2nd/10_Wiki/Topics/Coding/Quality_Engineering_Excellence.md

---
id: quality-engineering-excellence
title: Engineering Excellence — DORA / SPACE / DX metric
category: Coding
status: draft
source_trust_level: B
verification_status: conceptual
created_at: 2026-05-09
updated_at: 2026-05-09
tags: [quality, productivity, vibe-coding]
tech_stack: { language: "process", applicable_to: ["Engineering"] }
applied_in: []
aliases: [DORA, DORA metrics, SPACE, DX, developer experience, deployment frequency, lead time, MTTR]
---

# Engineering Excellence (DORA / SPACE / DX)

> "Good team 가 무엇 측정?" **DORA 4 가 baseline. SPACE 가 holistic. DX 가 modern**. Vanity metric (LOC, commit count) 안.

## 📖 핵심 개념
- DORA: Delivery + reliability.
- SPACE: 5 dimension (satisfaction, performance, activity, communication, efficiency).
- DX: developer experience.

## 💻 코드 패턴

### DORA 4 metrics
```
1. Deployment Frequency: 매 day vs 매 month.
2. Lead Time for Changes: PR open → prod.
3. Change Failure Rate: deploy 의 % 가 incident.
4. MTTR (Mean Time To Recovery): incident → fix.
```

### Elite vs Low
```
Elite:
- Daily+ deploy.
- < 1 hour lead time.
- < 5% failure.
- < 1 hour MTTR.

Low:
- Monthly deploy.
- 1+ month lead time.
- 16-30% failure.
- 1+ week MTTR.

→ Elite team 가 2x 더 많이 ship + 2x 적은 incident.
```

→ "Accelerate" book (Forsgren).

### Measure (자동)
```yaml
# GitHub
- PR open: timestamp.
- Deploy success: timestamp.
- Lead time = deploy - first commit.

# DataDog / Honeycomb / Sleuth
- Auto-collect.
- Per-team / per-service.
```

### SPACE framework
```
S - Satisfaction
P - Performance (output / outcome)
A - Activity (volume — but careful)
C - Communication / collaboration
E - Efficiency / flow
```

→ Multi-dimensional. Activity 만 = 위험 (게임).

### Activity 의 함정
```
"매 dev 가 매주 X commit"
→ Goodhart's law: target 가 measure 되면 fail.
→ Padding commit, busy work.

→ 측정 = activity but goal = outcome.
```

### DX (Developer Experience)
```
Build time, deploy time, dev env setup, hot reload, test time, dashboard ...

→ "Friction" 을 측정.
DX 가 좋음 = velocity.
```

→ "DX" by Abi Noda (research).

### Build time
```
< 1 min: green.
1-5 min: yellow.
> 5 min: red.

→ Cache + parallel + smaller bundle.
```

### Deploy time
```
< 5 min: elite.
5-30 min: high.
> 30 min: low.

→ CI / CD pipeline 의 dependency.
```

### Local dev setup
```
Onboarding 의 시간:
- < 1 hour: elite.
- < 1 day: good.
- > 1 week: 가짜.

→ Docker compose, devcontainer, automated.
```

### Test time
```
Unit: < 30 sec.
Integration: < 5 min.
E2E: < 15 min.

→ Slow test = skip 자주.
```

### Survey
```
"이 codebase 가 일하기 쉬운가?"
"매 PR 가 review 빠른가?"
"Tooling 가 좋은가?"

→ Quarterly survey + score.
```

### Lighthouse / Web Vitals (UX)
```
LCP: < 2.5s.
FID: < 100ms.
CLS: < 0.1.

→ User experience metric.
```

### SLO (Service Level Objective)
```
99.9% uptime = 4 hour 22 min / month down.
99.95% = 21.6 min.
99.99% = 4.32 min.
99.999% (5 9's) = 26 sec — 매우 어려움.

→ Match SLA / cost.
```

### Incident (MTTR breakdown)
```
Detection time: alert → human aware.
Acknowledgement: human aware → action.
Resolution: action → fix.
Recovery: fix → user happy.

→ 매 phase 측정 + improve.
```

### Postmortem
```
매 P0/P1 incident:
- Timeline.
- Root cause (5 whys).
- Action items (concrete).
- Public (blameless).

→ [[Productivity_Postmortem]].
```

### Code review metric
```
- Time to first review (target < 4 hour).
- Approval count (1-2).
- LOC per PR (< 400).
- Iterations per PR (< 3).

→ Big PR = slow review = lead time ↑.
```

→ "Small PR culture" 가 큰 lever.

### Test coverage
```
60-80% = baseline.
100% = brittle.
0% = scary.

→ Coverage 가 quality 의 proxy 만.
Mutation 가 진짜.
```

### Tech debt 측정
```
- TODO comment count.
- Deprecated API 사용 (Snyk, Sourcegraph).
- 큰 file (> 500 LOC).
- Cyclomatic complexity > 10.

→ "Debt ratio" 가 trend.
```

### CodeClimate / Sonar
```
Auto metric:
- Maintainability.
- Test coverage.
- Duplication.
- Complexity.

→ PR 가 quality gate.
```

### Engineering productivity ≠ output
```
"Velocity = code 작성 ↑" ≠ "Productivity ↑".
Productivity = customer outcome.

→ 매 commit 가 가치 가져야.
"이 PR 가 user 의 무엇 fix?"
```

### Goodhart 의 law
```
"매 dev 가 매주 X commit"
→ Padding.

"Coverage 100%"
→ 가짜 test.

"PR 매주 N 개"
→ Big PR 가 split 가짜.

→ Metric 가 target 가 됨 = 게임.
```

→ Mix of metrics + judgment.

### Real-world
- **Google**: monorepo + DORA + SPACE.
- **Spotify**: tribe model + autonomy metric.
- **Microsoft**: SPACE 의 origin.
- **Pinterest**: DORA + DX.
- **Intuit**: developer survey.

### Dashboard
```
매 team 의:
- Deploy / day.
- Lead time p50.
- Incident MTTR.
- PR cycle time.
- DX score.

→ Trend track.
```

→ Sleuth, LinearB, Code Climate, Faros.

### LinearB / Sleuth (managed)
```
GitHub / GitLab → 자동 metric.
- Cycle time visualization.
- Bottleneck identification.
- Per-team comparison.
```

### Engineering ladder
```
Promotion criteria:
- Junior: complete task.
- Senior: own feature.
- Staff: own system.
- Principal: cross-team impact.

→ Metric + qualitative.
```

### Avoid
```
- "Most active" award (commit count).
- LOC / day target.
- Coverage 100% gate.
- Strict deploy frequency.
- Comparison 가 individual (team OK).
```

### Best practice
```
1. DORA baseline.
2. SPACE survey (quarterly).
3. DX friction (build, deploy, test).
4. Incident postmortem.
5. Mix: metric + survey + judgment.
```

## 🤔 의사결정 기준
| 측정 | Tool |
|---|---|
| DORA | Sleuth / LinearB / GitHub Actions |
| SPACE | Survey + dashboard |
| DX | Build / test time + survey |
| Code quality | Sonar / CodeClimate |
| Test coverage | Codecov |
| Performance | Lighthouse CI / Web Vitals |
| Incident | PagerDuty / Linear |

## ❌ 안티패턴
- **Activity 만**: padding / busy work.
- **LOC measure**: misalign.
- **Individual leaderboard**: morale ↓.
- **100% coverage gate**: brittle test.
- **Strict daily deploy**: 가짜 deploy.
- **Goodhart's law 무시**: metric 가짜.
- **Survey 없음**: blind.

## 🤖 LLM 활용 힌트
- DORA 가 baseline (4 metric).
- SPACE 가 holistic (5 dimension).
- DX 가 modern (friction).
- Goodhart's law 항상 인지.

## 🔗 관련 문서
- [[Productivity_Estimating_Effort]]
- [[Quality_Code_Metrics]]
- [[Productivity_OKR_Goals]]