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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Antigravity Agent
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---
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id: wiki-2026-0508-software-architecture-recovery
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title: Software Architecture Recovery
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Architecture Recovery, Reverse Architecting]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.85
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verification_status: applied
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tags: [architecture, reverse-engineering, legacy, static-analysis]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: python
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framework: networkx
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---
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# Software Architecture Recovery
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## 매 한 줄
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> **"매 source code → 매 architectural model 의 inference"**. Documentation 의 lost / outdated 의 legacy system 의 understanding. 2026 현재 매 LLM (Claude Opus 4.7, GPT-5) 의 augmented static-analysis 가 매 dominant — 매 dependency graph + cluster + LLM-named module summary.
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## 매 핵심
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### 매 phases
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1. **Extraction**: 매 source code, build files, config 의 parse → entities (file, class, module).
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2. **Abstraction**: 매 dependency graph, call graph, data-flow.
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3. **Clustering**: 매 community detection (Louvain, label propagation), 매 LLM semantic grouping.
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4. **Presentation**: C4 diagram, dependency matrix, ADR.
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### 매 techniques
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- **Static**: AST parse, import graph (madge, jdeps, pyan).
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- **Dynamic**: trace logs, profilers, distributed tracing (OTel).
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- **Hybrid**: 매 static + runtime call data merge.
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- **LLM-augmented**: 매 module 별 README/code → 매 LLM summary, 매 architecture description.
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### 매 응용
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1. Legacy modernization assessment.
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2. Microservice decomposition planning.
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3. Onboarding new engineers.
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## 💻 패턴
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### Python — import graph 의 추출
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```python
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import ast, os, networkx as nx
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G = nx.DiGraph()
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for root, _, files in os.walk("src"):
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for f in files:
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if not f.endswith(".py"): continue
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path = os.path.join(root, f)
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tree = ast.parse(open(path).read())
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mod = path.replace("/", ".").removesuffix(".py")
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for node in ast.walk(tree):
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if isinstance(node, ast.ImportFrom) and node.module:
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G.add_edge(mod, node.module)
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```
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### JavaScript — madge dependency graph
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```bash
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npx madge --image graph.svg --extensions ts,tsx src/
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npx madge --circular src/ # detect cycles
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```
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### Java — jdeps + GraalVM
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```bash
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jdeps -verbose:class -recursive app.jar > deps.txt
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jdeps --inverse --package com.acme.payment app.jar
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```
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### Community detection (Louvain)
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```python
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import networkx as nx
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from networkx.algorithms.community import louvain_communities
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modules = louvain_communities(G.to_undirected(), resolution=1.2, seed=42)
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for i, m in enumerate(modules):
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print(f"Module {i}: {sorted(m)[:5]}...")
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```
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### LLM-augmented module naming (Claude Opus 4.7)
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```python
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from anthropic import Anthropic
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client = Anthropic()
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def name_module(files: list[str], code_snippets: list[str]) -> str:
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msg = client.messages.create(
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model="claude-opus-4-7",
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max_tokens=200,
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messages=[{"role": "user", "content":
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f"Files: {files}\n\nSnippets:\n{code_snippets}\n\n"
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"Give a 3-word module name + 1-line responsibility."}],
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)
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return msg.content[0].text
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```
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### Runtime trace → architecture (OpenTelemetry)
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```python
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# Aggregate spans into service-level call graph
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from collections import Counter
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edges = Counter()
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for span in fetch_traces(service="checkout", since="24h"):
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if span.parent and span.parent.service != span.service:
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edges[(span.parent.service, span.service)] += 1
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# Top edges = primary architectural connections
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```
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### C4 diagram emission (Structurizr DSL)
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```dsl
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workspace {
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model {
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user = person "Customer"
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sys = softwareSystem "Shop" {
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web = container "Web"
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api = container "API"
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db = container "Postgres"
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}
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user -> web "uses"
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web -> api "REST"
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api -> db "JDBC"
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}
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}
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| Small monolith (<100k LoC) | Static import graph + manual review |
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| Microservices distributed | Distributed tracing (OTel) + service map |
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| Legacy COBOL/Java enterprise | Lattix / Structure101 commercial tools |
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| Quick high-level overview | LLM (Opus 4.7) on README + top-level dirs |
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| Decomposition planning | Static + dynamic + LLM hybrid |
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**기본값**: 매 static import graph (madge / pyan / jdeps) → Louvain cluster → LLM name → C4 diagram.
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## 🔗 Graph
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- 부모: [[Software Architecture]]
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- 응용: [[Legacy Modernization]]
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- Adjacent: [[C4 Model (Architecture Documentation)]] · [[Dependency Analysis]] · [[Static Analysis]]
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## 🤖 LLM 활용
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**언제**: 매 undocumented codebase 의 onboarding, 매 modernization plan, 매 dependency cycle 의 detect.
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**언제 X**: 매 well-documented current arch — 매 ADR 의 read 의 충분.
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## ❌ 안티패턴
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- **Recovered = correct**: 매 inferred architecture 는 매 historical, 매 ideal X. Validate with team.
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- **Static only for distributed system**: 매 runtime topology 의 lost.
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- **LLM hallucination**: 매 module name 의 plausible 의 X-correct. 매 verify.
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## 🧪 검증 / 중복
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- Verified (Garlan & Schmerl SAR research, 2002–2024; SEI architecture reconstruction guides).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — recovery techniques with LLM-augmented analysis |
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