Systematic debugging

Purpose

Find the root cause of any bug before attempting a fix. Random fixes waste time and create new bugs. Symptom fixes are failure.

Core principle: ALWAYS find root cause before attempting 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

Do not skip when:

• Issue seems simple (simple bugs have root causes too)
• You're in a hurry (rushing guarantees rework)
• Someone wants it fixed immediately (systematic is faster than thrashing)

Inputs

• Bug description or error message.
• Stack trace or reproduction steps.
• Environment context (OS, versions, recent changes).
• Access to the codebase and test suite.

Workflow

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 that could cause this? Git diff, recent commits, new dependencies, config changes, environmental differences.

4. Gather evidence in multi-component systems — When the system has multiple components (CI → build → signing, API → service → database):

   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 to identify the failing component.
   Then investigate that specific component.
   

5. Trace data flow — Where does the bad value originate? What called this with the bad value? Keep tracing up until you find the source. Fix at source, not at symptom. See references/root-cause-tracing.md for the complete backward tracing technique.

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 other components, settings, or config does this need?

Phase 3: Hypothesis and Testing

1. Form a single hypothesis — "I think X is the root cause because Y." Write it down. Be specific.
2. Test minimally — Make the smallest possible change to test the hypothesis. One variable at a time.
3. Verify before continuing — Did it work? Yes → Phase 4. No → form a new hypothesis. Do not add more fixes on top.
4. When you don't know — Say so. Ask for help. Research more. Do not pretend to know.

Phase 4: Implementation

1. Create a failing test case — Simplest possible reproduction. Automated test if possible. Must exist before fixing.

2. Implement a single fix — Address the root cause identified. One change at a time. No "while I'm here" improvements.

3. Verify the fix — Test passes? No other tests broken? Issue actually resolved?

4. If fix doesn't work — STOP. Count how many fixes you've tried.

   • If fewer than 3: return to Phase 1, re-analyze with new information.
   • If 3 or more: question the architecture (see below).

5. If 3+ fixes failed: question the architecture — Each fix revealing new shared state or coupling elsewhere is a sign of an architectural problem, not a hypothesis failure. Stop and discuss with the team before attempting another fix.

Output

Root cause: <what caused the bug — specific, not vague>
Evidence: <what you observed that confirmed the hypothesis>
Fix: <what was changed and where>
Test: <test added or updated>
Verification: <how fix was confirmed>

Verification

• Root cause found (not just symptom location)
• Single hypothesis tested at a time
• Minimal fix applied — no bundled changes
• Test added or updated
• If 3+ fixes failed: architectural discussion initiated, not another fix attempt

Failure Modes

Red flags — STOP and return to Phase 1:

• "Quick fix for now, investigate later"
• "Just try changing X and see if it works"
• Adding multiple changes at once
• "Skip the test, I'll manually verify"
• "It's probably X" without evidence
• "I don't fully understand but this might work"
• "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

Common rationalizations:

Excuse	Reality
"Issue is simple, don't need process"	Simple issues have root causes too. Process is fast for simple bugs.
"Emergency, no time for process"	Systematic debugging is faster than guess-and-check thrashing.
"Just try this first, then investigate"	First fix sets the pattern. Do it right from the start.
"I'll write test after confirming fix works"	Untested fixes don't stick. Test first proves it.
"Multiple fixes at once saves time"	Can't isolate what worked. Causes new bugs.
"One more fix attempt" (after 2+ failures)	3+ failures = architectural problem. Question pattern, don't fix again.

Supporting References

Techniques available in references/:

• root-cause-tracing.md — Trace bugs backward through the call stack to find the original trigger.
• defense-in-depth.md — Add validation at multiple layers after finding the root cause.
• condition-based-waiting.md — Replace arbitrary timeouts with condition polling to fix flaky tests.
• find-polluter.sh — Bisection script to identify which test creates unwanted files or state.

Example implementation in examples/:

• condition-based-waiting-example.ts — Complete TypeScript implementation of condition-based waiting utilities.