_navigation for definitions/references when activated.

For bug fixes, pair the process with test-driven-development: RED regression loop → root-cause fix → GREEN verification.