Systematic Debugging

Systematic Debugging

Overview

Random fixes waste time and create new problems. 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 unexpected outcome:

In software: Test failures, bugs, unexpected behavior, performance problems, build failures, integration issues

In research: Failed replications, unexpected results, null findings, contradictory data, equipment issues, protocol failures

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 issues have root causes too)
• You're in a hurry (systematic is faster than thrashing)
• 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/Outputs Carefully

   • Don't skip past errors, warnings, or anomalies
   • They often contain the exact solution
   • Read stack traces/logs completely
   • Note specifics: line numbers, values, timestamps

2. Reproduce Consistently

   • Can you trigger it reliably?
   • What are the exact steps/conditions?
   • Does it happen every time?
   • If not reproducible → gather more data, don't guess

3. Check Recent Changes

   • What changed that could cause this?
   • Code changes, protocol changes, equipment changes
   • New dependencies, config, reagents, parameters
   • Environmental differences

4. Gather Evidence in Multi-Component Systems

   WHEN system has multiple components:

   For software: CI → build → deploy, API → service → database For research: Sample prep → measurement → analysis, multiple experimental stages

   BEFORE proposing fixes, add diagnostic instrumentation:

   For EACH component boundary:
     - Log/record what data enters component
     - Log/record what data exits component
     - Verify environment/conditions at each layer
     - Check state at each stage
   
   Run once to gather evidence showing WHERE it breaks
   THEN analyze evidence to identify failing component
   THEN investigate that specific component
   

5. Trace Data/Causation Flow

   • Where does the bad value/result originate?
   • What upstream process produced this?
   • Keep tracing backward 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/experiments in same project
   • What works that's similar to what's broken?

2. Compare Against References

   • If implementing pattern/protocol, read reference COMPLETELY
   • Don't skim - read every line/step
   • 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/conditions does this need?
   • What settings, config, environment, reagents?
   • 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 (software) or documented protocol (research)
   • MUST have before fixing

2. Implement Single Fix

   • Address the root cause identified
   • ONE change at a time
   • No "while I'm here" improvements
   • No bundled changes

3. Verify Fix

   • Test passes now? Experiment works?
   • No other tests/results 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/approach (step 5 below)
   • DON'T attempt Fix #4 without fundamental discussion

5. If 3+ Fixes Failed: Question Fundamentals

   Pattern indicating fundamental problem:

   • Each fix reveals new problem in different place
   • Fixes require "massive changes" to implement
   • Each fix creates new symptoms elsewhere

   STOP and question fundamentals:

   • Is this approach fundamentally sound?
   • Are we "sticking with it through sheer inertia"?
   • Should we redesign vs. continue fixing symptoms?

   Discuss before attempting more fixes

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"
• "Reference says X but I'll adapt it differently"
• "Here are the main problems: [lists fixes without investigation]"
• Proposing solutions before tracing causation
• "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.

Common Rationalizations

Quick Reference

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 problems introduced: Near zero vs common