---
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 |
