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>
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+---
+id: wiki-2026-0508-program-dependence-graph
+title: Program Dependence Graph
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [PDG, Program Dependence Graph, 프로그램 의존성 그래프]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.9
+verification_status: applied
+tags: [compiler, static-analysis, ir, slicing]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: compiler-ir
+  framework: static-analysis
+---
+
+# Program Dependence Graph
+
+## 매 한 줄
+> **"매 control + data dependence를 매 한 그래프로"**. Program Dependence Graph (PDG)는 Ferrante, Ottenstein, Warren (1987) 이 매 제안한 매 IR — 매 statement node 사이에 매 control dependence edge와 매 data dependence edge를 매 함께 표현. Program slicing, parallelization, change impact analysis의 매 backbone.
+
+## 매 핵심
+
+### 매 두 종류 edge
+- **매 Data dependence**: 매 statement A가 매 정의한 var를 매 B가 매 사용 → A → B (def-use).
+- **매 Control dependence**: 매 A의 매 결과가 매 B의 매 실행 여부를 매 결정 → A → B.
+- **매 Region node**: 매 unconditional block 매 그룹화 (선택적).
+
+### 매 CDG vs DDG vs PDG
+- **CDG**: control dependence만 (post-dominator frontier 기반).
+- **DDG**: data dependence만 (def-use chain).
+- **PDG**: 매 둘 모두를 매 single graph로.
+- **SDG (System DG)**: PDG + interprocedural call/parameter edge.
+
+### 매 응용
+1. Program slicing (Weiser 1981 + Horwitz et al. 1990).
+2. Change impact analysis.
+3. Loop parallelization (data dep 없으면 매 parallel-safe).
+4. Code clone detection (subgraph isomorphism).
+5. Differential testing / fuzzing.
+
+## 💻 패턴
+
+### PDG 구축 sketch (Python AST)
+```python
+import ast
+from collections import defaultdict
+
+class PDGBuilder(ast.NodeVisitor):
+    def __init__(self):
+        self.defs = defaultdict(list)   # var -> [stmt_id]
+        self.data_edges = []
+        self.ctrl_edges = []
+
+    def visit_Assign(self, node):
+        sid = id(node)
+        for n in ast.walk(node.value):
+            if isinstance(n, ast.Name):
+                for prev in self.defs[n.id]:
+                    self.data_edges.append((prev, sid))
+        for tgt in node.targets:
+            if isinstance(tgt, ast.Name):
+                self.defs[tgt.id].append(sid)
+        self.generic_visit(node)
+
+    def visit_If(self, node):
+        sid = id(node)
+        for s in node.body + node.orelse:
+            self.ctrl_edges.append((sid, id(s)))
+        self.generic_visit(node)
+```
+
+### Backward slicing
+```python
+def backward_slice(pdg, criterion: int) -> set[int]:
+    # criterion = stmt_id; 매 reachable predecessors via data + ctrl edges
+    reverse = defaultdict(list)
+    for u, v in pdg.data_edges + pdg.ctrl_edges:
+        reverse[v].append(u)
+    seen, stack = set(), [criterion]
+    while stack:
+        n = stack.pop()
+        if n in seen: continue
+        seen.add(n)
+        stack.extend(reverse[n])
+    return seen
+```
+
+### Loop parallelization check
+```python
+def is_parallelizable(loop_pdg) -> bool:
+    # 매 No loop-carried data dependence
+    for u, v in loop_pdg.data_edges:
+        if loop_pdg.iter_distance(u, v) > 0:
+            return False
+    return True
+```
+
+### LLVM via opt pass
+```bash
+# LLVM 18+ — print PDG of a function
+opt -passes='print<dependence-analysis>' -disable-output input.ll
+opt -passes='print<scalar-evolution>'    -disable-output input.ll
+```
+
+### Tree-sitter + custom analyzer (modern stack)
+```python
+import tree_sitter_python as tsp
+from tree_sitter import Language, Parser
+LANG = Language(tsp.language())
+parser = Parser(LANG)
+tree = parser.parse(b"x = 1\ny = x + 2")
+# 매 walk tree, 매 build PDG with same edges as above
+```
+
+## 매 결정 기준
+| 상황 | Approach |
+|---|---|
+| Slicing / debugging aid | PDG (data + control) |
+| Loop opt only | DDG (loop-carried 매 충분) |
+| Cross-function impact | SDG (PDG + summary edges) |
+| Code clone detection | PDG subgraph isomorphism |
+
+**기본값**: 매 PDG 시작, 매 cross-function 필요 시 매 SDG로 확장.
+
+## 🔗 Graph
+- 부모: [[Static-Analysis]]
+
+## 🤖 LLM 활용
+**언제**: 매 code understanding tool, 매 refactoring impact, 매 LLM-assisted slicing.
+**언제 X**: 매 trivial single-function script.
+
+## ❌ 안티패턴
+- **매 Pointer aliasing 무시**: 매 may-alias 매 conservative 처리 안 하면 매 unsound.
+- **매 Interprocedural skip**: 매 cross-function dep 매 결측 → 매 false negative.
+- **매 매 edge 폭주**: 매 every var 매 every stmt → 매 PDG 매 dense 매 unreadable.
+
+## 🧪 검증 / 중복
+- Verified (Ferrante/Ottenstein/Warren TOPLAS 1987, Horwitz et al. TOPLAS 1990).
+- 신뢰도 A.
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — PDG/CDG/DDG/SDG taxonomy + slicing impl |