Systematic Debugging

The Iron Law

NO FIXES WITHOUT ROOT CAUSE INVESTIGATION FIRST

If you haven't completed Phase 1, you cannot propose fixes.

Violating the letter of this process is violating the spirit of debugging.

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

1. Reproduce consistently

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

1. Check recent changes

• Git diff, recent commits, new dependencies, config changes
• Environmental differences

1. Gather evidence in multi-component systems

For EACH component boundary:

• Log what data enters component
• Log what data exits component
• Verify environment/config propagation

Run once to gather evidence showing WHERE it breaks, THEN analyze.

1. 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 in same codebase
2. Compare against references — read reference implementation COMPLETELY (don't skim)
3. Identify ALL differences between working and broken
4. Understand dependencies, settings, config, assumptions

Phase 3: Hypothesis and Testing

1. Form SINGLE hypothesis: "I think X is root cause because Y"
2. Test minimally — SMALLEST possible change, one variable at a time
3. Did it work? → Phase 4. Didn't work? → NEW hypothesis (don't pile fixes)

Phase 4: Implementation

1. Create failing test case — use /tdd skill. MUST have before fixing.
2. Implement single fix — ONE change, no "while I'm here" improvements
3. Verify fix — test passes, no other tests broken, issue resolved
4. If 3+ fixes failed: STOP. Question the architecture. Discuss with user before more attempts. This is NOT a failed hypothesis — this is a wrong architecture.

Defense-in-Depth

When you fix a bug, validate at EVERY layer data passes through:

• Layer 1: Entry point validation (reject invalid input at API boundary)
• Layer 2: Business logic validation (ensure data makes sense for operation)
• Layer 3: Environment guards (prevent dangerous operations in specific contexts)
• Layer 4: Debug instrumentation (capture context for forensics)

Single validation: "We fixed the bug." Multiple layers: "We made the bug impossible."

Condition-Based Waiting (Flaky Tests)

// ❌ Guessing at timing
await new Promise(r => setTimeout(r, 50));

// ✅ Waiting for actual condition
await waitFor(() => getResult() !== undefined);

Wait for the actual condition, not a guess about how long it takes.

Generic polling function:

async function waitFor<T>(
  condition: () => T | undefined | null | false,
  description: string,
  timeoutMs = 5000
): Promise<T> {
  const startTime = Date.now();
  while (true) {
    const result = condition();
    if (result) return result;
    if (Date.now() - startTime > timeoutMs) {
      throw new Error(`Timeout waiting for ${description} after ${timeoutMs}ms`);
    }
    await new Promise(r => setTimeout(r, 10));
  }
}

Root Cause Tracing

When a bug manifests deep in the call stack:

1. Observe the symptom — what error, where?
2. Find immediate cause — what code directly causes this?
3. Ask: what called this? — trace one level up
4. Keep tracing up — what value was passed? Where did it come from?
5. Find original trigger — the first point where bad data entered

NEVER fix just where the error appears. Trace back to the original trigger.

If you can't trace manually, add instrumentation:

const stack = new Error().stack;
console.error('DEBUG operation:', { directory, cwd: process.cwd(), stack });

Use console.error() in tests (not logger — may be suppressed).

Red Flags — STOP and Return to Phase 1

• "Quick fix for now, investigate later"
• "Just try changing X and see"
• "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 new problem in different place

Common Rationalizations