systematic-debugging（系统调试）

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 (systematic is faster than thrashing)
• Manager wants it fixed NOW (systematic is faster than guess-and-check)

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

1. Reproduce Consistently

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

1. Check Recent Changes

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

1. Gather Evidence in Multi-Component Systems WHEN system has multiple components: 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
2. 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

Find the pattern before fixing:

1. Find Working Examples

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

1. Compare Against References

- If implementing pattern, read reference implementation COMPLETELY - Don't skim - read every line - Understand the pattern fully before applying

1. Identify Differences

- What's different between working and broken? - List every difference, however small - Don't assume "that can't matter"

1. 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. Form Single Hypothesis

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

1. Test Minimally

- Make the SMALLEST possible change to test hypothesis - One variable at a time - Don't fix multiple things at once

1. Verify Before Continuing

- Did it work? Yes -> Phase 4 - Didn't work? Form NEW hypothesis - DON'T add more fixes on top

1. 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 - One-off test script if no framework - MUST have before fixing - Use the ltk:test-driven-development skill for writing proper failing tests

1. Implement Single Fix

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

1. Verify Fix

- Test passes now? - No other tests broken? - Issue actually resolved?

1. 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 (step 5 below) - DON'T attempt Fix #4 without architectural discussion

1. If 3+ Fixes Failed: Question Architecture Pattern indicating architectural problem: STOP and question fundamentals: Discuss with user before attempting more fixes

- Each fix reveals new shared state/coupling/problem in different place - Fixes require "massive refactoring" to implement - Each fix creates new symptoms elsewhere - Is this pattern fundamentally sound? - Are we "sticking with it through sheer inertia"? - Should we refactor architecture vs. continue fixing symptoms?

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 new problem in different place

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

If 3+ fixes failed: Question the architecture

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 bugs introduced: Near zero vs common