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

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
   • •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

   • •What changed? git diff, recent commits
   • •New dependencies, config changes
   • •Environmental differences
4. •

   Gather Evidence in Multi-Component Systems

   WHEN system has multiple components:

   Add diagnostic instrumentation at each component boundary:

   bash

   # Go: Add temporary log statements at boundaries
   log.Printf("=== Handler input: %+v", req)
   log.Printf("=== Service output: %+v", result)
   
   # Python: Similar boundary logging
   print(f"=== Input: {data!r}")
   print(f"=== Output: {result!r}")
   

   Run once to gather evidence showing WHERE it breaks, THEN investigate that component.

5. •

   Trace Data Flow

   • •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

1. •Find Working Examples — Locate similar working code in same codebase
2. •Compare Against References — Read reference implementation COMPLETELY, don't skim
3. •Identify Differences — List every difference, however small
4. •Understand Dependencies — What other components, settings, config does this need?

Phase 3: Hypothesis and Testing

1. •Form Single Hypothesis — "I think X is the root cause because Y"
2. •Test Minimally — Smallest possible change, one variable at a time
3. •Verify Before Continuing — Didn't work? Form 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 — Use the test-driven-development skill

2. •

   Implement Single Fix — ONE change at a time. No "while I'm here" improvements.

3. •

   Verify Fix — Test passes? No other tests broken?

4. •

   If 3+ Fixes Failed: Question Architecture

   Pattern indicating architectural problem:

   • •Each fix reveals new shared state/coupling
   • •Fixes require "massive refactoring"
   • •Each fix creates new symptoms elsewhere

   STOP and discuss with the user before attempting more fixes.

Common Rationalizations

Red Flags - STOP and Follow Process

• •"Quick fix for now, investigate later"
• •"Just try changing X and see"
• •"Add multiple changes, run tests"
• •"It's probably X, let me fix that"
• •"I don't fully understand but this might work"
• •Proposing solutions before tracing data flow
• •"One more fix attempt" (when already tried 2+)

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

Quick Reference