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Antigravity Agent
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id: wiki-2026-0508-program-comprehension-strategies
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title: Program Comprehension Strategies
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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: [Code Reading, Code Comprehension, Mental Models of Code]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [software-engineering, cognition, code-review, onboarding]
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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: multi
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framework: cognitive-software-engineering
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---
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# Program Comprehension Strategies
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## 매 한 줄
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> **"매 90% of programming is reading code, not writing it."**. 매 Brooks (1983), Pennington (1987), Soloway (1986) 의 cognitive software engineering 연구에서 출발한 매 분야 — 매 code → mental model 변환의 strategies. 매 2026 LLM 시대에는 매 Cursor/Claude Code 의 contextual indexing 이 매 human comprehension 을 augment.
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## 매 핵심
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### 매 3대 strategy
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- **Top-down (Brooks)**: 매 hypothesis 형성 → code 로 verify. 매 domain expert 가 사용.
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- **Bottom-up (Pennington)**: 매 statement → control-flow → data-flow → program model. 매 novice 가 사용.
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- **Opportunistic (mixed)**: 매 expert programmer 의 실제 행동 — top-down 시작, beacon 발견 시 bottom-up 으로 dive.
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### 매 cognitive constructs
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- **Beacons**: 매 recognizable patterns (e.g., `for(i=0; i<n; i++)` → 매 loop, `swap(a,b)` → 매 sort).
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- **Plans**: 매 stereotypical solutions (e.g., search plan, accumulator plan).
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- **Chunks**: 매 functionally cohesive code groups stored as 1 unit in working memory.
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### 매 응용
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1. Code review: 매 reviewer 는 top-down — PR 의 의도 파악 후 specific changes 검증.
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2. Onboarding: 매 new dev 는 bottom-up — small fixes 로 시작, 점진적 chunking.
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3. AI-assisted reading: 매 LLM 에게 code summarization → human 이 hypothesis 생성.
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## 💻 패턴
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### Pattern 1: Top-down hypothesis-driven reading
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```python
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# 매 step 1: read README / docstring → 매 form hypothesis
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# 매 step 2: locate entry point (main, app.py)
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# 매 step 3: trace only the path that confirms/refutes hypothesis
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def trace_hypothesis(repo, hypothesis):
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entry = find_entry_point(repo)
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call_graph = build_call_graph(entry)
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relevant = filter_by_keyword(call_graph, hypothesis.keywords)
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return relevant
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```
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### Pattern 2: Beacon recognition (LLM-augmented)
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```python
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import anthropic
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client = anthropic.Anthropic()
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def extract_beacons(code: str) -> list[str]:
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resp = client.messages.create(
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model="claude-opus-4-7",
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max_tokens=1024,
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system="Identify recognizable code patterns (beacons) and name their plan.",
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messages=[{"role": "user", "content": f"```\n{code}\n```"}],
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)
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return parse_beacons(resp.content[0].text)
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```
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### Pattern 3: Chunking via cohesion analysis
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```python
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def chunk_function(ast_node):
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"""매 group statements by 매 shared variables (cohesion)."""
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chunks, current = [], []
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last_vars = set()
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for stmt in ast_node.body:
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vars = extract_vars(stmt)
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if last_vars and not (vars & last_vars):
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chunks.append(current)
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current = []
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current.append(stmt)
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last_vars = vars
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if current:
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chunks.append(current)
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return chunks
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```
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### Pattern 4: Cross-reference walking (bottom-up)
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```bash
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# 매 ripgrep + ctags-driven exploration
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rg -l "AuthService" --type ts | head -5
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rg "class AuthService" --type ts -A 30
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rg "new AuthService\(" --type ts # 매 callers
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```
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### Pattern 5: LLM-driven code summarization
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```python
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# 매 Claude Code-style structured summary
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PROMPT = """Summarize this file with:
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1. 매 PURPOSE (1 sentence)
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2. 매 KEY DATA STRUCTURES
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3. 매 PUBLIC API
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4. 매 NON-OBVIOUS CONTRACTS / INVARIANTS
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5. 매 DEPENDENCIES (incoming + outgoing)
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"""
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def summarize(file_path):
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code = open(file_path).read()
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return claude_call(PROMPT + f"\n```\n{code}\n```")
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```
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### Pattern 6: Mental model checkpointing
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```markdown
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<!-- 매 personal-notes.md per repo while reading -->
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## Module: auth/
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- Purpose: JWT issuance & verification
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- Entry: `auth/router.ts:loginHandler`
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- Key invariant: tokens always include `iss=our-domain`
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- Open Q: where is refresh-token rotation?
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```
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### Pattern 7: Diagram-first — produce dependency graph before reading
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```bash
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madge --image deps.svg src/
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# 매 visual chunking — see clusters before diving
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```
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## 매 결정 기준
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| 상황 | Strategy |
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|---|---|
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| 매 domain familiar, code new | Top-down |
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| 매 domain new, code small | Bottom-up |
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| 매 large unknown codebase | Opportunistic + diagram first |
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| 매 bug hunt | Bottom-up from stack trace |
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| 매 architecture review | Top-down from entry points |
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| 매 LLM augmentation | Summarize → form hypothesis → verify |
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**기본값**: 매 opportunistic — 매 README + entry point 부터 시작, beacon 발견 시 dive.
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## 🔗 Graph
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- 부모: [[Cognitive Psychology]]
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- 변형: [[Code Review]] · [[Onboarding]]
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- 응용: [[Refactoring_Best_Practices|Refactoring]]
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- Adjacent: [[Mental_Models|Mental Models]] · [[Working Memory]] · [[AST]]
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## 🤖 LLM 활용
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**언제**: 매 onboarding new repo, 매 reviewing large PR, 매 understanding legacy code, 매 building mental model.
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**언제 X**: 매 1-line hot-fix 에 over-engineering 하지 마라.
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## ❌ 안티패턴
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- **Read everything linearly**: 매 working memory 초과 — chunking 없이 무너짐.
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- **Skip the README**: 매 hypothesis 없이 bottom-up 만 → 매 lost in details.
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- **No checkpointing**: 매 1시간 후 모두 잊음 — write down mental model.
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- **Trust LLM summary blindly**: 매 hallucination 위험 — 매 verify on key claims.
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## 🧪 검증 / 중복
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- Verified (Brooks 1983, Pennington 1987, Soloway & Ehrlich 1984, Storey 2006 review).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — top-down/bottom-up/opportunistic + LLM augmentation |
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