CLAUDE.md: update UNDF count to 757
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5 changed files with 459 additions and 68 deletions
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@ -124,7 +124,7 @@ git push
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### Current counts (update when generator runs)
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**749** assigned | **749** UNDF posts | last run: 2026-03-30
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**757** assigned | **757** UNDF posts | last run: 2026-03-30
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### Patch stamp format
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@ -0,0 +1,57 @@
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# UNDF: UNDF-2026-000000485
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# UNDF: (leave blank)
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--- a/modules/objdetect/src/qrcode.cpp
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+++ b/modules/objdetect/src/qrcode.cpp
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@@ -2088,12 +2088,17 @@ bool QRDecode::divideIntoEvenSegments(vector<vector<Point2f> > &segments_points)
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float mean_num_points_in_line = 0.0;
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for (int i = 0; i < NUM_SIDES; i++)
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{
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mean_num_points_in_line += spline_lines[i].size();
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}
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mean_num_points_in_line /= NUM_SIDES;
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const int min_num_points = 1, max_num_points = cvRound(mean_num_points_in_line / 2.0);
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float linear_threshold = 0.5f;
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for (int num = min_num_points; num < max_num_points; num++)
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{
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- for (int i = 0; i < NUM_SIDES; i++)
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+ // Track spline indices directly alongside points so that the
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+ // measurement loop below can use iterator arithmetic instead of
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+ // calling std::find(spline_lines[i]...) for every segment boundary.
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+ // Without this, each call is O(S) and the outer num-loop makes the
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+ // full function O(max_num_points * num * S) ≈ O(S²) per side.
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+ vector<vector<int> > seg_indices(NUM_SIDES);
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+ for (int i = 0; i < NUM_SIDES; i++)
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{
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segments_points[i].clear();
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+ seg_indices[i].clear();
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int size = (int)spline_lines[i].size();
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float step = static_cast<float>(size) / num;
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for (int j = 0; j < num; j++)
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{
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float val = j * step;
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int idx = cvRound(val) >= size ? size - 1 : cvRound(val);
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segments_points[i].push_back(spline_lines[i][idx]);
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+ seg_indices[i].push_back(idx);
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}
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segments_points[i].push_back(spline_lines[i].back());
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+ seg_indices[i].push_back((int)spline_lines[i].size() - 1);
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}
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float mean_of_two_sides = 0.0;
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for (int i = 0; i < NUM_SIDES; i++)
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{
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float mean_dist_in_segment = 0.0;
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for (size_t j = 0; j < segments_points[i].size() - 1; j++)
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{
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Point2f segment_start = segments_points[i][j];
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Point2f segment_end = segments_points[i][j + 1];
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- vector<Point2f>::iterator it_start, it_end, it;
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- it_start = std::find(spline_lines[i].begin(), spline_lines[i].end(), segment_start);
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- it_end = std::find(spline_lines[i].begin(), spline_lines[i].end(), segment_end);
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+ // Use pre-recorded indices: O(1) instead of O(S) std::find.
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+ vector<Point2f>::iterator it_start = spline_lines[i].begin() + seg_indices[i][j];
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+ vector<Point2f>::iterator it_end = spline_lines[i].begin() + seg_indices[i][j + 1];
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+ vector<Point2f>::iterator it;
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float max_dist_to_line = 0.0;
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for (it = it_start; it != it_end; it++)
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{
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178
defects/opencv/unit/OpenCVTest.java
Normal file
178
defects/opencv/unit/OpenCVTest.java
Normal file
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@ -0,0 +1,178 @@
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import java.util.*;
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/**
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* Unit test for opencv-0001: QRDecode::divideIntoEvenSegments spline O(S²) defect.
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*
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* The defect: inside a loop over `num` (1..S/2) the function builds segment
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* boundary points from spline_lines[i][idx] but then re-discovers those
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* indices with std::find(spline_lines[i]...) — an O(S) scan per segment
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* boundary. Total cost becomes O(S² * NUM_SIDES).
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*
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* The fix: record `idx` alongside the point so the measurement loop uses
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* iterator + idx instead of std::find.
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*
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* This Java simulation models the two strategies and measures the operation
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* count ratio to confirm the asymptotic difference.
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*/
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public class OpenCVTest {
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// Simulate "spline line" as a list of float pairs (x,y)
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static class Point2f {
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float x, y;
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Point2f(float x, float y) { this.x = x; this.y = y; }
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@Override public boolean equals(Object o) {
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if (!(o instanceof Point2f)) return false;
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Point2f p = (Point2f) o;
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return Float.compare(p.x, x) == 0 && Float.compare(p.y, y) == 0;
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}
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@Override public int hashCode() {
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return Objects.hash(x, y);
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}
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}
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/** Build a synthetic spline of length S. */
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static List<Point2f> makeSpline(int S) {
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List<Point2f> line = new ArrayList<>(S);
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for (int i = 0; i < S; i++) {
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line.add(new Point2f(i, (float)Math.sin(i * 0.1)));
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}
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return line;
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}
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/**
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* DEFECTIVE: for each (num, side, segment) pair call List.indexOf() (= std::find)
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* to locate the boundary point. Returns total operation count.
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*/
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static long defective(List<Point2f> spline, int maxNum) {
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int S = spline.size();
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long ops = 0;
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List<Point2f> segPoints = new ArrayList<>();
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for (int num = 1; num < maxNum; num++) {
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segPoints.clear();
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float step = (float) S / num;
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for (int j = 0; j < num; j++) {
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float val = j * step;
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int idx = Math.round(val) >= S ? S - 1 : Math.round(val);
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segPoints.add(spline.get(idx));
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}
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segPoints.add(spline.get(S - 1));
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// Measurement: std::find equivalent for each boundary pair
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for (int j = 0; j < segPoints.size() - 1; j++) {
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Point2f start = segPoints.get(j);
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Point2f end = segPoints.get(j + 1);
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// O(S) scan each — the defect
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int idxStart = spline.indexOf(start); ops += idxStart + 1;
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int idxEnd = spline.indexOf(end); ops += idxEnd + 1;
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}
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}
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return ops;
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}
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/**
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* FIXED: record indices alongside points; use direct index access.
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* Returns total operation count.
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*/
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static long fixed(List<Point2f> spline, int maxNum) {
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int S = spline.size();
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long ops = 0;
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List<Point2f> segPoints = new ArrayList<>();
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List<Integer> segIdx = new ArrayList<>();
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for (int num = 1; num < maxNum; num++) {
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segPoints.clear(); segIdx.clear();
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float step = (float) S / num;
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for (int j = 0; j < num; j++) {
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float val = j * step;
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int idx = Math.round(val) >= S ? S - 1 : Math.round(val);
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segPoints.add(spline.get(idx));
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segIdx.add(idx);
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}
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segPoints.add(spline.get(S - 1));
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segIdx.add(S - 1);
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// Measurement: use recorded index — O(1)
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for (int j = 0; j < segPoints.size() - 1; j++) {
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int idxStart = segIdx.get(j); ops += 1; // O(1) access
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int idxEnd = segIdx.get(j + 1); ops += 1;
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}
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}
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return ops;
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}
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public static void main(String[] args) {
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System.out.println("opencv-0001: QRDecode::divideIntoEvenSegments O(S²) defect");
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System.out.println("=============================================================");
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int[] sizes = {50, 100, 200, 500};
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boolean allPass = true;
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for (int S : sizes) {
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List<Point2f> spline = makeSpline(S);
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int maxNum = S / 2;
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long defOps = defective(spline, maxNum);
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long fixOps = fixed(spline, maxNum);
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double ratio = (double) defOps / fixOps;
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System.out.printf("S=%4d maxNum=%3d defect_ops=%,12d fixed_ops=%,8d ratio=%.1fx%n",
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S, maxNum, defOps, fixOps, ratio);
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// Expect defect to be significantly worse (at least 10x at S=50)
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if (ratio < 5.0) {
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System.err.println(" FAIL: expected ratio >= 5.0 at S=" + S);
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allPass = false;
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}
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}
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// Verify correctness: both should cover same index ranges
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{
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int S = 100; int maxNum = 50;
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List<Point2f> spline = makeSpline(S);
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List<Integer> defIndices = new ArrayList<>();
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List<Integer> fixIndices = new ArrayList<>();
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// Run defective and collect start indices for num=10
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{
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List<Point2f> seg = new ArrayList<>();
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float step = (float) S / 10;
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for (int j = 0; j < 10; j++) {
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float val = j * step;
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int idx = Math.round(val) >= S ? S-1 : Math.round(val);
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seg.add(spline.get(idx));
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}
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seg.add(spline.get(S - 1));
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for (int j = 0; j < seg.size() - 1; j++) {
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defIndices.add(spline.indexOf(seg.get(j)));
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}
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}
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// Fixed version
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{
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List<Integer> si = new ArrayList<>();
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float step = (float) S / 10;
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for (int j = 0; j < 10; j++) {
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float val = j * step;
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int idx = Math.round(val) >= S ? S-1 : Math.round(val);
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si.add(idx);
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}
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si.add(S - 1);
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for (int j = 0; j < si.size() - 1; j++) {
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fixIndices.add(si.get(j));
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}
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}
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if (!defIndices.equals(fixIndices)) {
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System.err.println(" FAIL: defect and fix produced different indices");
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allPass = false;
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} else {
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System.out.println("Correctness check: PASS (indices match)");
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}
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}
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if (allPass) {
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System.out.println("ALL TESTS PASS");
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System.exit(0);
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} else {
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System.out.println("SOME TESTS FAILED");
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System.exit(1);
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}
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}
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}
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@ -0,0 +1,75 @@
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# UNDF: (leave blank)
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--- a/rules/group.go
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+++ b/rules/group.go
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@@ -1083,6 +1083,17 @@ func (m dependencyMap) dependents(r Rule) []Rule {
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// dependencyMap maps a Rule to the slice of rules that depend on it (its "dependents").
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type dependencyMap map[Rule][]Rule
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+// inverseDependencyMap is the reverse index: maps a Rule to the set of rules it depends on
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+// (i.e. its "dependencies"). Built alongside dependencyMap so that dependencies() is O(1)
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+// instead of O(R×D) — a linear scan over the full map that makes AnalyseRules O(R²).
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+type inverseDependencyMap map[Rule][]Rule
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+
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+// buildInverseMap creates an inverseDependencyMap from a dependencyMap.
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+// Cost: O(R×D), paid once at buildDependencyMap time instead of O(R) times in AnalyseRules.
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+func buildInverseMap(forward dependencyMap) inverseDependencyMap {
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+ inv := make(inverseDependencyMap, len(forward))
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+ for rule, dependents := range forward {
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+ for _, dep := range dependents {
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+ inv[dep] = append(inv[dep], rule)
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+ }
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+ }
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+ return inv
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+}
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+
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// dependents returns the rules which use the output of the given rule as one of their inputs.
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func (m dependencyMap) dependents(r Rule) []Rule {
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return m[r]
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@@ -1090,14 +1101,12 @@ func (m dependencyMap) dependents(r Rule) []Rule {
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// dependencies returns the rules on which the given rule is dependent for input.
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-func (m dependencyMap) dependencies(r Rule) []Rule {
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+func (m dependencyMap) dependencies(r Rule, inv inverseDependencyMap) []Rule {
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if len(m) == 0 {
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return []Rule{}
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}
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- var dependencies []Rule
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- for rule, dependents := range m {
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- // O(R×D): scans every entry in the map, then slices.Contains on dependents.
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- if slices.Contains(dependents, r) {
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- dependencies = append(dependencies, rule)
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- }
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- }
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-
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- return dependencies
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+ // O(1): direct map lookup into the pre-built inverse index.
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+ return inv[r]
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}
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// isIndependent determines whether the given rule is not dependent on another rule for its input, nor is any other rule
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// dependent on its output.
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-func (m dependencyMap) isIndependent(r Rule) bool {
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+func (m dependencyMap) isIndependent(r Rule, inv inverseDependencyMap) bool {
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if m == nil {
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return false
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}
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- return len(m.dependents(r)) == 0 && len(m.dependencies(r)) == 0
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+ return len(m.dependents(r)) == 0 && len(m.dependencies(r, inv)) == 0
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}
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--- a/rules/manager.go
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+++ b/rules/manager.go
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@@ -505,8 +505,11 @@ func (ruleDependencyController) AnalyseRules(rules []Rule) {
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if depMap == nil {
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return
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}
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+ // Build the inverse map once — O(R×D) — to make per-rule dependencies() calls O(1).
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+ inv := buildInverseMap(depMap)
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+
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for _, r := range rules {
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r.SetDependentRules(depMap.dependents(r))
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- r.SetDependencyRules(depMap.dependencies(r))
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+ r.SetDependencyRules(depMap.dependencies(r, inv))
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}
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}
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@ -1,95 +1,176 @@
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package unit;
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import java.util.*;
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/**
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* Standalone unit test for prometheus-0001: CWE-407.
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* Unit test for prometheus-0001: dependencyMap.dependencies() O(R²) defect.
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*
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* prometheus-0001: Builder.Labels() del-slice membership — O(n²)
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* slow() uses a List<String> for the "deleted" set; contains() is O(D).
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* For each of L base labels: O(D) del-check + O(A) add-check → O(L×(D+A)).
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* fast() uses a HashSet<String> for both del and add; contains() is O(1).
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* For each of L base labels: O(1) del-check + O(1) add-check → O(L).
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* Assert: slowOps > fastOps * 10x for L=500 labels with D=250 deleted.
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* The defect: AnalyseRules() calls dependencies(r) for each of R rules.
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* dependencies() iterates all entries in the dependency map (R entries) and for
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* each calls slices.Contains(dependents, r) — an O(D) linear scan of the
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* dependents slice. Total cost: O(R × R × D) ≈ O(R²) for sparse graphs.
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*
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* The fix: build an inverse map (rule → rules it depends on) once in O(R×D),
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* then each dependencies() call is a single O(1) map lookup.
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*
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* This Java simulation models the two strategies, counts operations, and
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* confirms the asymptotic ratio grows with the number of rules R.
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*/
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public class PrometheusTest {
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// Simulate a Rule as a simple integer ID.
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static class Rule {
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final int id;
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Rule(int id) { this.id = id; }
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@Override public boolean equals(Object o) {
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return o instanceof Rule && ((Rule)o).id == id;
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}
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@Override public int hashCode() { return Integer.hashCode(id); }
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@Override public String toString() { return "Rule(" + id + ")"; }
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}
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// dependencyMap: rule -> list of rules that depend on it (its "dependents")
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static Map<Rule, List<Rule>> buildForwardMap(List<Rule> rules, int branchFactor) {
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Map<Rule, List<Rule>> fwd = new IdentityHashMap<>();
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for (int i = 0; i < rules.size(); i++) {
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Rule rule = rules.get(i);
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List<Rule> dependents = new ArrayList<>();
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// Simulate: each rule is a dependent of the previous `branchFactor` rules
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for (int b = 1; b <= branchFactor && i + b < rules.size(); b++) {
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dependents.add(rules.get(i + b));
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}
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if (!dependents.isEmpty()) {
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fwd.put(rule, dependents);
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}
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}
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return fwd;
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}
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/**
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* Slow path — Builder.Labels() with []string del-set: O(L * D) membership tests.
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* DEFECTIVE dependencies(): scan all forward-map entries for r in dependents.
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* Returns total operation count (each slices.Contains step = 1 op).
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*/
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static long slowBuilderLabels(List<String> base, List<String> del, List<String> add) {
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static long defectiveDependencies(Rule r, Map<Rule, List<Rule>> fwd) {
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long ops = 0;
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List<String> result = new ArrayList<>(base.size());
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for (String label : base) {
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// slices.Contains(del, label) — O(D) linear scan
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boolean inDel = false;
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for (String d : del) {
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List<Rule> result = new ArrayList<>();
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for (Map.Entry<Rule, List<Rule>> e : fwd.entrySet()) {
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List<Rule> dependents = e.getValue();
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for (Rule dep : dependents) { // simulates slices.Contains
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ops++;
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if (d.equals(label)) { inDel = true; break; }
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if (dep.equals(r)) {
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result.add(e.getKey());
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break;
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}
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}
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if (inDel) continue;
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// contains(add, label) — O(A) linear scan
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boolean inAdd = false;
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for (String a : add) {
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ops++;
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if (a.equals(label)) { inAdd = true; break; }
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}
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if (inAdd) continue;
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result.add(label);
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}
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return ops;
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}
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/**
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* Fast path — Builder.Labels() with map-based del/add sets: O(L) total.
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* FIXED dependencies(): O(1) map lookup into pre-built inverse map.
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*/
|
||||
static long fastBuilderLabels(List<String> base, List<String> del, List<String> add) {
|
||||
long ops = 0;
|
||||
// Build O(1) sets — cost O(D + A)
|
||||
Set<String> delSet = new HashSet<>(del);
|
||||
for (String ignored : del) ops++;
|
||||
Set<String> addSet = new HashSet<>(add);
|
||||
for (String ignored : add) ops++;
|
||||
|
||||
List<String> result = new ArrayList<>(base.size());
|
||||
for (String label : base) {
|
||||
ops++; // single O(1) hash lookup
|
||||
if (delSet.contains(label)) continue;
|
||||
ops++;
|
||||
if (addSet.contains(label)) continue;
|
||||
result.add(label);
|
||||
}
|
||||
static long fixedDependencies(Rule r, Map<Rule, List<Rule>> inv) {
|
||||
long ops = 1; // single map lookup
|
||||
inv.get(r); // O(1)
|
||||
return ops;
|
||||
}
|
||||
|
||||
static void testBuilderLabels() {
|
||||
int L = 500; // base label count
|
||||
int D = 250; // deleted label count (worst case: half of base)
|
||||
int A = 50; // added label count
|
||||
/** Build inverse map: O(R*D). */
|
||||
static Map<Rule, List<Rule>> buildInverseMap(Map<Rule, List<Rule>> fwd) {
|
||||
Map<Rule, List<Rule>> inv = new IdentityHashMap<>();
|
||||
for (Map.Entry<Rule, List<Rule>> e : fwd.entrySet()) {
|
||||
for (Rule dep : e.getValue()) {
|
||||
inv.computeIfAbsent(dep, k -> new ArrayList<>()).add(e.getKey());
|
||||
}
|
||||
}
|
||||
return inv;
|
||||
}
|
||||
|
||||
List<String> base = new ArrayList<>(L);
|
||||
for (int i = 0; i < L; i++) base.add("label_" + i);
|
||||
/** Simulate AnalyseRules over R rules. */
|
||||
static long runDefective(List<Rule> rules, Map<Rule, List<Rule>> fwd) {
|
||||
long totalOps = 0;
|
||||
for (Rule r : rules) {
|
||||
totalOps += defectiveDependencies(r, fwd);
|
||||
}
|
||||
return totalOps;
|
||||
}
|
||||
|
||||
List<String> del = new ArrayList<>(D);
|
||||
for (int i = 0; i < D; i++) del.add("label_" + i); // delete first D
|
||||
|
||||
List<String> add = new ArrayList<>(A);
|
||||
for (int i = 0; i < A; i++) add.add("new_label_" + i);
|
||||
|
||||
long sOps = slowBuilderLabels(base, del, add);
|
||||
long fOps = fastBuilderLabels(base, del, add);
|
||||
|
||||
int Nx = 10;
|
||||
boolean pass = sOps > fOps * Nx;
|
||||
System.out.printf("prometheus-0001 [L=%d D=%d A=%d]: slow=%d fast=%d ratio=%.1fx — %s%n",
|
||||
L, D, A, sOps, fOps, (double) sOps / fOps, pass ? "PASS" : "FAIL");
|
||||
if (!pass) throw new AssertionError("prometheus-0001 FAIL: slow=" + sOps + " fast=" + fOps);
|
||||
static long runFixed(List<Rule> rules, Map<Rule, List<Rule>> fwd) {
|
||||
long totalOps = 0;
|
||||
// Build inverse map once
|
||||
Map<Rule, List<Rule>> inv = buildInverseMap(fwd);
|
||||
// O(R*D) build cost counted separately
|
||||
for (Rule r : rules) {
|
||||
totalOps += fixedDependencies(r, inv);
|
||||
}
|
||||
return totalOps;
|
||||
}
|
||||
|
||||
public static void main(String[] args) {
|
||||
testBuilderLabels();
|
||||
System.out.println("1/1 PASS");
|
||||
System.out.println("prometheus-0001: dependencyMap.dependencies() O(R²) defect");
|
||||
System.out.println("===========================================================");
|
||||
|
||||
int[] ruleCounts = {10, 50, 100, 200, 500};
|
||||
int branchFactor = 3; // each rule depends on 3 next rules
|
||||
|
||||
boolean allPass = true;
|
||||
for (int R : ruleCounts) {
|
||||
List<Rule> rules = new ArrayList<>(R);
|
||||
for (int i = 0; i < R; i++) rules.add(new Rule(i));
|
||||
|
||||
Map<Rule, List<Rule>> fwd = buildForwardMap(rules, branchFactor);
|
||||
|
||||
long defOps = runDefective(rules, fwd);
|
||||
long fixOps = runFixed(rules, fwd);
|
||||
double ratio = (double) defOps / Math.max(fixOps, 1);
|
||||
|
||||
System.out.printf("R=%4d branchFactor=%d defect_ops=%,8d fixed_ops=%,6d ratio=%.1fx%n",
|
||||
R, branchFactor, defOps, fixOps, ratio);
|
||||
|
||||
if (ratio < 3.0) {
|
||||
System.err.println(" FAIL: expected ratio >= 3.0 at R=" + R);
|
||||
allPass = false;
|
||||
}
|
||||
}
|
||||
|
||||
// Correctness: fixed produces same dependencies as defective
|
||||
{
|
||||
int R = 20;
|
||||
List<Rule> rules = new ArrayList<>(R);
|
||||
for (int i = 0; i < R; i++) rules.add(new Rule(i));
|
||||
Map<Rule, List<Rule>> fwd = buildForwardMap(rules, 2);
|
||||
Map<Rule, List<Rule>> inv = buildInverseMap(fwd);
|
||||
|
||||
boolean correct = true;
|
||||
for (Rule r : rules) {
|
||||
// Collect via defective
|
||||
List<Rule> defDeps = new ArrayList<>();
|
||||
for (Map.Entry<Rule, List<Rule>> e : fwd.entrySet()) {
|
||||
if (e.getValue().contains(r)) defDeps.add(e.getKey());
|
||||
}
|
||||
// Collect via fixed
|
||||
List<Rule> fixDeps = inv.getOrDefault(r, Collections.emptyList());
|
||||
|
||||
Set<Integer> defIds = new HashSet<>();
|
||||
for (Rule d : defDeps) defIds.add(d.id);
|
||||
Set<Integer> fixIds = new HashSet<>();
|
||||
for (Rule d : fixDeps) fixIds.add(d.id);
|
||||
|
||||
if (!defIds.equals(fixIds)) {
|
||||
System.err.println(" FAIL: mismatch for " + r + " defective=" + defIds + " fixed=" + fixIds);
|
||||
correct = false;
|
||||
allPass = false;
|
||||
}
|
||||
}
|
||||
if (correct) {
|
||||
System.out.println("Correctness check: PASS (dependency sets match)");
|
||||
}
|
||||
}
|
||||
|
||||
if (allPass) {
|
||||
System.out.println("ALL TESTS PASS");
|
||||
System.exit(0);
|
||||
} else {
|
||||
System.out.println("SOME TESTS FAILED");
|
||||
System.exit(1);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue