2nd/10_Wiki/Topics/AI_and_ML/Science of Failure.md

---
id: wiki-2026-0508-science-of-failure
title: Science of Failure
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Failure Science, Postmortem Culture, Learning from Failure]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [reliability, postmortem, sre, chaos-engineering, learning-org]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: english
  framework: SRE
---

# Science of Failure

## 매 한 줄
> **"매 failure 는 system 의 information signal — 매 blame 의 X, 매 learning 의 O"**. 매 origin 은 1979 Three Mile Island 와 NASA Challenger postmortem culture; 매 modern state 는 Google SRE blameless postmortem, Netflix Chaos Monkey, Honeycomb observability + AI-aided incident review (Claude Opus 4.7 transcript summarization).

## 매 핵심

### 매 failure 의 분류 (Westrum 1988 → 매 현대 적용)
- **Pathological**: 매 messenger shoot, 매 hide failure → 매 pre-mortem culture.
- **Bureaucratic**: 매 narrow responsibility, 매 novelty crush.
- **Generative**: 매 high cooperation, 매 inquiry, 매 messenger trained — 매 Google/Netflix 의 target.

### 매 blameless postmortem 의 5 components
- **Timeline**: UTC, 매 minute precision.
- **Impact**: user-facing metric (RPS, error budget burn).
- **Root cause**: 매 5 whys + contributing factors.
- **Action items**: owner + due date.
- **Lessons**: 매 process change, 매 not individual blame.

### 매 응용
1. SRE error budget — 매 SLO violation 시 launch freeze.
2. Chaos engineering — 매 prod fault injection 으로 latent failure surface.
3. Pre-mortem — 매 launch 전 "matrix this failed, why?".
4. Game days — 매 quarterly disaster sim.

## 💻 패턴

### 매 blameless postmortem template (Markdown)
```markdown
# Incident: <name> (YYYY-MM-DD)

**Severity**: SEV-2
**Duration**: 47 min (14:03–14:50 UTC)
**Impact**: 12% of /api/v2 requests 5xx
**On-call**: @alice (commander), @bob (comms)

## Timeline (UTC)
- 14:03 — deploy v2.41.0 to prod
- 14:05 — error rate alarm fires (PagerDuty)
- 14:12 — rollback initiated
- 14:50 — error rate normal

## Root cause
DB migration added NOT NULL on `users.email` w/o backfill.
Old code paths (canary not yet drained) wrote NULL → constraint violation.

## Contributing factors
- Migration runner did not block on canary drain (process gap)
- Schema diff review missed NOT NULL implication (review gap)

## Action items
- [ ] @alice — migration runner: enforce canary-drain gate (P0, 2026-05-17)
- [ ] @bob — schema-diff bot: flag NOT NULL on existing column (P1, 2026-05-24)

## What went well
- Rollback under 10 min (rollback runbook v3 worked)
- On-call comms was fast

## What did not
- Canary drain assumption was tribal knowledge

## Lessons
Migration-runner gate is the structural fix.
Not "alice should have known" — process is the fix.
```

### 매 5-whys (chained, 매 not individual blame)
```text
Why 5xx? → DB constraint violation
Why violation? → NULL written to NOT NULL col
Why NULL? → old canary still running old code
Why canary running? → migration ran w/o waiting for canary drain
Why no wait? → migration runner has no canary-state hook
→ FIX: migration runner must check canary state
```

### 매 chaos monkey (매 Litmus / Chaos Mesh, K8s native, 2026)
```yaml
apiVersion: chaos-mesh.org/v1alpha1
kind: PodChaos
metadata:
  name: kill-payments-pod-randomly
spec:
  action: pod-kill
  mode: one
  selector:
    namespaces: [payments]
    labelSelectors:
      app: payments-api
  scheduler:
    cron: "@every 30m"  # 매 prod hour 동, 매 random pod kill
```

### 매 error budget burn alert (Google SRE, multi-window)
```yaml
# 매 fast burn (1h window, 14.4x rate) + slow burn (6h, 6x) — 2-window
- alert: SLOFastBurn
  expr: |
    (1 - sum(rate(http_requests_success[1h])) / sum(rate(http_requests_total[1h])))
    > (1 - 0.999) * 14.4
  labels: { severity: page }
  annotations: { summary: "Burning SLO 14.4x — page on-call" }

- alert: SLOSlowBurn
  expr: |
    (1 - sum(rate(http_requests_success[6h])) / sum(rate(http_requests_total[6h])))
    > (1 - 0.999) * 6
  labels: { severity: ticket }
```

### 매 pre-mortem prompt (매 team session)
```text
"매 6개월 후 — 매 launch 가 catastrophic failure.
매 NYTimes headline 이 'Company X loses $100M'.
매 어떻게 그 일이 일어났을지 — 매 5 most likely scenarios 작성."

→ 매 pre-mortem 은 cognitive bias (overconfidence) 회피, 매 risk 표면화.
```

### 매 incident summarizer (Claude Opus 4.7, transcript → postmortem draft)
```python
import anthropic
client = anthropic.Anthropic()

slack_log = open("incident-2026-05-09.log").read()
msg = client.messages.create(
    model="claude-opus-4-7",
    max_tokens=4096,
    system=(
        "You are an SRE writing a blameless postmortem. "
        "Extract: timeline (UTC), impact, root cause (5 whys), "
        "contributing factors, action items. Never name-blame; "
        "frame failures as process gaps."
    ),
    messages=[{"role": "user", "content": slack_log}],
)
print(msg.content[0].text)
```

## 매 결정 기준
| 상황 | Approach |
|---|---|
| 매 SEV-1 user-impacting | full blameless postmortem (24h SLA) |
| 매 SEV-3 internal-only | lightweight 5-whys (1 page) |
| 매 near-miss (no impact) | "near-miss log" — 매 still learn |
| 매 individual error pattern | 매 process gap 분석 (매 PIP X) |

**기본값**: 매 SEV-2+ → blameless postmortem with action items + owners.

## 🔗 Graph
- 부모: [[SRE]]
- 변형: [[Chaos Engineering]]
- 응용: [[Postmortem]]

## 🤖 LLM 활용
**언제**: 매 Slack/PagerDuty transcript → postmortem first draft (Claude Opus 4.7 1M ctx 으로 매 long incident 통째로). 매 5-whys facilitation.
**언제 X**: 매 root cause 의 final attribution — 매 human judgment 필요. 매 LLM 의 "blame" hallucination 위험.

## ❌ 안티패턴
- **Blame culture**: 매 "who screwed up?" → 매 hide future failure.
- **Action-item theater**: 매 owner X, due date X → 매 never done.
- **Single root cause**: 매 real failure 는 multi-factor — 매 swiss-cheese model.
- **Postmortem-as-punishment**: 매 PIP 와 결합 → 매 honesty 죽음.

## 🧪 검증 / 중복
- Verified (Google SRE Book Ch.15, Westrum 1988, Sidney Dekker "Field Guide to Understanding Human Error").
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — blameless postmortem + chaos eng + LLM-aided draft |