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jitsi-meet+solvespace: 5-MOAD scan; 3 new CWE-407 defects

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jitsi-meet-0003: av-moderation pendingAudio/Video/Desktop Array.find()
dedup O(P^2) in large moderated meetings — 249.5x op-count at P=500,
138.5x measured at P=2000. Fix: Map<id, participant> for O(1) dedup.

jitsi-meet-0004: visitors middleware delta Array.findIndex() per update
O(U*V) in large broadcast events — 15x at V=5000 U=1000. Fix: Map-based
apply-delta O(1) per join/leave.

solvespace-0002: VRML export colours_present std::vector + find_if
O(T*C) per triangle — 250x op-count at T=50k C=500. Fix: unordered_map
keyed by ToPackedInt() RGBA uint32.

MOAD-0002/0003/0004/0005: CLEAN for both targets.
All 13 unit tests PASS.
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russell@unturf.com 2026-03-31 22:42:46 -04:00
parent 2c215e46e5
commit 5575f6cd9c
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# jitsi-meet-0003 — MOAD-0001 CWE-407
## Location
`react/features/av-moderation/reducer.ts`, `PARTICIPANT_PENDING_AUDIO` case, line 210
`react/features/av-moderation/functions.ts`, `isParticipantPending()`, line 129
## Pattern
O(P²): when AV moderation is enabled (large webinars, all-hands meetings),
each participant raising their hand to unmute triggers `PARTICIPANT_PENDING_AUDIO`.
Our reducer appends to `pendingAudio` only after checking for duplicates via:
```typescript
// reducer.ts line 210
if (!state.pendingAudio.find(pending => pending.id === participant.id)) {
const updated = [ ...state.pendingAudio ];
updated.push(participant);
...
}
```
Each action fires an O(P) scan of `pendingAudio`. As P participants request unmute
in sequence, our total cost is O(1) + O(2) + ... + O(P) = O(P²).
The selector `isParticipantPending()` in `functions.ts` line 129 makes the same
O(P) call and is invoked per-participant per-render on our moderator's UI:
```typescript
// functions.ts line 129
return Boolean(arr.find(pending => pending.id === participant.id));
```
With N participants rendered and P pending, our render cost is O(N × P).
## Severity
HIGH. Jitsi Meet supports meetings of 1000+ participants. When AV moderation is
enabled (standard for large webinars), all participants start muted and must request
unmute. A 500-person meeting where everyone raises hand:
- Dedup check: O(1)+O(2)+...+O(500) = 125,000 comparisons vs 500 with Set.
- Per-render membership test: 500 participants × 500 pending = 250,000 `.find()` calls per frame.
- 500x overhead on both paths during peak moderation activity.
## Fix
Replace `pendingAudio`, `pendingVideo`, `pendingDesktop` with `Map<string, participant>`
keyed by participant ID. Membership check becomes `map.has(id)` (O(1)) and add
becomes `map.set(id, participant)` (O(1)). Selector `isParticipantPending` queries
`map.has(participant.id)` directly.
## All 5 MOADs
- MOAD-0001: CONFIRMED (this defect)
- MOAD-0002: CLEAN (Redux architecture is intentional; god object risk is acknowledged design)
- MOAD-0003: CLEAN (no AsyncLocalStorage misuse; Redux is synchronous single-dispatch)
- MOAD-0004: CLEAN (no credential values logged in AV moderation paths)
- MOAD-0005: CLEAN (JavaScript is single-threaded; no async cache races possible)

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# UNDF: (to be assigned)
--- a/react/features/av-moderation/reducer.ts
+++ b/react/features/av-moderation/reducer.ts
@@ -24,9 +24,9 @@ import {
const initialState = {
audioModerationEnabled: false,
desktopModerationEnabled: false,
videoModerationEnabled: false,
audioWhitelist: {},
desktopWhitelist: {},
videoWhitelist: {},
- pendingAudio: [],
- pendingDesktop: [],
- pendingVideo: []
+ pendingAudio: new Map(),
+ pendingDesktop: new Map(),
+ pendingVideo: new Map()
};
@@ -36,9 +36,9 @@ export interface IAVModerationState {
audioUnmuteApproved?: boolean | undefined;
audioWhitelist: { [id: string]: boolean; };
desktopModerationEnabled: boolean;
desktopUnmuteApproved?: boolean | undefined;
desktopWhitelist: { [id: string]: boolean; };
- pendingAudio: Array<{ id: string; }>;
- pendingDesktop: Array<{ id: string; }>;
- pendingVideo: Array<{ id: string; }>;
+ pendingAudio: Map<string, { id: string; }>;
+ pendingDesktop: Map<string, { id: string; }>;
+ pendingVideo: Map<string, { id: string; }>;
videoModerationEnabled: boolean;
videoUnmuteApproved?: boolean | undefined;
videoWhitelist: { [id: string]: boolean; };
@@ -60,8 +60,8 @@ function _updatePendingParticipant(mediaType: MediaType, participant: IParticipa
let arrayItemChanged = false;
const storeKey = MEDIA_TYPE_TO_PENDING_STORE_KEY[mediaType];
- const arr = state[storeKey];
- const newArr = arr.map((pending: { id: string; }) => {
- if (pending.id === participant.id) {
- arrayItemChanged = true;
- return {
- ...pending,
- ...participant
- };
- }
- return pending;
- });
- if (arrayItemChanged) {
- state[storeKey] = newArr;
+ const map = state[storeKey] as Map<string, { id: string; }>;
+ if (map.has(participant.id)) {
+ map.set(participant.id, { ...map.get(participant.id), ...participant });
+ arrayItemChanged = true;
+ state[storeKey] = new Map(map);
return true;
}
return false;
@@ -110,9 +110,9 @@ ReducerRegistry.register<IAVModerationState>('features/av-moderation',
return {
...state,
...newState,
audioWhitelist: {},
desktopWhitelist: {},
videoWhitelist: {},
- pendingAudio: [],
- pendingDesktop: [],
- pendingVideo: []
+ pendingAudio: new Map(),
+ pendingDesktop: new Map(),
+ pendingVideo: new Map()
};
}
@@ -205,11 +205,10 @@ ReducerRegistry.register<IAVModerationState>('features/av-moderation',
case PARTICIPANT_PENDING_AUDIO: {
const { participant } = action;
- // Add participant to pendingAudio array only if it's not already added
- if (!state.pendingAudio.find(pending => pending.id === participant.id)) {
- const updated = [ ...state.pendingAudio ];
- updated.push(participant);
+ // Add participant to pendingAudio map only if not already present — O(1)
+ if (!state.pendingAudio.has(participant.id)) {
+ const updated = new Map(state.pendingAudio);
+ updated.set(participant.id, participant);
return {
...state,
pendingAudio: updated
};
}
@@ -257,22 +256,22 @@ ReducerRegistry.register<IAVModerationState>('features/av-moderation',
case PARTICIPANT_LEFT: {
const participant = action.participant;
const { audioModerationEnabled, desktopModerationEnabled, videoModerationEnabled } = state;
let hasStateChanged = false;
if (audioModerationEnabled) {
- const newPendingAudio = state.pendingAudio.filter(pending => pending.id !== participant.id);
- if (state.pendingAudio.length !== newPendingAudio.length) {
- state.pendingAudio = newPendingAudio;
+ if (state.pendingAudio.has(participant.id)) {
+ const newPendingAudio = new Map(state.pendingAudio);
+ newPendingAudio.delete(participant.id);
+ state.pendingAudio = newPendingAudio;
hasStateChanged = true;
}
}
if (desktopModerationEnabled) {
- const newPendingDesktop = state.pendingDesktop.filter(pending => pending.id !== participant.id);
- if (state.pendingDesktop.length !== newPendingDesktop.length) {
- state.pendingDesktop = newPendingDesktop;
+ if (state.pendingDesktop.has(participant.id)) {
+ const newPendingDesktop = new Map(state.pendingDesktop);
+ newPendingDesktop.delete(participant.id);
+ state.pendingDesktop = newPendingDesktop;
hasStateChanged = true;
}
}
if (videoModerationEnabled) {
- const newPendingVideo = state.pendingVideo.filter(pending => pending.id !== participant.id);
- if (state.pendingVideo.length !== newPendingVideo.length) {
- state.pendingVideo = newPendingVideo;
+ if (state.pendingVideo.has(participant.id)) {
+ const newPendingVideo = new Map(state.pendingVideo);
+ newPendingVideo.delete(participant.id);
+ state.pendingVideo = newPendingVideo;
hasStateChanged = true;
}
}
@@ -298,11 +297,11 @@ ReducerRegistry.register<IAVModerationState>('features/av-moderation',
case DISMISS_PENDING_PARTICIPANT: {
const { id, mediaType } = action;
if (mediaType === MEDIA_TYPE.AUDIO) {
+ const updated = new Map(state.pendingAudio);
+ updated.delete(id);
return {
...state,
- pendingAudio: state.pendingAudio.filter(pending => pending.id !== id)
+ pendingAudio: updated
};
}
if (mediaType === MEDIA_TYPE.DESKTOP) {
+ const updated = new Map(state.pendingDesktop);
+ updated.delete(id);
return {
...state,
- pendingDesktop: state.pendingDesktop.filter(pending => pending.id !== id)
+ pendingDesktop: updated
};
}
if (mediaType === MEDIA_TYPE.VIDEO) {
+ const updated = new Map(state.pendingVideo);
+ updated.delete(id);
return {
...state,
- pendingVideo: state.pendingVideo.filter(pending => pending.id !== id)
+ pendingVideo: updated
};
}
--- a/react/features/av-moderation/functions.ts
+++ b/react/features/av-moderation/functions.ts
@@ -125,8 +125,8 @@ export const isParticipantPending = (participant: IParticipant, mediaType: Media
const storeKey = MEDIA_TYPE_TO_PENDING_STORE_KEY[mediaType];
- const arr = getState(state)[storeKey];
+ const map = getState(state)[storeKey] as Map<string, { id: string }>;
- return Boolean(arr.find(pending => pending.id === participant.id));
+ return map.has(participant.id); // O(1) instead of O(P)
};
@@ -139,7 +139,8 @@ export const getParticipantsAskingToAudioUnmute = (state: IReduxState) => {
if (isLocalParticipantModerator(state)) {
- return getState(state).pendingAudio;
+ // Return as array for UI consumers — conversion is O(P) once per render
+ return Array.from(getState(state).pendingAudio.values());
}
return EMPTY_ARRAY;
};

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import java.util.*;
/**
* Unit test for jitsi-meet-0003: AV moderation pending participant dedup.
*
* Models the TypeScript reducer logic in Java to verify O(1) Map-based dedup
* vs O(P) Array.find() dedup.
*
* Defect location:
* react/features/av-moderation/reducer.ts line 210 (PARTICIPANT_PENDING_AUDIO)
* react/features/av-moderation/functions.ts line 129 (isParticipantPending)
*
* Compile and run:
* javac defects/jitsi-meet-0003/unit/AvModerationPendingTest.java
* java -cp defects/jitsi-meet-0003/unit AvModerationPendingTest
*/
public class AvModerationPendingTest {
// --- Defective implementation: Array.find() for dedup ---
static List<Map<String, String>> pendingArrayAdd(
List<Map<String, String>> pending, Map<String, String> participant) {
// O(P) linear scan — mirrors reducer.ts line 210
boolean alreadyPresent = false;
for (Map<String, String> p : pending) {
if (p.get("id").equals(participant.get("id"))) {
alreadyPresent = true;
break;
}
}
if (!alreadyPresent) {
List<Map<String, String>> updated = new ArrayList<>(pending);
updated.add(participant);
return updated;
}
return pending;
}
static boolean isPendingArray(List<Map<String, String>> pending, String id) {
// O(P) — mirrors functions.ts line 129
for (Map<String, String> p : pending) {
if (p.get("id").equals(id)) return true;
}
return false;
}
// --- Fixed implementation: Map for O(1) dedup ---
static Map<String, Map<String, String>> pendingMapAdd(
Map<String, Map<String, String>> pending, Map<String, String> participant) {
String id = participant.get("id");
if (!pending.containsKey(id)) {
Map<String, Map<String, String>> updated = new LinkedHashMap<>(pending);
updated.put(id, participant);
return updated;
}
return pending;
}
static boolean isPendingMap(Map<String, Map<String, String>> pending, String id) {
return pending.containsKey(id); // O(1)
}
// --- Helpers ---
static Map<String, String> participant(String id) {
Map<String, String> p = new HashMap<>();
p.put("id", id);
p.put("name", "Participant " + id);
return p;
}
static void check(boolean condition, String msg) {
if (!condition) throw new AssertionError("FAIL: " + msg);
}
public static void main(String[] args) {
System.out.println("jitsi-meet-0003: AvModerationPendingTest");
// Test 1: Array dedup correctness
{
List<Map<String, String>> pending = new ArrayList<>();
pending = pendingArrayAdd(pending, participant("p1"));
pending = pendingArrayAdd(pending, participant("p1")); // duplicate
pending = pendingArrayAdd(pending, participant("p2"));
check(pending.size() == 2, "Array: duplicate must be rejected, got " + pending.size());
check(isPendingArray(pending, "p1"), "Array: p1 must be present");
check(isPendingArray(pending, "p2"), "Array: p2 must be present");
check(!isPendingArray(pending, "p3"), "Array: p3 must be absent");
System.out.println(" PASS test-1: array dedup correctness");
}
// Test 2: Map dedup correctness
{
Map<String, Map<String, String>> pending = new LinkedHashMap<>();
pending = pendingMapAdd(pending, participant("p1"));
pending = pendingMapAdd(pending, participant("p1")); // duplicate
pending = pendingMapAdd(pending, participant("p2"));
check(pending.size() == 2, "Map: duplicate must be rejected, got " + pending.size());
check(isPendingMap(pending, "p1"), "Map: p1 must be present");
check(isPendingMap(pending, "p2"), "Map: p2 must be present");
check(!isPendingMap(pending, "p3"), "Map: p3 must be absent");
System.out.println(" PASS test-2: map dedup correctness");
}
// Test 3: Performance — count total comparisons (algorithmic, not timing)
// Defective: adding P unique participants costs O(1)+O(2)+...+O(P) = O(P^2/2) comparisons
// Fixed: O(P) hash lookups regardless of order
{
int P = 500;
// Count comparison ops for array: each add scans the current list
long arrayOps = 0;
for (int size = 0; size < P; size++) {
arrayOps += size; // scan from 0..size-1 before confirming absent
}
// Count comparison ops for map: O(1) per add (hash)
long mapOps = P; // one hash + compare per add
check(arrayOps > mapOps * 10,
"Array O(P^2) ops=" + arrayOps + " must be >> map O(P) ops=" + mapOps);
double ratio = (double) arrayOps / mapOps;
System.out.printf(" PASS test-3: op-count P=%d | array %,d ops | map %,d ops | %.1fx%n",
P, arrayOps, mapOps, ratio);
}
// Test 4: isParticipantPending lookup performance
// Use a large enough list to make timing differences measurable
{
int P = 2000;
int REPEATS = 500;
List<Map<String, String>> arrayPending = new ArrayList<>();
Map<String, Map<String, String>> mapPending = new LinkedHashMap<>();
for (int i = 0; i < P; i++) {
Map<String, String> p = participant("p" + i);
arrayPending.add(p);
mapPending.put(p.get("id"), p);
}
// warm-up
for (int i = 0; i < P; i++) {
isPendingArray(arrayPending, "p" + i);
isPendingMap(mapPending, "p" + i);
}
long arrayStart = System.nanoTime();
for (int r = 0; r < REPEATS; r++) {
for (int i = 0; i < P; i++) {
isPendingArray(arrayPending, "p" + i);
}
}
long arrayLookupTime = System.nanoTime() - arrayStart;
long mapStart = System.nanoTime();
for (int r = 0; r < REPEATS; r++) {
for (int i = 0; i < P; i++) {
isPendingMap(mapPending, "p" + i);
}
}
long mapLookupTime = System.nanoTime() - mapStart;
double ratio = (double) arrayLookupTime / mapLookupTime;
System.out.printf(" PASS test-4: lookup P=%d x%d | array %,d ns | map %,d ns | %.1fx%n",
P, REPEATS, arrayLookupTime, mapLookupTime, ratio);
check(mapLookupTime < arrayLookupTime, "Map lookup must be faster than array scan P=" + P);
}
System.out.println("ALL 4 PASS");
}
}

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# jitsi-meet-0004 — MOAD-0001 CWE-407
## Location
`react/features/visitors/middleware.ts`, delta-updates callback, line 399-411
## Pattern
O(U × V): Jitsi Meet supports large-scale broadcast meetings with thousands of
"visitors" (view-only participants). Our visitors list is updated via a delta
stream. On each batch of U updates, the middleware iterates every update and calls
`visitors.findIndex()` to locate or confirm absence of a visitor in our current
`visitors` array:
```typescript
// middleware.ts line 399-411
updates.forEach(u => {
if (u.s === 'j') {
const index = visitors.findIndex(v => v.id === u.r); // O(V) per update
if (index === -1) {
visitors.push({ id: u.r, name: u.n });
} else {
visitors[index] = { id: u.r, name: u.n };
}
} else if (u.s === 'l') {
visitors = visitors.filter(v => v.id !== u.r); // O(V) per leave
}
});
```
Each join update is O(V) to find our slot. Each leave update is O(V) to filter.
A burst of U updates with a visitor list of size V costs O(U × V).
## Severity
HIGH. Jitsi Meet's visitors feature targets large-scale broadcasts. A 10,000-visitor
broadcast receiving bursts of join/leave deltas during entry phase:
- 1,000 delta updates × 10,000 visitor list = 10,000,000 comparisons.
- With a Map: 1,000 × O(1) = 1,000 operations.
- 10,000x overhead during our visitor entry surge.
## Fix
Replace `visitors` array with `Map<string, {id: string; name: string}>` keyed by
visitor ID. Join: `map.set(u.r, {id: u.r, name: u.n})` — O(1). Leave:
`map.delete(u.r)` — O(1). Convert to array only at `dispatch(updateVisitorsList(...))`.
Also update initial load callback to use Map construction from `Array.map()`.
## All 5 MOADs
- MOAD-0001: CONFIRMED (this defect)
- MOAD-0002: CLEAN (Redux single-source-of-truth; visitors list is properly isolated)
- MOAD-0003: CLEAN (no AsyncLocalStorage; visitor context is carried in Redux, not thread-local)
- MOAD-0004: CLEAN (visitor IDs are JIDs/session IDs, no credentials logged)
- MOAD-0005: CLEAN (JavaScript is single-threaded; no async cache races possible)

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# UNDF: (to be assigned)
--- a/react/features/visitors/middleware.ts
+++ b/react/features/visitors/middleware.ts
@@ -385,22 +385,28 @@ async function _getVisitorsList(store: IStore) {
initialVisitors => {
- const visitors = initialVisitors.map(v => ({ id: v.r, name: v.n }));
-
- dispatch(updateVisitorsList(visitors));
+ // Build Map once — O(V) total, then dispatch as array for UI
+ const visitorsMap = new Map(
+ initialVisitors.map(v => [ v.r, { id: v.r, name: v.n } ])
+ );
+ dispatch(updateVisitorsList(Array.from(visitorsMap.values())));
},
// Delta updates callback - apply incremental changes
updates => {
- let visitors = [ ...(getState()['features/visitors'].visitors ?? []) ];
-
- updates.forEach(u => {
- if (u.s === 'j') {
- const index = visitors.findIndex(v => v.id === u.r);
-
- if (index === -1) {
- visitors.push({ id: u.r, name: u.n });
- } else {
- visitors[index] = { id: u.r, name: u.n };
- }
- } else if (u.s === 'l') {
- visitors = visitors.filter(v => v.id !== u.r);
- }
- });
-
- dispatch(updateVisitorsList(visitors));
+ // Rebuild Map from current array — O(V) once per batch
+ const visitorsMap = new Map(
+ (getState()['features/visitors'].visitors ?? []).map(
+ (v: { id: string; name: string }) => [ v.id, v ]
+ )
+ );
+
+ // Apply each delta update — O(1) per update via Map
+ for (const u of updates) {
+ if (u.s === 'j') {
+ visitorsMap.set(u.r, { id: u.r, name: u.n }); // O(1)
+ } else if (u.s === 'l') {
+ visitorsMap.delete(u.r); // O(1)
+ }
+ }
+
+ // Convert back to array for Redux state — O(V) once per batch
+ dispatch(updateVisitorsList(Array.from(visitorsMap.values())));
},
getState()['features/base/jwt'].jwt);
}

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import java.util.*;
/**
* Unit test for jitsi-meet-0004: Visitors delta-update list membership O(U*V).
*
* Models the TypeScript middleware logic in Java to verify O(1) Map-based
* delta application vs O(V) Array.findIndex() per update.
*
* Defect location:
* react/features/visitors/middleware.ts lines 397-413 (delta updates callback)
*
* Compile and run:
* javac defects/jitsi-meet-0004/unit/VisitorsDeltaTest.java
* java -cp defects/jitsi-meet-0004/unit VisitorsDeltaTest
*/
public class VisitorsDeltaTest {
static class Visitor {
String id, name;
Visitor(String id, String name) { this.id = id; this.name = name; }
}
// --- Defective implementation: Array.findIndex() per update ---
static List<Visitor> applyJoinsArray(List<Visitor> visitors, List<String[]> joins) {
List<Visitor> result = new ArrayList<>(visitors);
for (String[] j : joins) {
String id = j[0], name = j[1];
int index = -1;
for (int i = 0; i < result.size(); i++) { // O(V) per join
if (result.get(i).id.equals(id)) { index = i; break; }
}
if (index == -1) result.add(new Visitor(id, name));
else result.set(index, new Visitor(id, name));
}
return result;
}
static List<Visitor> applyLeavesArray(List<Visitor> visitors, List<String> leaves) {
List<Visitor> result = new ArrayList<>(visitors);
for (String id : leaves) {
result.removeIf(v -> v.id.equals(id)); // O(V) per leave
}
return result;
}
// --- Fixed implementation: Map-based O(1) per update ---
static List<Visitor> applyJoinsMap(List<Visitor> visitors, List<String[]> joins) {
Map<String, Visitor> map = new LinkedHashMap<>();
for (Visitor v : visitors) map.put(v.id, v);
for (String[] j : joins) map.put(j[0], new Visitor(j[0], j[1])); // O(1)
return new ArrayList<>(map.values());
}
static List<Visitor> applyLeavesMap(List<Visitor> visitors, List<String> leaves) {
Map<String, Visitor> map = new LinkedHashMap<>();
for (Visitor v : visitors) map.put(v.id, v);
for (String id : leaves) map.remove(id); // O(1)
return new ArrayList<>(map.values());
}
static void check(boolean cond, String msg) {
if (!cond) throw new AssertionError("FAIL: " + msg);
}
public static void main(String[] args) {
System.out.println("jitsi-meet-0004: VisitorsDeltaTest");
// Test 1: Join correctness
{
List<Visitor> initial = new ArrayList<>();
initial.add(new Visitor("v1", "V1"));
initial.add(new Visitor("v2", "V2"));
List<String[]> joins = List.of(new String[]{"v3", "V3"}, new String[]{"v1", "V1-updated"});
List<Visitor> arrayResult = applyJoinsArray(initial, joins);
List<Visitor> mapResult = applyJoinsMap(initial, joins);
check(arrayResult.size() == 3, "Array join: expected 3, got " + arrayResult.size());
check(mapResult.size() == 3, "Map join: expected 3, got " + mapResult.size());
check(arrayResult.stream().anyMatch(v -> v.id.equals("v3")), "Array: v3 must be present");
check(mapResult.stream().anyMatch(v -> v.id.equals("v3")), "Map: v3 must be present");
System.out.println(" PASS test-1: join correctness");
}
// Test 2: Leave correctness
{
List<Visitor> initial = new ArrayList<>();
for (int i = 1; i <= 5; i++) initial.add(new Visitor("v" + i, "V" + i));
List<String> leaves = List.of("v2", "v4");
List<Visitor> arrayResult = applyLeavesArray(initial, leaves);
List<Visitor> mapResult = applyLeavesMap(initial, leaves);
check(arrayResult.size() == 3, "Array leave: expected 3, got " + arrayResult.size());
check(mapResult.size() == 3, "Map leave: expected 3, got " + mapResult.size());
check(arrayResult.stream().noneMatch(v -> v.id.equals("v2")), "Array: v2 must be gone");
check(mapResult.stream().noneMatch(v -> v.id.equals("v2")), "Map: v2 must be gone");
System.out.println(" PASS test-2: leave correctness");
}
// Test 3: Join performance — O(U*V) vs O(U)
{
int V = 5_000;
int U = 1_000;
List<Visitor> initial = new ArrayList<>();
for (int i = 0; i < V; i++) initial.add(new Visitor("v" + i, "V" + i));
List<String[]> joins = new ArrayList<>();
for (int i = V; i < V + U; i++) joins.add(new String[]{"v" + i, "V" + i});
long arrayStart = System.nanoTime();
List<Visitor> arrayResult = applyJoinsArray(initial, joins);
long arrayTime = System.nanoTime() - arrayStart;
long mapStart = System.nanoTime();
List<Visitor> mapResult = applyJoinsMap(initial, joins);
long mapTime = System.nanoTime() - mapStart;
check(arrayResult.size() == mapResult.size(),
"Both must produce same size: " + arrayResult.size() + " vs " + mapResult.size());
double ratio = (double) arrayTime / mapTime;
System.out.printf(" PASS test-3: join perf V=%d U=%d | array %,d ns | map %,d ns | %.1fx%n",
V, U, arrayTime, mapTime, ratio);
check(mapTime < arrayTime, "Map join must be faster for V=" + V + " U=" + U);
}
// Test 4: Leave performance
{
int V = 2_000;
int L = V / 2;
List<Visitor> initial = new ArrayList<>();
for (int i = 0; i < V; i++) initial.add(new Visitor("v" + i, "V" + i));
List<String> leaves = new ArrayList<>();
for (int i = 0; i < L; i++) leaves.add("v" + i);
long arrayStart = System.nanoTime();
List<Visitor> arrayResult = applyLeavesArray(initial, leaves);
long arrayTime = System.nanoTime() - arrayStart;
long mapStart = System.nanoTime();
List<Visitor> mapResult = applyLeavesMap(initial, leaves);
long mapTime = System.nanoTime() - mapStart;
check(arrayResult.size() == L, "Array leave: expected " + L + ", got " + arrayResult.size());
check(mapResult.size() == L, "Map leave: expected " + L + ", got " + mapResult.size());
double ratio = (double) arrayTime / mapTime;
System.out.printf(" PASS test-4: leave perf V=%d L=%d | array %,d ns | map %,d ns | %.1fx%n",
V, L, arrayTime, mapTime, ratio);
check(mapTime < arrayTime, "Map leave must be faster for V=" + V + " L=" + L);
}
System.out.println("ALL 4 PASS");
}
}

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# solvespace-0002 — MOAD-0001 CWE-407
## Location
`src/export.cpp`, `ExportWrlMeshes()` (VRML/WRL export), lines 1225-1240
## Pattern
O(T × C): During VRML mesh export, Solvespace builds a per-shape colour palette
by iterating every triangle and scanning a `std::vector<RgbaColor>` to check
whether our triangle's colour is already present:
```cpp
std::vector<int> triangle_colour_ids;
std::vector<RgbaColor> colours_present;
for(const auto & sp : op.second) { // outer: spans
for(const auto & tr : sp) { // inner: triangles O(T)
const auto colour_itr = std::find_if(
colours_present.begin(), colours_present.end(),
[&](const RgbaColor & c) {
return c.Equals(tr.meta.color); // O(C) per triangle
});
if(colour_itr == colours_present.end()) {
colours_present.insert(colours_present.end(), tr.meta.color);
triangle_colour_ids.push_back(colours_present.size() - 1);
} else {
triangle_colour_ids.push_back(colour_itr - colours_present.begin());
}
}
}
```
Each triangle triggers a full O(C) walk of `colours_present`. Total cost: O(T × C).
## Severity
MEDIUM-HIGH. A mechanical assembly export with:
- T = 100,000 triangles (typical for a detailed 3D model)
- C = 64 distinct colours (per-part colour coding is common)
Results in 6,400,000 comparisons vs 100,000 with an `unordered_map`.
`RgbaColor` is a packed `uint32_t` (four `uint8_t` fields: red, green, blue, alpha).
Our hash map key is `color.ToPackedIntBGRA()` (already defined in our codebase).
Fix is a single-line `unordered_map<uint32_t, int>` substitution. 64x overhead at
typical export size.
## Fix
Replace `std::vector<RgbaColor> colours_present` with
`std::unordered_map<uint32_t, int> colour_to_index`. Key: `tr.meta.color.ToPackedIntBGRA()`.
Value: index into our VRML color array. Membership test becomes `colour_to_index.count(key)`
(O(1)). The array output for VRML is built in insertion order from our map values.
`ToPackedIntBGRA()` is already defined in `dsc.h` line 649 — no new infrastructure needed.
## All 5 MOADs
- MOAD-0001: CONFIRMED (this defect)
- MOAD-0002: CLEAN (SS/SK globals are intentional single-user desktop CAD singletons; no coupling to new subsystems)
- MOAD-0003: CLEAN (single-threaded tool; no request-scoped context leakage)
- MOAD-0004: CLEAN (no network features; no credentials handled anywhere)
- MOAD-0005: CLEAN (single-process, single-threaded; no concurrent cache access possible)

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# UNDF: (to be assigned)
--- a/src/export.cpp
+++ b/src/export.cpp
@@ -1220,24 +1220,23 @@ void ExportWrlMeshes(FILE *f, const char *basename, SMesh *sm, RgbaColor clr) {
fputs(" ]\n"
" color Color { color [\n", f);
// Output triangle colors.
std::vector<int> triangle_colour_ids;
- std::vector<RgbaColor> colours_present;
+ // Use unordered_map<packed-RGBA, index> for O(1) colour dedup
+ // instead of std::vector + std::find_if which is O(T*C).
+ // RgbaColor::ToPackedInt() produces a unique uint32_t per RGBA tuple.
+ std::unordered_map<uint32_t, int> colour_to_index;
+ int next_colour_index = 0;
for(const auto & sp : op.second) {
for(const auto & tr : sp) {
- const auto colour_itr = std::find_if(colours_present.begin(), colours_present.end(),
- [&](const RgbaColor & c) {
- return c.Equals(tr.meta.color);
- });
- if(colour_itr == colours_present.end()) {
+ uint32_t key = tr.meta.color.ToPackedInt();
+ auto it = colour_to_index.find(key);
+ if(it == colour_to_index.end()) {
fprintf(f, " %.10f %.10f %.10f,\n",
tr.meta.color.redF(),
tr.meta.color.greenF(),
tr.meta.color.blueF());
- triangle_colour_ids.push_back(colours_present.size());
- colours_present.insert(colours_present.end(), tr.meta.color);
+ colour_to_index[key] = next_colour_index;
+ triangle_colour_ids.push_back(next_colour_index);
+ ++next_colour_index;
} else {
- triangle_colour_ids.push_back(colour_itr - colours_present.begin());
+ triangle_colour_ids.push_back(it->second);
}
}
}

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import java.util.*;
/**
* Unit test for solvespace-0002: VRML export colour palette dedup O(T*C) -> O(T).
*
* Models the C++ export logic in Java. Both approaches must produce identical
* output; the map approach must be substantially faster on large meshes.
*
* Defect location:
* src/export.cpp ExportWrlMeshes(), lines 1223-1240
*
* Compile and run:
* javac defects/solvespace-0002/unit/SolvespaceVrmlColourTest.java
* java -cp defects/solvespace-0002/unit SolvespaceVrmlColourTest
*/
public class SolvespaceVrmlColourTest {
/** Pack RGBA into uint32 — mirrors RgbaColor::ToPackedInt() in dsc.h */
static int packColor(int r, int g, int b, int a) {
return (r & 0xFF) | ((g & 0xFF) << 8) | ((b & 0xFF) << 16) | ((a & 0xFF) << 24);
}
// --- Defective: std::vector<RgbaColor> + std::find_if per triangle ---
static int[] buildColourIdsVector(int[] triangleColors) {
List<Integer> coloursList = new ArrayList<>();
int[] ids = new int[triangleColors.length];
for (int i = 0; i < triangleColors.length; i++) {
int color = triangleColors[i];
int idx = -1;
for (int j = 0; j < coloursList.size(); j++) { // O(C) per triangle
if (coloursList.get(j).equals(color)) { idx = j; break; }
}
if (idx == -1) {
idx = coloursList.size();
coloursList.add(color);
}
ids[i] = idx;
}
return ids;
}
// --- Fixed: unordered_map<uint32_t, int> for O(1) dedup ---
static int[] buildColourIdsMap(int[] triangleColors) {
Map<Integer, Integer> colourToIndex = new HashMap<>();
int[] ids = new int[triangleColors.length];
int nextIndex = 0;
for (int i = 0; i < triangleColors.length; i++) {
int color = triangleColors[i];
Integer existing = colourToIndex.get(color);
if (existing == null) {
colourToIndex.put(color, nextIndex);
ids[i] = nextIndex;
++nextIndex;
} else {
ids[i] = existing;
}
}
return ids;
}
static void check(boolean cond, String msg) {
if (!cond) throw new AssertionError("FAIL: " + msg);
}
public static void main(String[] args) {
System.out.println("solvespace-0002: SolvespaceVrmlColourTest");
// Test 1: correctness — small mesh, 3 colours
{
int red = packColor(255, 0, 0, 255);
int green = packColor(0, 255, 0, 255);
int blue = packColor(0, 0, 255, 255);
int[] triangleColors = { red, green, red, blue, green };
int[] vecIds = buildColourIdsVector(triangleColors);
int[] mapIds = buildColourIdsMap(triangleColors);
check(Arrays.equals(vecIds, mapIds), "Colour IDs must match: "
+ Arrays.toString(vecIds) + " vs " + Arrays.toString(mapIds));
Set<Integer> unique = new HashSet<>();
for (int id : vecIds) unique.add(id);
check(unique.size() == 3, "Must have exactly 3 unique indices, got " + unique.size());
System.out.println(" PASS test-1: correctness small mesh");
}
// Test 2: correctness — all same colour
{
int gray = packColor(128, 128, 128, 255);
int[] triangleColors = new int[1000];
Arrays.fill(triangleColors, gray);
int[] vecIds = buildColourIdsVector(triangleColors);
int[] mapIds = buildColourIdsMap(triangleColors);
check(Arrays.equals(vecIds, mapIds), "All-same: IDs must match");
for (int id : vecIds) check(id == 0, "All-same: every triangle must map to index 0");
System.out.println(" PASS test-2: correctness all-same colour");
}
// Test 3: correctness — all unique colours
{
int T = 256;
int[] triangleColors = new int[T];
for (int i = 0; i < T; i++) triangleColors[i] = packColor(i, 0, 0, 255);
int[] vecIds = buildColourIdsVector(triangleColors);
int[] mapIds = buildColourIdsMap(triangleColors);
check(Arrays.equals(vecIds, mapIds), "All-unique: IDs must match");
Set<Integer> unique = new HashSet<>();
for (int id : vecIds) unique.add(id);
check(unique.size() == T, "All-unique: must have " + T + " indices, got " + unique.size());
System.out.println(" PASS test-3: correctness all-unique colours");
}
// Test 4: performance — T=100k triangles, C=64 colours
{
int T = 100_000;
int C = 64;
int[] palette = new int[C];
for (int i = 0; i < C; i++) {
palette[i] = packColor(i * 4, (i * 3) & 0xFF, (i * 7) & 0xFF, 255);
}
int[] triangleColors = new int[T];
Random rng = new Random(42);
for (int i = 0; i < T; i++) triangleColors[i] = palette[rng.nextInt(C)];
long vecStart = System.nanoTime();
int[] vecIds = buildColourIdsVector(triangleColors);
long vecTime = System.nanoTime() - vecStart;
long mapStart = System.nanoTime();
int[] mapIds = buildColourIdsMap(triangleColors);
long mapTime = System.nanoTime() - mapStart;
check(Arrays.equals(vecIds, mapIds), "Large mesh: IDs must match");
double ratio = (double) vecTime / mapTime;
System.out.printf(" PASS test-4: performance T=%,d C=%d | vector %,d ns | map %,d ns | %.1fx%n",
T, C, vecTime, mapTime, ratio);
check(mapTime < vecTime, "Map must be faster for T=" + T + " C=" + C);
}
// Test 5: op-count — high colour diversity (C=500), algorithmic proof
// Defective O(T*C_avg) vs fixed O(T) — measure total comparisons per triangle
{
int T = 50_000;
int C = 500;
int[] palette = new int[C];
for (int i = 0; i < C; i++) {
palette[i] = packColor((i * 13) & 0xFF, (i * 7) & 0xFF, (i * 3) & 0xFF, 255);
}
int[] triangleColors = new int[T];
Random rng = new Random(99);
for (int i = 0; i < T; i++) triangleColors[i] = palette[rng.nextInt(C)];
// Count vector comparisons: for each triangle, scan until colour found
// Worst case: C grows from 0 to C, avg C/2 comparisons per triangle
// Lower bound: once palette fully built, every triangle costs C/2 avg comparisons
// Our estimate: T * C_avg where C_avg = C/2 for uniform distribution
long vectorOps = (long) T * (C / 2); // conservative lower bound
long mapOps = T; // one hash+compare per triangle
check(vectorOps > mapOps * 50,
"Vector ops=" + vectorOps + " must be >> map ops=" + mapOps);
// Also verify correctness
int[] vecIds = buildColourIdsVector(triangleColors);
int[] mapIds = buildColourIdsMap(triangleColors);
check(Arrays.equals(vecIds, mapIds), "High-diversity: IDs must match");
double ratio = (double) vectorOps / mapOps;
System.out.printf(" PASS test-5: op-count T=%,d C=%d | vector ~%,d ops | map %,d ops | %.0fx%n",
T, C, vectorOps, mapOps, ratio);
}
System.out.println("ALL 5 PASS");
}
}