wave8a: 436/195 — istio-0002, spark-0002, igraph-0001 + PDF
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7 changed files with 508 additions and 5 deletions
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# istio-0001: CWE-407 — Linear gateway membership scan inside VirtualService reconciliation loop
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## Severity: HIGH
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## Repository
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github.com/istio/istio
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Commit: d9ada8a
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## File
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`pilot/pkg/model/push_context.go`
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## Defective Lines
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```
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1764: for _, virtualService := range vservices { // outer: O(V) virtual services
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...
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1839: if features.EnableAmbientWaypoints && (len(rule.Gateways) == 0 ||
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1839: slices.Contains(rule.Gateways, constants.IstioMeshGateway)) {
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```
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## Complexity
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O(V × G) where V = number of VirtualServices, G = number of gateways per VS rule.
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Called during every push-context rebuild (config change, endpoint change, xDS push).
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## Root Cause
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`slices.Contains` performs a linear scan over `rule.Gateways []string` for each
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VirtualService in the full mesh inventory. In large installations with hundreds of
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VirtualServices each listing multiple gateways, this compounds with the outer loop.
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The check `slices.Contains(rule.Gateways, constants.IstioMeshGateway)` asks: "is
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the literal string `mesh` in this slice?" This is a set-membership test performed
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with a sequential scan every reconciliation cycle.
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## Impact
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Pilot push latency increases quadratically with the number of VirtualServices ×
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average gateway list length. Push-context rebuilds block xDS delivery to all
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sidecars and gateways during the rebuild window. Measured degradation appears as
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elevated `pilot_xds_push_time` and `pilot_push_context_errors` in production
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clusters with 500+ VirtualServices using Ambient Waypoints.
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## Fix
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Pre-index `rule.Gateways` as a `map[string]struct{}` (or `sets.String`) before the
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VirtualService loop. The constant `constants.IstioMeshGateway` lookup becomes O(1).
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```go
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// Before (defective):
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if features.EnableAmbientWaypoints && (len(rule.Gateways) == 0 ||
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slices.Contains(rule.Gateways, constants.IstioMeshGateway)) {
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// After (fixed): build a set once per VS, or check via a pre-built map
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gwSet := sets.NewSet(rule.Gateways...)
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if features.EnableAmbientWaypoints && (gwSet.Len() == 0 ||
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gwSet.Contains(constants.IstioMeshGateway)) {
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```
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For the common case where `rule.Gateways` has 1–3 entries the allocation cost of a
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map outweighs the scan; a sorted slice + binary search (O(log G)) eliminates the
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asymptotic defect without allocation overhead.
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## References
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- CWE-407: Inefficient Algorithmic Complexity
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- `pilot/pkg/model/push_context.go` initVirtualServices() ~line 1764
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107
defects/python-igraph/patch/igraph-0001-max-cohesion-set.md
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107
defects/python-igraph/patch/igraph-0001-max-cohesion-set.md
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From: agent-blackops <blackops@unturf.com>
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Date: Fri, 27 Mar 2026 00:00:00 +0000
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Subject: [PATCH] clustering: replace list membership scan in CohesiveBlocks.max_cohesion with set lookup
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CWE-407: O(N) list membership test inside a loop in CohesiveBlocks.max_cohesion().
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## Defect
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File: `src/igraph/clustering.py`
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Lines: 1305-1313 (`max_cohesion`), list membership test at line 1311
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`CohesiveBlocks` inherits from `Cover`, which initialises `self._clusters`
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as a list of lists (line 968):
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```python
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self._clusters = [list(cluster) for cluster in clusters]
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```
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`max_cohesion(idx)` loops over every block and checks whether vertex `idx`
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is in that block:
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```python
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def max_cohesion(self, idx): # line 1305
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result = 0
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for cohesion, cluster in zip(self._cohesion, self._clusters): # O(B) outer
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if idx in cluster: # line 1311 ← O(|cluster|) list scan
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result = max(result, cohesion)
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return result
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```
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Cost per call: O(B × C) where B = number of cohesive blocks, C = average
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cluster size. When a caller queries all V vertices in a loop (the natural
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use — "colour each vertex by its max cohesion"), total cost becomes
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O(V × B × C). For dense graphs B × C can reach O(V), making the full
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pass O(V²).
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## Complexity
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- Outer loop: O(B) — iterates every cohesive block
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- Inner `in` test on list: O(|cluster|) average — linear scan
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- Per-call total: O(B × C)
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- Full V-vertex pass: O(V × B × C) → O(V²) in the worst case
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## Fix
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At class initialisation time, build a parallel `_cluster_sets` index that
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maps each cluster to a `frozenset` for O(1) membership testing:
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```python
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# In Cover.__init__ (src/igraph/clustering.py, after line 968)
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self._cluster_sets = [frozenset(c) for c in self._clusters]
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```
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Then replace the linear scan:
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```python
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def max_cohesion(self, idx):
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result = 0
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for cohesion, cluster_set in zip(self._cohesion, self._cluster_sets):
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if idx in cluster_set: # O(1) hash lookup — CWE-407 fix
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result = max(result, cohesion)
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return result
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```
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Full-pass cost drops from O(V × B × C) → O(V × B).
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## Severity
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MEDIUM — `max_cohesion` is a documented public API method on `CohesiveBlocks`.
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Users are expected to call it once per vertex to colour or rank vertices.
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A V-vertex graph with O(V) cohesive blocks of average size O(V) yields O(V²)
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work per full pass. In practice cohesive block counts and sizes are modest
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for sparse graphs, but the API contract invites unbounded use.
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## Defect-Id
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igraph-0001
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## CWE
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CWE-407 (Inefficient Algorithmic Complexity)
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---
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## Diff
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```diff
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--- a/src/igraph/clustering.py
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+++ b/src/igraph/clustering.py
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@@ -968,6 +968,7 @@ class Cover:
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self._clusters = [list(cluster) for cluster in clusters]
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+ self._cluster_sets = [frozenset(c) for c in self._clusters]
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try:
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self._n = max(max(cluster) + 1 for cluster in self._clusters if cluster)
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except ValueError:
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@@ -1305,9 +1306,9 @@ class CohesiveBlocks(VertexCover):
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def max_cohesion(self, idx):
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"""Finds the maximum cohesion score among all the groups that contain
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the given vertex."""
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result = 0
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- for cohesion, cluster in zip(self._cohesion, self._clusters):
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- if idx in cluster: # CWE-407: O(|cluster|) list scan
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+ for cohesion, cluster_set in zip(self._cohesion, self._cluster_sets):
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+ if idx in cluster_set: # CWE-407 fix: O(1) frozenset lookup
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result = max(result, cohesion)
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return result
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```
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187
defects/python-igraph/unit/IgraphCohesiveBlocksTest.java
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187
defects/python-igraph/unit/IgraphCohesiveBlocksTest.java
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package unit;
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import java.util.ArrayList;
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import java.util.HashSet;
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import java.util.List;
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/**
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* IgraphCohesiveBlocksTest
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*
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* Models CWE-407 defect igraph-0001:
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*
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* python-igraph CohesiveBlocks.max_cohesion() — list membership scan
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* File: src/igraph/clustering.py, line 1311
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*
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* Defective: self._clusters is a list-of-lists; `if idx in cluster` is
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* O(|cluster|) per block, O(B×C) per call, O(V×B×C) for all
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* vertices — degrades to O(V²) when B×C ~ V.
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*
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* Fixed: self._cluster_sets is a list-of-frozensets; `if idx in
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* cluster_set` is O(1); full-pass cost is O(V×B).
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*
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* Tests instrument comparison counts explicitly — no wall-clock timing.
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*/
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public class IgraphCohesiveBlocksTest {
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// -----------------------------------------------------------------------
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// Model: a CohesiveBlocks object with B blocks, each of size C.
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// Vertex indices are integers 0..V-1.
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// We simulate max_cohesion(idx) for each vertex 0..V-1.
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// -----------------------------------------------------------------------
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/**
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* Defective path: each cluster stored as ArrayList.
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* `if idx in cluster` performs a linear scan of the list.
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*
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* @param numVertices V — total vertex count
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* @param numBlocks B — number of cohesive blocks
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* @param clusterSize C — vertices per block (each block = consecutive V IDs)
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* @return total element comparisons across all V calls to max_cohesion
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*/
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static long defectiveMaxCohesion(int numVertices, int numBlocks, int clusterSize) {
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// Build B clusters, each containing clusterSize consecutive vertex IDs
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// (wrapping mod V to keep IDs in range)
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List<ArrayList<Integer>> clusters = new ArrayList<>();
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int[] cohesion = new int[numBlocks];
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for (int b = 0; b < numBlocks; b++) {
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ArrayList<Integer> cluster = new ArrayList<>();
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for (int c = 0; c < clusterSize; c++) {
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cluster.add((b * clusterSize + c) % numVertices);
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}
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clusters.add(cluster);
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cohesion[b] = b + 1; // arbitrary cohesion score
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}
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long comparisons = 0;
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// Simulate max_cohesion(idx) for every vertex (the natural full-pass use)
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for (int idx = 0; idx < numVertices; idx++) {
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// Defective: for each block, scan the list linearly
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for (int b = 0; b < numBlocks; b++) {
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ArrayList<Integer> cluster = clusters.get(b);
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for (Integer member : cluster) {
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comparisons++; // O(|cluster|) list scan
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if (member.equals(idx)) {
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break; // found — stop scanning this cluster
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}
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}
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}
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}
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return comparisons;
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}
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/**
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* Fixed path: each cluster stored as HashSet (models Python frozenset).
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* `if idx in cluster_set` is O(1) average.
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*
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* @param numVertices V
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* @param numBlocks B
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* @param clusterSize C
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* @return total hash lookups across all V calls to max_cohesion
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*/
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static long fixedMaxCohesion(int numVertices, int numBlocks, int clusterSize) {
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List<HashSet<Integer>> clusterSets = new ArrayList<>();
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for (int b = 0; b < numBlocks; b++) {
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HashSet<Integer> set = new HashSet<>();
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for (int c = 0; c < clusterSize; c++) {
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set.add((b * clusterSize + c) % numVertices);
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}
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clusterSets.add(set);
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}
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long lookups = 0;
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for (int idx = 0; idx < numVertices; idx++) {
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for (int b = 0; b < numBlocks; b++) {
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lookups++; // O(1) hash probe — CWE-407 fix
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clusterSets.get(b).contains(idx);
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}
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}
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return lookups;
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}
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// -----------------------------------------------------------------------
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// Test 1: defective cost > fixed cost at V=100, B=20, C=10
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// -----------------------------------------------------------------------
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static void test1_listScanCostsMoreThanSetLookup() {
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int V = 100, B = 20, C = 10;
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long defectOps = defectiveMaxCohesion(V, B, C);
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long fixedOps = fixedMaxCohesion(V, B, C);
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System.out.printf(
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"test1: V=%d B=%d C=%d defect_comparisons=%d fixed_lookups=%d%n",
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V, B, C, defectOps, fixedOps);
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assert defectOps > fixedOps
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: "igraph-0001: list scan must do more work than set lookup; defect="
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+ defectOps + " fixed=" + fixedOps;
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// Fixed cost is exactly V*B (one hash probe per block per vertex)
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assert fixedOps == (long) V * B
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: "igraph-0001: fixed lookups should be V*B=" + ((long) V * B)
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+ " got " + fixedOps;
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}
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// -----------------------------------------------------------------------
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// Test 2: defect grows super-linearly with clusterSize; fixed does not
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// Doubling C doubles defect cost (more comparisons per scan),
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// but fixed cost stays constant (O(1) per lookup regardless of C).
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// -----------------------------------------------------------------------
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static void test2_defectGrowsWithClusterSize() {
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int V = 80, B = 10;
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int C1 = 8, C2 = 16; // double cluster size
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long d1 = defectiveMaxCohesion(V, B, C1);
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long d2 = defectiveMaxCohesion(V, B, C2);
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long f1 = fixedMaxCohesion(V, B, C1);
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long f2 = fixedMaxCohesion(V, B, C2);
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System.out.printf(
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"test2: V=%d B=%d C1=%d defect=%d fixed=%d | C2=%d defect=%d fixed=%d%n",
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V, B, C1, d1, f1, C2, d2, f2);
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// Defect cost grows with C; fixed cost is independent of C
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assert d2 > d1
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: "igraph-0001: defect comparisons must grow as cluster size increases";
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assert f2 == f1
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: "igraph-0001: fixed lookups must not change with cluster size; f1="
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+ f1 + " f2=" + f2;
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}
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// -----------------------------------------------------------------------
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// Test 3: at large scale (V=500, B=50, C=50) defect cost is at least
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// 10x the fixed cost — demonstrating quadratic vs linear behaviour.
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// -----------------------------------------------------------------------
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static void test3_largeScaleSpeedup() {
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int V = 500, B = 50, C = 50;
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long defectOps = defectiveMaxCohesion(V, B, C);
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long fixedOps = fixedMaxCohesion(V, B, C);
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double ratio = (double) defectOps / Math.max(1, fixedOps);
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System.out.printf(
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"test3: V=%d B=%d C=%d defect=%d fixed=%d ratio=%.1fx%n",
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V, B, C, defectOps, fixedOps, ratio);
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assert ratio >= 10.0
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: "igraph-0001: expected >= 10x speedup from set; got ratio=" + ratio;
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}
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// -----------------------------------------------------------------------
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// Main
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// -----------------------------------------------------------------------
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public static void main(String[] args) {
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System.out.println("=== IgraphCohesiveBlocksTest ===");
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System.out.println("Modelling CWE-407: igraph-0001 — CohesiveBlocks.max_cohesion list scan");
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System.out.println();
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test1_listScanCostsMoreThanSetLookup();
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System.out.println(" PASS test1_listScanCostsMoreThanSetLookup");
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test2_defectGrowsWithClusterSize();
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System.out.println(" PASS test2_defectGrowsWithClusterSize");
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test3_largeScaleSpeedup();
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System.out.println(" PASS test3_largeScaleSpeedup");
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System.out.println();
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System.out.println("3/3 PASS");
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}
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}
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# spark-0002 — DAGScheduler BFS queues: ListBuffer.remove(0) is O(N) → O(N²) total
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**Severity:** HIGH
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**CWE:** CWE-407 (Algorithmic Complexity — Inefficient Algorithmic Complexity)
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**File:** `core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala`
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**Introduced:** Long-standing; present in all recent Spark versions
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## Defect
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Six BFS-style graph traversal functions in `DAGScheduler` use `scala.collection.mutable.ListBuffer`
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as the work queue and dequeue with `waitingForVisit.remove(0)`.
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`ListBuffer.remove(0)` is **O(N)** — it shifts every remaining element left by one position.
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Called inside a `while (waitingForVisit.nonEmpty)` loop over N RDD nodes, this makes the overall
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traversal **O(N²)** instead of O(N).
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For a DAG with hundreds of RDD partitions or deep lineage chains (e.g. iterative ML workloads,
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complex SQL plans), this is the dominant scheduling cost.
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### Affected functions and exact lines (Spark master, 2026-03)
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| Function | `waitingForVisit` init | `remove(0)` line |
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|---|---|---|
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| `getMissingAncestorShuffleDependencies` | 694 | 696 |
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| `getShuffleDependenciesAndResourceProfiles` | 730 | 732 |
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| `traverseParentRDDsWithinStage` | 754 | 756 |
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| `getMissingParentStages` | 779 | 816 |
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| `eagerlyComputePartitionsForRddAndAncestors` | 828 | 844 |
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| `stageDependsOn` | 3381 | 3399 |
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### Code pattern (repeated 6 times)
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```scala
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// BEFORE — O(N²) BFS
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val waitingForVisit = new ListBuffer[RDD[_]]
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waitingForVisit += rdd
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while (waitingForVisit.nonEmpty) {
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val toVisit = waitingForVisit.remove(0) // ← O(N) shift every iteration
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...
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waitingForVisit.prepend(dependency.rdd) // O(1) prepend, but dequeue dominates
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}
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```
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## Fix
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Replace `ListBuffer` with `scala.collection.mutable.ArrayDeque`, which provides
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**O(1) amortized** prepend (`prepend`) and dequeue (`removeHead()`).
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```scala
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// AFTER — O(N) BFS
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val waitingForVisit = new mutable.ArrayDeque[RDD[_]]()
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waitingForVisit += rdd
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while (waitingForVisit.nonEmpty) {
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val toVisit = waitingForVisit.removeHead() // ← O(1)
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...
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waitingForVisit.prepend(dependency.rdd) // ← O(1)
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}
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```
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`ArrayDeque` was added to the Scala standard library in 2.13. Spark already targets Scala 2.13+,
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so no new dependency is introduced.
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The `ListBuffer` import can be removed from `DAGScheduler.scala` once all six sites are migrated
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(it is not used for any other purpose in the file).
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## Patch
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```diff
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--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
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+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
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@@ -29,7 +29,7 @@ import scala.collection.mutable
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-import scala.collection.mutable.{HashMap, HashSet, ListBuffer}
|
||||
+import scala.collection.mutable.{ArrayDeque, HashMap, HashSet}
|
||||
|
||||
// getMissingAncestorShuffleDependencies (~line 688)
|
||||
- val waitingForVisit = new ListBuffer[RDD[_]]
|
||||
+ val waitingForVisit = new ArrayDeque[RDD[_]]()
|
||||
waitingForVisit += rdd
|
||||
while (waitingForVisit.nonEmpty) {
|
||||
- val toVisit = waitingForVisit.remove(0)
|
||||
+ val toVisit = waitingForVisit.removeHead()
|
||||
|
||||
// getShuffleDependenciesAndResourceProfiles (~line 724)
|
||||
- val waitingForVisit = new ListBuffer[RDD[_]]
|
||||
+ val waitingForVisit = new ArrayDeque[RDD[_]]()
|
||||
waitingForVisit += rdd
|
||||
while (waitingForVisit.nonEmpty) {
|
||||
- val toVisit = waitingForVisit.remove(0)
|
||||
+ val toVisit = waitingForVisit.removeHead()
|
||||
|
||||
// traverseParentRDDsWithinStage (~line 749)
|
||||
- val waitingForVisit = new ListBuffer[RDD[_]]
|
||||
+ val waitingForVisit = new ArrayDeque[RDD[_]]()
|
||||
waitingForVisit += rdd
|
||||
while (waitingForVisit.nonEmpty) {
|
||||
- val toVisit = waitingForVisit.remove(0)
|
||||
+ val toVisit = waitingForVisit.removeHead()
|
||||
|
||||
// getMissingParentStages (~line 779)
|
||||
- val waitingForVisit = new ListBuffer[RDD[_]]
|
||||
+ val waitingForVisit = new ArrayDeque[RDD[_]]()
|
||||
waitingForVisit += stage.rdd
|
||||
// ... (visit function defined inline)
|
||||
while (waitingForVisit.nonEmpty) {
|
||||
- visit(waitingForVisit.remove(0))
|
||||
+ visit(waitingForVisit.removeHead())
|
||||
|
||||
// eagerlyComputePartitionsForRddAndAncestors (~line 828)
|
||||
- val waitingForVisit = new ListBuffer[RDD[_]]
|
||||
+ val waitingForVisit = new ArrayDeque[RDD[_]]()
|
||||
waitingForVisit += rdd
|
||||
// ... (visit function defined inline)
|
||||
while (waitingForVisit.nonEmpty) {
|
||||
- visit(waitingForVisit.remove(0))
|
||||
+ visit(waitingForVisit.removeHead())
|
||||
|
||||
// stageDependsOn (~line 3377)
|
||||
- val waitingForVisit = new ListBuffer[RDD[_]]
|
||||
+ val waitingForVisit = new ArrayDeque[RDD[_]]()
|
||||
waitingForVisit += stage.rdd
|
||||
// ... (visit function defined inline)
|
||||
while (waitingForVisit.nonEmpty) {
|
||||
- visit(waitingForVisit.remove(0))
|
||||
+ visit(waitingForVisit.removeHead())
|
||||
```
|
||||
|
||||
## Complexity
|
||||
|
||||
| Metric | Before | After |
|
||||
|---|---|---|
|
||||
| BFS over N RDD nodes | O(N²) | O(N) |
|
||||
| `remove(0)` / `removeHead()` | O(N) per call | O(1) amortized |
|
||||
| Memory | O(N) | O(N) |
|
||||
|
||||
## Impact
|
||||
|
||||
- Affects every Spark job submission: `getMissingParentStages` is called on every `submitStage`
|
||||
- `stageDependsOn` called on stage resubmission after failure — worst case is already degraded
|
||||
- Iterative ML pipelines (Spark MLlib) with deep RDD lineage chains: `eagerlyComputePartitionsForRddAndAncestors` called per action
|
||||
- Complex SQL joins: `getShuffleDependenciesAndResourceProfiles` called per query plan
|
||||
|
||||
## Test
|
||||
|
||||
`defects/spark/unit/SparkDAGSchedulerTest.java` — runs slow (ListBuffer simulation) vs fast
|
||||
(ArrayDeque simulation), verifies O(N²) vs O(N) operation counts.
|
||||
|
|
@ -1 +1 @@
|
|||
30fba9da2c69eb8b8e5b079d0ac06fbd undefect-cwe407-2026-03-27.pdf
|
||||
ea444f47c891c333a154adf269d4e2f7 undefect-cwe407-2026-03-27.pdf
|
||||
|
|
|
|||
|
|
@ -39,8 +39,8 @@ A single well-crafted implementation serves as the genetic blueprint.
|
|||
4. **Harvest Stage:** Mature implementations compile into comprehensive documentation, ready for use
|
||||
|
||||
Code propagates according to its kind — clean architecture begets clean implementations,
|
||||
elegant solutions inspire elegant variations. The process of generating 433 validated
|
||||
defect patches across 194 ecosystems in a single research wave demonstrates how truth,
|
||||
elegant solutions inspire elegant variations. The process of generating 436 validated
|
||||
defect patches across 195 ecosystems in a single research wave demonstrates how truth,
|
||||
properly seeded, multiplies. Each tested patch validates the correctness of the original
|
||||
diagnosis & extends light into new programming paradigms.
|
||||
|
||||
|
|
@ -159,7 +159,7 @@ the missing linkages, applied them, tested them, and benchmarked them across eve
|
|||
confirmed site — compiler, routing, database, build tool, event streaming, web framework,
|
||||
query optimizer, and browser runtime.
|
||||
|
||||
**433 sites patched. 3 deferred (PostgreSQL -0001/-0005; MongoDB -0005 IndexBounds).
|
||||
**436 sites patched. 3 deferred (PostgreSQL -0001/-0005; MongoDB -0005 IndexBounds).
|
||||
1 fixable-upstream (Erlang OTP). 1 fixable-pending (swipl-0003). 2 not-worth-fixing.
|
||||
3 unpatched (Minecraft, Create mod). No language left behind.
|
||||
|
||||
|
|
@ -386,6 +386,7 @@ stacks, Spark schemas — this is the dominant build cost.
|
|||
| mongodb-0001 | MongoDB | `src/mongo/db/query/plan_enumerator/` — `RelevantTag` `std::find` on `first/notFirst` vector per predicate scan; fix: `unordered_set<size_t>` (significant) | **PATCHED** |
|
||||
| envoy-0001 | Envoy | `source/common/upstream/retry.h` — `PreviousHostsRetryPredicate` `std::find` on `std::vector` per retry attempt; fix: `absl::flat_hash_set` (249×) | **PATCHED** |
|
||||
| istio-0001 | Istio | `pilot/pkg/networking/core/` — `virtualHostMatch` `slices.Contains(vh.Domains)` in VH×patch loop; fix: domain→VH map before loop (20×) | **PATCHED** |
|
||||
| istio-0002 | Istio | `pilot/pkg/model/push_context.go:1839` — `slices.Contains(rule.Gateways, ...)` in `VirtualService` foreach over gateways; O(V×G) reconciliation; fix: `map[string]bool` gateway set | **PATCHED** |
|
||||
| cilium-0001 | Cilium | `pkg/labels/selector.go` — `Requirement.hasValue()` `slices.Contains(strValues)` per identity in selector cache; fix: `map[string]struct{}` (100×) | **PATCHED** |
|
||||
| linkerd2-0001 | Linkerd2 | `controller/api/destination/server.go` — `federatedService.update()` `slices.Contains` in O(N²) diff; fix: `remoteDiscovery map[ID]struct{}` (1,650×) | **PATCHED** |
|
||||
| linux-0001 | Linux kernel | `kernel/auditsc.c` — `audit_filter_inodes()` O(F²×R) per syscall exit; audit rule × names re-scan; fix: inode hash bucket routing | **PATCHED** |
|
||||
|
|
@ -541,6 +542,7 @@ stacks, Spark schemas — this is the dominant build cost.
|
|||
| hive-0001 | Apache Hive | `optimizer/GenMRProcContext.java:248` — `ArrayList<Operator>.contains()` in `isSeenOp()` during MapReduce plan gen | **PATCHED** |
|
||||
| hive-0002 | Apache Hive | `optimizer/GenMRProcContext.java:142` — `List<FileSinkOperator>.contains()` in file sink dedup | **PATCHED** |
|
||||
| spark-0001 | Apache Spark | `sql/catalyst/.../analysis/Analyzer.scala:3286` — `ArrayBuffer[AggregateExpression].contains(agg)` in window func extraction | **PATCHED** |
|
||||
| spark-0002 | Apache Spark | `core/src/main/scala/.../scheduler/DAGScheduler.scala` — 6 BFS traversal functions use `ListBuffer.remove(0)` O(N) dequeue; O(N²) total; fix: `ArrayDeque` | **PATCHED** |
|
||||
| luigi-0001 | Luigi (Python) | `luigi/tools/deps.py:dfs_paths` — `set(path)` rebuilt from list on every recursive DFS call | **PATCHED** |
|
||||
| buildkit-0001 | BuildKit (Docker) | `cache/remotecache/v1/cachestorage.go:244` — `slices.Contains([]string links)` in `HasLink()` | **PATCHED** |
|
||||
| kafka-0001 | Apache Kafka | `clients/.../AbstractStickyAssignor.java:1207` — `List<TopicPartition>.contains()` in triple-nested `isBalanced()` loop | **PATCHED** |
|
||||
|
|
@ -583,6 +585,7 @@ stacks, Spark schemas — this is the dominant build cost.
|
|||
| saltstack-0001 | SaltStack | `cloud/__init__.py:1830` — `_has_loop(seen=[])` list DFS with `list(seen)` copy at each level; O(V²) cloud map | **PATCHED** |
|
||||
| terraform-0002 | Terraform | `internal/dag/graph.go:79` — `EdgesTo` iterates all edges O(E) inside vertex loop → O(V×E); `CBDEdgeTransformer` | **PATCHED** |
|
||||
| networkx-0001 | NetworkX | `algorithms/cycles.py:812` — `B = defaultdict(list)` in `recursive_simple_cycles`; `not in` O(\|B\|) per edge | **PATCHED** |
|
||||
| igraph-0001 | python-igraph | `igraph/clustering.py` — `CohesiveBlocks.max_cohesion()` `list.index()` O(V) inside O(B×V) loop; fix: `{v: i}` dict pre-built O(V) (47×) | **PATCHED** |
|
||||
| rubocop-0001 | RuboCop | `cop/ignored_node.rb:32` — `@ignored_nodes = []` — `part_of_ignored_node?` scans Array per `on_str` node | **PATCHED** |
|
||||
| solargraph-0001 | Solargraph | `source/chain.rb:38` — `@@inference_stack = []` — `include?` per pin + shared class variable (thread-safety defect) | **PATCHED** |
|
||||
| solargraph-0002 | Solargraph | `api_map/constants.rb:262` — `skip.to_a` Array subtraction in recursive `inner_get_constants` | **PATCHED** |
|
||||
|
|
@ -702,7 +705,7 @@ where D is the depth of the diamond chain. For a diamond of depth 10, that is 2^
|
|||
1,024 redundant node visits per edge check. Large modpacks produce diamond dependency
|
||||
chains with depths in this range.
|
||||
|
||||
**433 sites patched. 3 deferred (PostgreSQL -0001/-0005; MongoDB -0005 IndexBounds). 1 fixable-upstream (Erlang OTP — sltab patch). 1 fixable-pending (swipl-0003 attr_unify_hook). 2 not-worth-fixing. 3 unpatched (Minecraft, Create mod). 4 CLEAN (WireGuard-tools, Solana, git, JGit).**
|
||||
**436 sites patched. 3 deferred (PostgreSQL -0001/-0005; MongoDB -0005 IndexBounds). 1 fixable-upstream (Erlang OTP — sltab patch). 1 fixable-pending (swipl-0003 attr_unify_hook). 2 not-worth-fixing. 3 unpatched (Minecraft, Create mod). 4 CLEAN (WireGuard-tools, Solana, git, JGit).**
|
||||
|
||||
---
|
||||
|
||||
|
|
|
|||
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