refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조

에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게
[공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류.
문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인).

- Topic_Programming → Domain_Programming (내부 구조 보존)
- Topic_Graphic → Domain_Design
- Topic_Business → Domain_Product
- Topic_General → Domain_General
- _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning),
  Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing)
- 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서)
- 빈 폴더 정리 (memory/procedures)
- 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트)

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

10_Wiki/Topics/Domain_Programming/DevOps_and_Security/Code Stylometry (코드 문체론).md

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+---
+id: wiki-2026-0508-code-stylometry-코드-문체론
+title: Code Stylometry (코드 문체론)
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [Authorship Attribution, Code Fingerprinting, Programmer Identification]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.9
+verification_status: applied
+tags: [security, ml, forensics, privacy]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: Python
+  framework: scikit-learn/transformers
+---
+
+# Code Stylometry (코드 문체론)
+
+## 매 한 줄
+> **"매 코드 작성자를 매 stylistic feature 로 식별하는 ML 기법"**. Caliskan et al. 2015 (USENIX) 가 random forest 로 250 명 중 94% 식별. 매 modern era — CodeBERT/StarCoder embedding 기반 분류기로 매 더 강력해짐. Privacy 위협 (anonymous contributor de-anon) ↔ defensive utility (malware attribution, plagiarism detection) 의 양날.
+
+## 매 핵심
+
+### 매 feature class
+- **Lexical**: identifier naming (camelCase vs snake_case), keyword frequency.
+- **Layout**: indentation, brace style, line length.
+- **Syntactic**: AST node distribution, depth, n-gram of node types.
+- **Idiomatic**: preferred construct (`for` vs `map`, ternary vs if).
+- **Embedding-based**: CodeBERT/StarCoder hidden states (2024+).
+
+### 매 attack scenario
+- De-anonymizing GitHub anonymous account.
+- Linking malware author across samples.
+- Plagiarism detection in coursework.
+- Insider threat attribution.
+
+### 매 응용
+1. Forensic attribution (FBI/Interpol cases).
+2. Academic integrity (MOSS, JPlag).
+3. Bug-injection-source detection (xz-style supply chain).
+
+## 💻 패턴
+
+### Layout features
+```python
+import re
+def layout_features(src: str) -> dict:
+    lines = src.split('\n')
+    return {
+        'avg_line_len': sum(len(l) for l in lines) / max(len(lines), 1),
+        'tab_ratio': sum(l.startswith('\t') for l in lines) / max(len(lines), 1),
+        'blank_ratio': sum(not l.strip() for l in lines) / max(len(lines), 1),
+        'snake_ratio': len(re.findall(r'\b[a-z]+_[a-z]+\b', src)),
+        'camel_ratio': len(re.findall(r'\b[a-z]+[A-Z][a-z]+\b', src)),
+    }
+```
+
+### AST n-gram (Python)
+```python
+import ast
+from collections import Counter
+
+def ast_ngrams(src: str, n=3):
+    tree = ast.parse(src)
+    seq = [type(node).__name__ for node in ast.walk(tree)]
+    return Counter(tuple(seq[i:i+n]) for i in range(len(seq)-n+1))
+```
+
+### Random forest classifier (Caliskan-style)
+```python
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.feature_extraction import DictVectorizer
+
+vec = DictVectorizer(sparse=False)
+X = vec.fit_transform([extract_all_features(s) for s in samples])
+clf = RandomForestClassifier(n_estimators=300, max_depth=20)
+clf.fit(X, authors)
+print(clf.score(X_test, y_test))  # ~90%+ on 100-author corpus
+```
+
+### CodeBERT embedding classifier (2024+)
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+
+tok = AutoTokenizer.from_pretrained('microsoft/codebert-base')
+model = AutoModel.from_pretrained('microsoft/codebert-base').eval()
+
+def embed(src: str) -> torch.Tensor:
+    inp = tok(src, truncation=True, max_length=512, return_tensors='pt')
+    with torch.no_grad():
+        out = model(**inp).last_hidden_state[:, 0]  # CLS
+    return out.squeeze()
+
+# Then train linear classifier on embeddings
+```
+
+### Defensive: code anonymizer
+```python
+# Normalize to defeat stylometry
+import black, autopep8
+def anonymize(src: str) -> str:
+    src = black.format_str(src, mode=black.Mode())  # uniform layout
+    # rename identifiers via AST transform
+    # replace idiosyncratic constructs with canonical form
+    return src
+```
+
+## 매 결정 기준
+| 상황 | Approach |
+|---|---|
+| Small corpus (<50 authors) | RF on hand-crafted features |
+| Large corpus, deep features | CodeBERT/StarCoder embedding + classifier |
+| Defending privacy | Black/Prettier + identifier normalization |
+| Adversarial robust attack | Limited — formatting tools 매 defeat 대부분 |
+| Cross-language | Embedding-based 만 가능 |
+
+**기본값**: 매 RF + AST n-gram 으로 baseline. Embedding 으로 boost.
+
+## 🔗 Graph
+- 부모: [[Authorship Attribution]]
+- 응용: [[Supply Chain Security]]
+- Adjacent: [[Code Obfuscation]] · [[AST]]
+
+## 🤖 LLM 활용
+**언제**: Forensic context, plagiarism check, OSS contributor analysis.
+**언제 X**: Identifying anonymous whistleblower — ethical 매 거부.
+
+## ❌ 안티패턴
+- **Single-feature reliance**: layout 만 → autoformatter 로 매 trivial defeat.
+- **Ignoring base rate**: low base rate = high false positive rate (Bonferroni).
+- **Author-set assumption**: open-world (unknown author) ≠ closed-world.
+- **Privacy ignored**: deploying on anonymous code 매 ethical review 없이.
+
+## 🧪 검증 / 중복
+- Verified (Caliskan USENIX 2015, Abuhamad 2018, CodeBERT papers).
+- 신뢰도 A.
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — stylometry features + RF/CodeBERT pipelines |