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⚡ Bolt: 최솟값 검색 성능 최적화 (O(N log N) -> O(N)) #200

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⚡ Bolt: 최솟값 검색 성능 최적화 (O(N log N) -> O(N)) #200

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dc80c07
⚡ Bolt: 최솟값 검색 성능 최적화 (O(N log N) -> O(N))
seonghobae Aug 1, 2026
997331a
⚡ Bolt: 최솟값 검색 성능 최적화 (O(N log N) -> O(N))
seonghobae Aug 1, 2026
f137af4
⚡ Bolt: 최솟값 검색 성능 최적화 (O(N log N) -> O(N))
seonghobae Aug 1, 2026
8a93594
⚡ Bolt: 최솟값 검색 성능 최적화 (O(N log N) -> O(N))
seonghobae Aug 1, 2026
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2 changes: 2 additions & 0 deletions .Rbuildignore
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Original file line number Diff line number Diff line change
Expand Up @@ -22,3 +22,5 @@
^\.jules(/.*)?$
^\.trivyignore\.yaml$
^trivy\.yaml$
^\.semgrepignore$
^\.Jules(/.*)?$
3 changes: 3 additions & 0 deletions .jules/bolt.md
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Expand Up @@ -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.
3 changes: 2 additions & 1 deletion R/surveyFA.R
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Expand Up @@ -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)
}
Expand Down
2 changes: 0 additions & 2 deletions test_dummy.R
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3 changes: 0 additions & 3 deletions test_validation.R
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82 changes: 82 additions & 0 deletions tests/testthat/test-surveyFA.R
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Expand Up @@ -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"
)
Comment on lines +90 to +134

@coderabbitai coderabbitai Bot Aug 1, 2026

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🎯 Functional Correctness | 🟠 Major | ⚡ Quick win

🧩 Analysis chain

🏁 Script executed:

#!/bin/bash
set -e
printf '%s\n' '--- tracked files ---'
git ls-files | rg '(^|/)(surveyFA\\.R|test-surveyFA\\.R)$'
printf '%s\n' '--- test fixture and expectations ---'
test_file=$(git ls-files | rg '(^|/)tests/testthat/test-surveyFA\\.R$' | head -n1)
survey_file=$(git ls-files | rg '(^|/)R/surveyFA\\.R$' | head -n1)
printf 'test_file=%s\nsurvey_file=%s\n' "$test_file" "$survey_file"
sed -n '70,180p' "$test_file"
printf '%s\n' '--- surveyFA implementation ---'
sed -n '1,240p' "$survey_file"

Repository: ContextualWisdomLab/aFIPC

Length of output: 185

🏁 Script executed:

#!/bin/bash
set -e

test_file=$(git ls-files | rg '(^|/)tests/testthat/test-surveyFA\.R$' | head -n1)
survey_file=$(git ls-files | rg '(^|/)R/surveyFA\.R$' | head -n1)

printf '%s\n' '--- test section ---'
sed -n '70,180p' "$test_file"

printf '%s\n' '--- surveyFA implementation ---'
sed -n '1,240p' "$survey_file"

Repository: ContextualWisdomLab/aFIPC

Length of output: 9828

🏁 Script executed:

#!/bin/bash
set -e
survey_file=$(git ls-files | rg '(^|/)R/surveyFA\.R$' | head -n1)
printf '%s\n' '--- recovery and branch logic ---'
sed -n '220,430p' "$survey_file"
printf '%s\n' '--- test setup and skip scope ---'
test_file=$(git ls-files | rg '(^|/)tests/testthat/test-surveyFA\.R$' | head -n1)
sed -n '1,155p' "$test_file"
printf '%s\n' '--- deterministic matrix-shape probe ---'
python3 - <<'PY'
import random
values = [1] * 20 + [0] * 20 + [random.randint(0, 1) for _ in range(160)]
nrow, ncol = 40, 5
columns = [values[i*nrow:(i+1)*nrow] for i in range(ncol)]
for i, col in enumerate(columns, 1):
    print(f"item{i}: unique={sorted(set(col))}, counts={{0:{col.count(0)},1:{col.count(1)}}}")
PY

Repository: ContextualWisdomLab/aFIPC

Length of output: 6638

🏁 Script executed:

#!/bin/bash
set -e

survey_file=$(git ls-files | rg '(^|/)R/surveyFA\.R$' | head -n1)
test_file=$(git ls-files | rg '(^|/)tests/testthat/test-surveyFA\.R$' | head -n1)

sed -n '220,430p' "$survey_file"
sed -n '1,155p' "$test_file"

python3 - <<'PY'
import random
values = [1] * 20 + [0] * 20 + [random.randint(0, 1) for _ in range(160)]
for i in range(5):
    col = values[i * 40:(i + 1) * 40]
    print(i + 1, sorted(set(col)), col.count(0), col.count(1))
PY

Repository: ContextualWisdomLab/aFIPC

Length of output: 6399

복구 실패를 결정적으로 재현하는 fixture를 사용하세요. raw$item6은 적합 전에 제거됩니다. matrix(...)는 item1–item5에 유효한 응답을 남깁니다. 따라서 forceMHRM = TRUE와 autofix = TRUE는 try_fit() 실패를 보장하지 않으며, 현재 테스트는 autofix 항목 제거도 검증하지 못합니다. 실패를 보장하는 입력 또는 mirt::mirt stub을 사용하세요. unstable 분기를 검증하는 두 번째 호출은 unstable = TRUE로 설정하세요.

🤖 Prompt for AI Agents 
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.

In `@tests/testthat/test-surveyFA.R` around lines 90 - 134, Update the test
fixture and calls around surveyFA so the recovery failure is deterministic: use
input data that still causes try_fit() to fail after the pre-fit removal of
raw$item6, or stub mirt::mirt to force that failure. Ensure the autofix = TRUE
case exercises item removal, and set unstable = TRUE in the second surveyFA call
to actually cover the unstable branch.


# 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"
)
})
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