⚡ Bolt: 데이터 프레임의 불필요한 부분집합 추출에 따른 O(N) 오버헤드 제거

.github/workflows/codeql.yml

@@ -0,0 +1,57 @@
+# Restores PR-head CodeQL coverage for this repo.
+#
+# History: commit 83ecc6e (PR #118, "ci: remove local governance workflows
+# duplicated by central required workflows") deleted the previous local
+# .github/workflows/codeql.yml on the assumption that the org-wide required
+# workflow ruleset (ContextualWisdomLab, ruleset id 18156473) already runs
+# ContextualWisdomLab/.github's central codeql-pr.yml against every PR here.
+#
+# That assumption was verified FALSE on 2026-09-02: ruleset 18156473 does not
+# actually include codeql-pr.yml, so this repo has had zero CodeQL coverage on
+# pull requests since #118 merged. This file is an interim, repo-local safety
+# net until an org admin adds codeql-pr.yml to ruleset 18156473 (tracked
+# separately — out of scope for a repo-level change). Remove this file once
+# that ruleset fix is confirmed live.
+#
+# Language: "actions" only, verified against this repo's own file extensions
+# (no first-party Python/JS-TS/Java-Kotlin source; the ~980 .c/.h/.hpp files
+# under packrat/lib/ are vendored R package dependencies, not repo-authored
+# code) and against the CodeQL analyses this repo has actually produced
+# historically (always /language:actions, never anything else).
+name: CodeQL
+
+on:
+  pull_request:
+    branches: ["master"]
+  workflow_dispatch:
+
+permissions:
+  contents: read
+
+jobs:
+  analyze:
+    name: Analyze (actions)
+    runs-on: ubuntu-latest
+    permissions:
+      actions: read
+      contents: read
+      security-events: write
+    steps:
+      - name: Harden runner
+        uses: step-security/harden-runner@bf7454d06d71f1098171f2acdf0cd4708d7b5920
+        with:
+          egress-policy: audit
+
+      - name: Checkout
+        uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd
+
+      - name: Initialize CodeQL
+        uses: github/codeql-action/init@db488ddef3bf6cb639b32c2e9a7c0a7ea8271d28 # v4.37.8
+        with:
+          languages: actions
+          build-mode: none
+
+      - name: Perform CodeQL Analysis
+        uses: github/codeql-action/analyze@db488ddef3bf6cb639b32c2e9a7c0a7ea8271d28 # v4.37.8
+        with:
+          category: "/language:actions"

.jules/bolt.md

@@ -16,3 +16,6 @@
 ## 2025-02-12 - R 언어에서 반복적인 mirt 모델 생성 시 불필요한 데이터프레임 부분집합 추출 최적화
 **Learning:** R에서 데이터프레임의 특정 열을 추출하는 작업(`df[cols]`)은 O(N)의 메모리 복사를 수반합니다. `autoFIPC`에서 `mirt` 모델의 파라미터를 설정하거나 호출하는 과정 중에 `newformXDataK[colnames(newFormModel@Data$data)]` 코드가 반복해서 사용되었고, 심지어 `ncol()`을 위해 단순히 개수를 구할 때도 사용되어 불필요한 메모리 할당과 오버헤드를 초래했습니다.
 **Action:** 조건문이나 반복문 내부에서 불필요하게 데이터프레임 부분집합 연산이 반복되지 않도록 외부에서 한 번만 `linkedFormData <- newformXDataK[colnames(newFormModel@Data$data)]`로 캐싱(caching)한 뒤, `ncol(linkedFormData)`와 `data = linkedFormData` 형태로 재사용하여 메모리 복사와 O(N) 오버헤드를 방지해야 합니다.
+## 2025-02-13 - R 언어에서 데이터 프레임 불필요한 부분집합 추출 오버헤드 제거
+**Learning:** R에서 열 이름을 얻기 위해 `colnames(df[cols])` 처럼 데이터 프레임을 서브셋팅(subsetting)하는 것은, 모든 해당 데이터를 복사하므로 불필요한 O(N) 메모리 할당 및 복사 오버헤드를 발생시킵니다.
+**Action:** 단순히 열 이름 배열이 필요한 경우에는 `df[cols]`처럼 부분집합을 만들지 않고 기존에 확보된 열 이름 벡터(`cols`)를 그대로 재사용하여 O(1) 수준으로 불필요한 메모리 복사를 제거해야 합니다.

R/aFIPC.R

@@ -620,8 +620,8 @@ autoFIPC <-
       IPDItemCount <- 0
 
       # IPD target item checking
-      newFormColNames <- colnames(newformXDataK[colnames(newFormModel@Data$data)])
-      oldFormColNames <- colnames(oldformYDataK[colnames(oldFormModel@Data$data)])
+      newFormColNames <- colnames(newFormModel@Data$data)
+      oldFormColNames <- colnames(oldFormModel@Data$data)
 
       # ⚡ Bolt: Vectorized match() to avoid dynamic array growth overhead inside a for loop
       idxNew <- match(newformCommonItemNames, newFormColNames)
@@ -749,8 +749,8 @@ autoFIPC <-
       }
     }
 
-    newFormColNames <- colnames(newformXDataK[colnames(newFormModel@Data$data)])
-    oldFormColNames <- colnames(oldformYDataK[colnames(oldFormModel@Data$data)])
+    newFormColNames <- colnames(newFormModel@Data$data)
+    oldFormColNames <- colnames(oldFormModel@Data$data)
 
     # ⚡ Bolt: Cache parameter indices to avoid O(N) linear search inside loop
     newScaleParmsItemIdxCache <- split(seq_len(nrow(NewScaleParms)), NewScaleParms$item)