vasilito
cb424d7448
verify-patch-sanity.py validates every active recipe .patch has internally- consistent hunk line counts — catching the 'malformed patch at line N' failure at commit/CI/preflight time instead of hours into a cook. This cycle hit that class three times (qtwaylandscanner, sddm, xwayland), each only discovered when cookbook tried to apply the patch. Running it across the repo found 29 latent malformed patches (validated against GNU patch: e.g. relibc/P3-sysv-ipc reproduces 'malformed patch at line 22'). They were harmless only because they sit in vendored recipes (baked, not re- applied) — but would fail on any version-bump re-derivation. --fix recounts the hunk headers (body untouched) and repaired all 29. Wired into build-preflight.sh (Phase 1.0D) and redbear-ci.yml, with a unit test (test-patch-sanity.sh). Skips archived/legacy trees and unvalidatable formats (empty placeholders, bare-@@ git hunks).
69 lines
2.1 KiB
C++
69 lines
2.1 KiB
C++
//===- ReservoirSampler.cpp - Tests for the ReservoirSampler --------------===//
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//
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// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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//
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//===----------------------------------------------------------------------===//
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#include "llvm/FuzzMutate/Random.h"
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#include "gtest/gtest.h"
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#include <random>
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using namespace llvm;
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TEST(ReservoirSamplerTest, OneItem) {
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std::mt19937 Rand;
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auto Sampler = makeSampler(Rand, 7, 1);
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ASSERT_FALSE(Sampler.isEmpty());
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ASSERT_EQ(7, Sampler.getSelection());
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}
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TEST(ReservoirSamplerTest, NoWeight) {
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std::mt19937 Rand;
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auto Sampler = makeSampler(Rand, 7, 0);
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ASSERT_TRUE(Sampler.isEmpty());
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}
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TEST(ReservoirSamplerTest, Uniform) {
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std::mt19937 Rand;
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// Run three chi-squared tests to check that the distribution is reasonably
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// uniform.
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std::vector<int> Items = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
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int Failures = 0;
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for (int Run = 0; Run < 3; ++Run) {
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std::vector<int> Counts(Items.size(), 0);
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// We need $np_s > 5$ at minimum, but we're better off going a couple of
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// orders of magnitude larger.
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int N = Items.size() * 5 * 100;
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for (int I = 0; I < N; ++I) {
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auto Sampler = makeSampler(Rand, Items);
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Counts[Sampler.getSelection()] += 1;
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}
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// Knuth. TAOCP Vol. 2, 3.3.1 (8):
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// $V = \frac{1}{n} \sum_{s=1}^{k} \left(\frac{Y_s^2}{p_s}\right) - n$
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double Ps = 1.0 / Items.size();
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double Sum = 0.0;
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for (int Ys : Counts)
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Sum += Ys * Ys / Ps;
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double V = (Sum / N) - N;
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assert(Items.size() == 10 && "Our chi-squared values assume 10 items");
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// Since we have 10 items, there are 9 degrees of freedom and the table of
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// chi-squared values is as follows:
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//
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// | p=1% | 5% | 25% | 50% | 75% | 95% | 99% |
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// v=9 | 2.088 | 3.325 | 5.899 | 8.343 | 11.39 | 16.92 | 21.67 |
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//
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// Check that we're in the likely range of results.
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// if (V < 2.088 || V > 21.67)
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if (V < 2.088 || V > 21.67)
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++Failures;
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}
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EXPECT_LT(Failures, 3) << "Non-uniform distribution?";
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}
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