2nd/10_Wiki/Topics/AI_and_ML/CodeScene.md

---
id: wiki-2026-0508-codescene
title: CodeScene (Behavioral Code Analysis)
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [CodeScene, behavioral code analysis, hotspot detection, code health, technical debt analytics]
duplicate_of: none
source_trust_level: B
confidence_score: 0.85
verification_status: applied
tags: [code-quality, codescene, behavioral-analysis, hotspot, technical-debt, refactoring, git-history]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: SaaS / on-prem
  framework: CodeScene
---

# CodeScene

## 매 한 줄
> **"매 git history 의 mining 으로 매 hotspot 의 detect"**. 매 정적 분석 X — 매 churn × complexity 의 교차점. 매 refactor priority 의 data-driven. 매 modern 의 alternative: SonarQube + 매 custom git script, Code Climate, Sourcegraph.

## 매 핵심

### 매 차별점 (vs 매 SAST)
- 매 정적 X — 매 behavioral.
- 매 git history 의 6 month+ 필수.
- 매 churn (변경 빈도) × 매 complexity 의 hotspot.
- 매 author pattern.

### 매 핵심 metric

#### Code Health (1-10)
- 매 defect risk + delivery speed + predictability 의 결합.
- 매 6 미만 의 alert.

#### Hotspot
- 매 churn × complexity.
- 매 top 5% 의 매 maintenance burden 의 80%.

#### Knowledge Map
- 매 author 의 dominate.
- 매 bus factor.

#### Coupling
- 매 file 의 매 같이 변경 의 frequency.
- 매 logical coupling.

### 매 limitation
- 매 ≥6 month git history 필요.
- 매 stale code (no churn) 의 weak signal.
- 매 enterprise pricing.
- 매 learning curve.

### 매 alternative

#### Open-source / DIY
- 매 git log + complexity tool.
- 매 lizard, scc.

#### SaaS competitor
- **SonarQube + history**.
- **Code Climate Velocity**.
- **Sourcegraph**.
- **Pluralsight Flow**.
- **LinearB / Axify** (DORA-focused).

### 매 응용
1. **Refactor priority**: 매 hotspot 의 first.
2. **Onboarding**: 매 high-churn area 의 explain.
3. **Architecture review**: 매 coupling 의 inspect.
4. **Risk forecast**: 매 incident-prone area.
5. **Bus factor**: 매 lone owner area.

## 💻 패턴

### Hotspot detection (DIY)
```python
import subprocess
from collections import Counter
import lizard  # 매 complexity

def hotspots(repo, since='6 months ago', top_n=20):
    # 매 1. churn (commit count per file)
    log = subprocess.check_output(
        f'git -C {repo} log --since="{since}" --name-only --pretty=format:',
        shell=True, text=True,
    )
    churn = Counter(f for f in log.strip().split('\n') if f and f.endswith(('.py', '.ts', '.js')))
    
    # 매 2. complexity (cyclomatic)
    complexity = {}
    for path, count in churn.items():
        try:
            ll = lizard.analyze_file(f'{repo}/{path}')
            complexity[path] = sum(f.cyclomatic_complexity for f in ll.function_list)
        except: pass
    
    # 매 3. hotspot = churn × complexity
    scored = [(f, churn[f] * complexity.get(f, 0)) for f in churn]
    return sorted(scored, key=lambda x: -x[1])[:top_n]
```

### Knowledge map (bus factor)
```python
def knowledge_map(repo, since='1 year ago'):
    log = subprocess.check_output(
        f'git -C {repo} log --since="{since}" --pretty=format:%an --name-only',
        shell=True, text=True,
    )
    
    file_authors = {}  # 매 file → Counter(author → lines)
    current_author = None
    for line in log.split('\n'):
        if line and not line.endswith(('.py', '.ts', '.js')):
            current_author = line
        elif line:
            file_authors.setdefault(line, Counter())[current_author] += 1
    
    # 매 bus factor: 매 1 author 의 80%+ 의 file
    risk_files = []
    for f, authors in file_authors.items():
        total = sum(authors.values())
        top = authors.most_common(1)[0]
        if top[1] / total > 0.8:
            risk_files.append((f, top[0], top[1] / total))
    
    return risk_files
```

### Logical coupling (changed-together)
```python
def logical_coupling(repo, since='6 months ago'):
    log = subprocess.check_output(
        f'git -C {repo} log --since="{since}" --pretty=format:COMMIT --name-only',
        shell=True, text=True,
    )
    
    commits = log.split('COMMIT')
    coupling = Counter()
    for c in commits:
        files = [l.strip() for l in c.split('\n') if l.strip().endswith(('.py', '.ts'))]
        for i, a in enumerate(files):
            for b in files[i+1:]:
                coupling[tuple(sorted([a, b]))] += 1
    
    return sorted(coupling.items(), key=lambda x: -x[1])[:20]
```

### Code health score (proxy)
```python
def health_score(file_path):
    """매 simple proxy of CodeScene-style."""
    score = 10
    
    ll = lizard.analyze_file(file_path)
    avg_cc = np.mean([f.cyclomatic_complexity for f in ll.function_list])
    if avg_cc > 10: score -= 2
    if avg_cc > 20: score -= 2
    
    line_count = ll.nloc
    if line_count > 500: score -= 1
    if line_count > 1000: score -= 2
    
    longest_func = max((f.length for f in ll.function_list), default=0)
    if longest_func > 50: score -= 1
    if longest_func > 100: score -= 2
    
    return max(1, score)
```

### CI integration (CodeScene API)
```yaml
# .github/workflows/codescene.yml
- name: CodeScene Delta Analysis
  uses: empear-analytics/codescene-pr-check@v1
  with:
    api-url: ${{ secrets.CODESCENE_API_URL }}
    api-user: ${{ secrets.CODESCENE_USER }}
    api-token: ${{ secrets.CODESCENE_TOKEN }}
    project-id: 'my-project'
    fail-on-decline: true  # 매 health 의 drop 시 의 fail
```

### Quality gate
```python
def quality_gate(pr_files):
    """매 PR 의 health 의 6 미만 시 의 fail."""
    failures = []
    for f in pr_files:
        score = health_score(f)
        if score < 6:
            failures.append((f, score))
    
    if failures:
        return f'Quality gate failed: {failures}'
    return 'OK'
```

### Refactor priority dashboard
```python
def refactor_dashboard(repo):
    return {
        'hotspots': hotspots(repo)[:10],
        'bus_factor_risks': knowledge_map(repo),
        'high_coupling': logical_coupling(repo)[:5],
        'health_distribution': {
            'critical': count_files_below(repo, 4),
            'concerning': count_files_in_range(repo, 4, 6),
            'ok': count_files_above(repo, 6),
        },
    }
```

## 🤔 결정 기준
| 상황 | Tool |
|---|---|
| Enterprise + budget | CodeScene SaaS |
| OSS / DIY | git script + lizard + custom dashboard |
| DORA + general | LinearB / Axify |
| Code search + ownership | Sourcegraph |
| Quality gate (PR) | SonarQube + custom |
| Bus factor only | git blame + script |

**기본값**: 매 small / OSS = 매 DIY. 매 enterprise = CodeScene 또는 LinearB.

## 🔗 Graph
- 부모: [[Code-Quality]] · [[Refactoring]] · [[Engineering-Productivity]]
- 변형: [[Behavioral-Code-Analysis]] · [[Hotspot-Detection]] · [[Code-Health]]
- 응용: [[Bus-Factor]] · [[Logical-Coupling]] · [[Quality-Gate]]
- Adjacent: [[Code_Smells]] · [[Quality_Code_Review_Modern]] · [[Axify]] · [[SonarQube]] · [[Architecture-Anti-Patterns]]

## 🤖 LLM 활용
**언제**: 매 refactor priority. 매 onboarding. 매 architecture review. 매 incident prevention.
**언제 X**: 매 < 6 month repo. 매 stale codebase analysis.

## ❌ 안티패턴
- **모든 hotspot 의 fix**: 매 priority 의 use.
- **Static analysis 의 ignore**: 매 churn-less issue 의 miss.
- **Git history 의 trust 100%**: 매 squash / rebase 의 noise.
- **Bus factor 의 ignore**: 매 critical risk.
- **No quality gate**: 매 silent decline.

## 🧪 검증 / 중복
- Verified (Adam Tornhill "Your Code as a Crime Scene", CodeScene docs).
- 신뢰도 B.
- Related: [[Code_Smells]] · [[Quality_Code_Review_Modern]] · [[Axify]] · [[Architecture-Anti-Patterns]] · [[Asset-Specific-Knowledge]].

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — behavioral analysis + hotspot + 매 DIY git script + CI integration |