diff --git a/.Rbuildignore b/.Rbuildignore
index 232504f..5a76495 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -22,3 +22,5 @@
 ^\.jules(/.*)?$
 ^\.trivyignore\.yaml$
 ^trivy\.yaml$
+^\.semgrepignore$
+^\.Jules(/.*)?$
diff --git a/.jules/bolt.md b/.jules/bolt.md
index 7d3c603..b028326 100644
--- a/.jules/bolt.md
+++ b/.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.
+## 2024-08-01 - Avoid sort() overhead for finding minimum element
+**Learning:** In R, using `sort(x)[1]` or `names(sort(x))[1]` to find the minimum (or maximum) element incurs unnecessary O(N log N) overhead, compared to using `which.min()` (or `which.max()`) which runs in O(N) linear time.
+**Action:** When searching for the minimum or maximum value or its index/name, prefer using `which.min(x)` or `which.max(x)` instead of sorting the entire vector, especially inside iterative/looping structures like model fitting fallbacks where overhead compounds.
diff --git a/R/surveyFA.R b/R/surveyFA.R
index f60fffd..c1dcfa5 100644
--- a/R/surveyFA.R
+++ b/R/surveyFA.R
@@ -232,7 +232,8 @@ surveyFA <- function(
       names(p_values) <- rownames(fit_df)
       if (any(!is.na(p_values))) {
         p_values[is.na(p_values)] <- 1
-        candidate <- names(sort(p_values, decreasing = FALSE))[1L]
+        # ⚡ Bolt: Use which.min() for O(N) linear time lookup instead of O(N log N) sort() overhead
+        candidate <- names(which.min(p_values))
         if (!is.na(candidate) && p_values[[candidate]] < pThreshold) {
           return(candidate)
         }
diff --git a/test_dummy.R b/test_dummy.R
deleted file mode 100644
index e6f7019..0000000
--- a/test_dummy.R
+++ /dev/null
@@ -1,2 +0,0 @@
-source("R/aFIPC.R")
-source("R/surveyFA.R")
diff --git a/test_validation.R b/test_validation.R
deleted file mode 100644
index f084116..0000000
--- a/test_validation.R
+++ /dev/null
@@ -1,3 +0,0 @@
-source("R/aFIPC.R")
-source("R/surveyFA.R")
-print("Syntax check passed")
diff --git a/tests/testthat/test-surveyFA.R b/tests/testthat/test-surveyFA.R
index 060ae68..e1cc146 100644
--- a/tests/testthat/test-surveyFA.R
+++ b/tests/testthat/test-surveyFA.R
@@ -82,3 +82,85 @@ test_that("surveyFA reports bounded recovery exhaustion when unrecoverable", {
     "could not estimate a valid model after bounded recovery attempts"
   )
 })
+
+test_that("surveyFA properly triggers fallback when autofix is disabled and covers item removal", {
+  skip_if_not_installed("mirt")
+  set.seed(20260702)
+
+  # Intentionally messy data to force failure on standard methods
+  raw <- as.data.frame(
+    matrix(
+      c(rep(1, 20), rep(0, 20), rbinom(160, 1, 0.5)),
+      ncol = 5
+    )
+  )
+  names(raw) <- paste0("item", 1:5)
+  raw$item6 <- 0 # Constant column
+
+  # Force failure without autofix
+  expect_error(
+    suppressWarnings(
+      aFIPC::surveyFA(
+        data = raw,
+        autofix = FALSE,
+        forceUIRT = TRUE,
+        forceNormalEM = FALSE,
+        forceMHRM = TRUE, # Cover forceMHRM branch
+        unstable = FALSE,
+        SE = TRUE,
+        itemtype = "2PL",
+        maxItemRemovals = 2
+      )
+    ),
+    "could not estimate a valid model after bounded recovery attempts"
+  )
+
+  # Trigger unstable branch and force NormalEM=FALSE
+  expect_error(
+     suppressWarnings(
+        aFIPC::surveyFA(
+          data = raw,
+          autofix = TRUE,
+          forceUIRT = TRUE,
+          forceNormalEM = FALSE,
+          forceMHRM = FALSE,
+          unstable = FALSE,
+          SE = TRUE,
+          itemtype = "2PL",
+          maxItemRemovals = 2
+        )
+      ),
+      "could not estimate a valid model after bounded recovery attempts"
+  )
+
+  # Trigger legacy forceUIRT warning
+  expect_error(
+    aFIPC::surveyFA(data=raw, forceUIRT = FALSE),
+    "surveyFA requires forceUIRT=TRUE"
+  )
+
+  # Check invalid itemtype
+  expect_error(
+    aFIPC::surveyFA(data=raw, itemtype = c("2PL", "3PL")),
+    "surveyFA requires itemtype to be a single non-NA character value"
+  )
+
+  # Check invalid maxItemRemovals
+  expect_error(
+    aFIPC::surveyFA(data=raw, maxItemRemovals = -1),
+    "surveyFA requires maxItemRemovals to be a non-negative numeric scalar"
+  )
+
+  # Check invalid pThreshold
+  expect_error(
+    aFIPC::surveyFA(data=raw, pThreshold = 1.5),
+    "surveyFA requires pThreshold to be in \\(0, 1\\]"
+  )
+
+  # Check insufficient non-constant columns
+  bad_raw <- data.frame(item1 = rep(1, 10), item2 = rep(2, 10))
+  expect_error(
+    aFIPC::surveyFA(bad_raw, forceUIRT=TRUE),
+    "surveyFA needs at least two non-constant response columns"
+  )
+})