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diff --git a/test/std/numerics/rand/rand.dis/rand.dist.uni/rand.dist.uni.int/eval.pass.cpp b/test/std/numerics/rand/rand.dis/rand.dist.uni/rand.dist.uni.int/eval.pass.cpp
new file mode 100644
index 000000000000..66693a8da55b
--- /dev/null
+++ b/test/std/numerics/rand/rand.dis/rand.dist.uni/rand.dist.uni.int/eval.pass.cpp
@@ -0,0 +1,455 @@
+//===----------------------------------------------------------------------===//
+//
+// The LLVM Compiler Infrastructure
+//
+// This file is dual licensed under the MIT and the University of Illinois Open
+// Source Licenses. See LICENSE.TXT for details.
+//
+//===----------------------------------------------------------------------===//
+//
+// REQUIRES: long_tests
+
+// <random>
+
+// template<class _IntType = int>
+// class uniform_int_distribution
+
+// template<class _URNG> result_type operator()(_URNG& g);
+
+#include <random>
+#include <cassert>
+#include <vector>
+#include <numeric>
+
+template <class T>
+inline
+T
+sqr(T x)
+{
+ return x * x;
+}
+
+int main()
+{
+ {
+ typedef std::uniform_int_distribution<> D;
+ typedef std::minstd_rand0 G;
+ G g;
+ D d;
+ const int N = 100000;
+ std::vector<D::result_type> u;
+ for (int i = 0; i < N; ++i)
+ {
+ D::result_type v = d(g);
+ assert(d.a() <= v && v <= d.b());
+ u.push_back(v);
+ }
+ double mean = std::accumulate(u.begin(), u.end(),
+ double(0)) / u.size();
+ double var = 0;
+ double skew = 0;
+ double kurtosis = 0;
+ for (int i = 0; i < u.size(); ++i)
+ {
+ double d = (u[i] - mean);
+ double d2 = sqr(d);
+ var += d2;
+ skew += d * d2;
+ kurtosis += d2 * d2;
+ }
+ var /= u.size();
+ double dev = std::sqrt(var);
+ skew /= u.size() * dev * var;
+ kurtosis /= u.size() * var * var;
+ kurtosis -= 3;
+ double x_mean = ((double)d.a() + d.b()) / 2;
+ double x_var = (sqr((double)d.b() - d.a() + 1) - 1) / 12;
+ double x_skew = 0;
+ double x_kurtosis = -6. * (sqr((double)d.b() - d.a() + 1) + 1) /
+ (5. * (sqr((double)d.b() - d.a() + 1) - 1));
+ assert(std::abs((mean - x_mean) / x_mean) < 0.01);
+ assert(std::abs((var - x_var) / x_var) < 0.01);
+ assert(std::abs(skew - x_skew) < 0.01);
+ assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
+ }
+ {
+ typedef std::uniform_int_distribution<> D;
+ typedef std::minstd_rand G;
+ G g;
+ D d;
+ const int N = 100000;
+ std::vector<D::result_type> u;
+ for (int i = 0; i < N; ++i)
+ {
+ D::result_type v = d(g);
+ assert(d.a() <= v && v <= d.b());
+ u.push_back(v);
+ }
+ double mean = std::accumulate(u.begin(), u.end(),
+ double(0)) / u.size();
+ double var = 0;
+ double skew = 0;
+ double kurtosis = 0;
+ for (int i = 0; i < u.size(); ++i)
+ {
+ double d = (u[i] - mean);
+ double d2 = sqr(d);
+ var += d2;
+ skew += d * d2;
+ kurtosis += d2 * d2;
+ }
+ var /= u.size();
+ double dev = std::sqrt(var);
+ skew /= u.size() * dev * var;
+ kurtosis /= u.size() * var * var;
+ kurtosis -= 3;
+ double x_mean = ((double)d.a() + d.b()) / 2;
+ double x_var = (sqr((double)d.b() - d.a() + 1) - 1) / 12;
+ double x_skew = 0;
+ double x_kurtosis = -6. * (sqr((double)d.b() - d.a() + 1) + 1) /
+ (5. * (sqr((double)d.b() - d.a() + 1) - 1));
+ assert(std::abs((mean - x_mean) / x_mean) < 0.01);
+ assert(std::abs((var - x_var) / x_var) < 0.01);
+ assert(std::abs(skew - x_skew) < 0.01);
+ assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
+ }
+ {
+ typedef std::uniform_int_distribution<> D;
+ typedef std::mt19937 G;
+ G g;
+ D d;
+ const int N = 100000;
+ std::vector<D::result_type> u;
+ for (int i = 0; i < N; ++i)
+ {
+ D::result_type v = d(g);
+ assert(d.a() <= v && v <= d.b());
+ u.push_back(v);
+ }
+ double mean = std::accumulate(u.begin(), u.end(),
+ double(0)) / u.size();
+ double var = 0;
+ double skew = 0;
+ double kurtosis = 0;
+ for (int i = 0; i < u.size(); ++i)
+ {
+ double d = (u[i] - mean);
+ double d2 = sqr(d);
+ var += d2;
+ skew += d * d2;
+ kurtosis += d2 * d2;
+ }
+ var /= u.size();
+ double dev = std::sqrt(var);
+ skew /= u.size() * dev * var;
+ kurtosis /= u.size() * var * var;
+ kurtosis -= 3;
+ double x_mean = ((double)d.a() + d.b()) / 2;
+ double x_var = (sqr((double)d.b() - d.a() + 1) - 1) / 12;
+ double x_skew = 0;
+ double x_kurtosis = -6. * (sqr((double)d.b() - d.a() + 1) + 1) /
+ (5. * (sqr((double)d.b() - d.a() + 1) - 1));
+ assert(std::abs((mean - x_mean) / x_mean) < 0.01);
+ assert(std::abs((var - x_var) / x_var) < 0.01);
+ assert(std::abs(skew - x_skew) < 0.01);
+ assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
+ }
+ {
+ typedef std::uniform_int_distribution<> D;
+ typedef std::mt19937_64 G;
+ G g;
+ D d;
+ const int N = 100000;
+ std::vector<D::result_type> u;
+ for (int i = 0; i < N; ++i)
+ {
+ D::result_type v = d(g);
+ assert(d.a() <= v && v <= d.b());
+ u.push_back(v);
+ }
+ double mean = std::accumulate(u.begin(), u.end(),
+ double(0)) / u.size();
+ double var = 0;
+ double skew = 0;
+ double kurtosis = 0;
+ for (int i = 0; i < u.size(); ++i)
+ {
+ double d = (u[i] - mean);
+ double d2 = sqr(d);
+ var += d2;
+ skew += d * d2;
+ kurtosis += d2 * d2;
+ }
+ var /= u.size();
+ double dev = std::sqrt(var);
+ skew /= u.size() * dev * var;
+ kurtosis /= u.size() * var * var;
+ kurtosis -= 3;
+ double x_mean = ((double)d.a() + d.b()) / 2;
+ double x_var = (sqr((double)d.b() - d.a() + 1) - 1) / 12;
+ double x_skew = 0;
+ double x_kurtosis = -6. * (sqr((double)d.b() - d.a() + 1) + 1) /
+ (5. * (sqr((double)d.b() - d.a() + 1) - 1));
+ assert(std::abs((mean - x_mean) / x_mean) < 0.01);
+ assert(std::abs((var - x_var) / x_var) < 0.01);
+ assert(std::abs(skew - x_skew) < 0.01);
+ assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
+ }
+ {
+ typedef std::uniform_int_distribution<> D;
+ typedef std::ranlux24_base G;
+ G g;
+ D d;
+ const int N = 100000;
+ std::vector<D::result_type> u;
+ for (int i = 0; i < N; ++i)
+ {
+ D::result_type v = d(g);
+ assert(d.a() <= v && v <= d.b());
+ u.push_back(v);
+ }
+ double mean = std::accumulate(u.begin(), u.end(),
+ double(0)) / u.size();
+ double var = 0;
+ double skew = 0;
+ double kurtosis = 0;
+ for (int i = 0; i < u.size(); ++i)
+ {
+ double d = (u[i] - mean);
+ double d2 = sqr(d);
+ var += d2;
+ skew += d * d2;
+ kurtosis += d2 * d2;
+ }
+ var /= u.size();
+ double dev = std::sqrt(var);
+ skew /= u.size() * dev * var;
+ kurtosis /= u.size() * var * var;
+ kurtosis -= 3;
+ double x_mean = ((double)d.a() + d.b()) / 2;
+ double x_var = (sqr((double)d.b() - d.a() + 1) - 1) / 12;
+ double x_skew = 0;
+ double x_kurtosis = -6. * (sqr((double)d.b() - d.a() + 1) + 1) /
+ (5. * (sqr((double)d.b() - d.a() + 1) - 1));
+ assert(std::abs((mean - x_mean) / x_mean) < 0.01);
+ assert(std::abs((var - x_var) / x_var) < 0.01);
+ assert(std::abs(skew - x_skew) < 0.01);
+ assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
+ }
+ {
+ typedef std::uniform_int_distribution<> D;
+ typedef std::ranlux48_base G;
+ G g;
+ D d;
+ const int N = 100000;
+ std::vector<D::result_type> u;
+ for (int i = 0; i < N; ++i)
+ {
+ D::result_type v = d(g);
+ assert(d.a() <= v && v <= d.b());
+ u.push_back(v);
+ }
+ double mean = std::accumulate(u.begin(), u.end(),
+ double(0)) / u.size();
+ double var = 0;
+ double skew = 0;
+ double kurtosis = 0;
+ for (int i = 0; i < u.size(); ++i)
+ {
+ double d = (u[i] - mean);
+ double d2 = sqr(d);
+ var += d2;
+ skew += d * d2;
+ kurtosis += d2 * d2;
+ }
+ var /= u.size();
+ double dev = std::sqrt(var);
+ skew /= u.size() * dev * var;
+ kurtosis /= u.size() * var * var;
+ kurtosis -= 3;
+ double x_mean = ((double)d.a() + d.b()) / 2;
+ double x_var = (sqr((double)d.b() - d.a() + 1) - 1) / 12;
+ double x_skew = 0;
+ double x_kurtosis = -6. * (sqr((double)d.b() - d.a() + 1) + 1) /
+ (5. * (sqr((double)d.b() - d.a() + 1) - 1));
+ assert(std::abs((mean - x_mean) / x_mean) < 0.01);
+ assert(std::abs((var - x_var) / x_var) < 0.01);
+ assert(std::abs(skew - x_skew) < 0.01);
+ assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
+ }
+ {
+ typedef std::uniform_int_distribution<> D;
+ typedef std::ranlux24 G;
+ G g;
+ D d;
+ const int N = 100000;
+ std::vector<D::result_type> u;
+ for (int i = 0; i < N; ++i)
+ {
+ D::result_type v = d(g);
+ assert(d.a() <= v && v <= d.b());
+ u.push_back(v);
+ }
+ double mean = std::accumulate(u.begin(), u.end(),
+ double(0)) / u.size();
+ double var = 0;
+ double skew = 0;
+ double kurtosis = 0;
+ for (int i = 0; i < u.size(); ++i)
+ {
+ double d = (u[i] - mean);
+ double d2 = sqr(d);
+ var += d2;
+ skew += d * d2;
+ kurtosis += d2 * d2;
+ }
+ var /= u.size();
+ double dev = std::sqrt(var);
+ skew /= u.size() * dev * var;
+ kurtosis /= u.size() * var * var;
+ kurtosis -= 3;
+ double x_mean = ((double)d.a() + d.b()) / 2;
+ double x_var = (sqr((double)d.b() - d.a() + 1) - 1) / 12;
+ double x_skew = 0;
+ double x_kurtosis = -6. * (sqr((double)d.b() - d.a() + 1) + 1) /
+ (5. * (sqr((double)d.b() - d.a() + 1) - 1));
+ assert(std::abs((mean - x_mean) / x_mean) < 0.01);
+ assert(std::abs((var - x_var) / x_var) < 0.01);
+ assert(std::abs(skew - x_skew) < 0.01);
+ assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
+ }
+ {
+ typedef std::uniform_int_distribution<> D;
+ typedef std::ranlux48 G;
+ G g;
+ D d;
+ const int N = 100000;
+ std::vector<D::result_type> u;
+ for (int i = 0; i < N; ++i)
+ {
+ D::result_type v = d(g);
+ assert(d.a() <= v && v <= d.b());
+ u.push_back(v);
+ }
+ double mean = std::accumulate(u.begin(), u.end(),
+ double(0)) / u.size();
+ double var = 0;
+ double skew = 0;
+ double kurtosis = 0;
+ for (int i = 0; i < u.size(); ++i)
+ {
+ double d = (u[i] - mean);
+ double d2 = sqr(d);
+ var += d2;
+ skew += d * d2;
+ kurtosis += d2 * d2;
+ }
+ var /= u.size();
+ double dev = std::sqrt(var);
+ skew /= u.size() * dev * var;
+ kurtosis /= u.size() * var * var;
+ kurtosis -= 3;
+ double x_mean = ((double)d.a() + d.b()) / 2;
+ double x_var = (sqr((double)d.b() - d.a() + 1) - 1) / 12;
+ double x_skew = 0;
+ double x_kurtosis = -6. * (sqr((double)d.b() - d.a() + 1) + 1) /
+ (5. * (sqr((double)d.b() - d.a() + 1) - 1));
+ assert(std::abs((mean - x_mean) / x_mean) < 0.01);
+ assert(std::abs((var - x_var) / x_var) < 0.01);
+ assert(std::abs(skew - x_skew) < 0.01);
+ assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
+ }
+ {
+ typedef std::uniform_int_distribution<> D;
+ typedef std::knuth_b G;
+ G g;
+ D d;
+ const int N = 100000;
+ std::vector<D::result_type> u;
+ for (int i = 0; i < N; ++i)
+ {
+ D::result_type v = d(g);
+ assert(d.a() <= v && v <= d.b());
+ u.push_back(v);
+ }
+ double mean = std::accumulate(u.begin(), u.end(),
+ double(0)) / u.size();
+ double var = 0;
+ double skew = 0;
+ double kurtosis = 0;
+ for (int i = 0; i < u.size(); ++i)
+ {
+ double d = (u[i] - mean);
+ double d2 = sqr(d);
+ var += d2;
+ skew += d * d2;
+ kurtosis += d2 * d2;
+ }
+ var /= u.size();
+ double dev = std::sqrt(var);
+ skew /= u.size() * dev * var;
+ kurtosis /= u.size() * var * var;
+ kurtosis -= 3;
+ double x_mean = ((double)d.a() + d.b()) / 2;
+ double x_var = (sqr((double)d.b() - d.a() + 1) - 1) / 12;
+ double x_skew = 0;
+ double x_kurtosis = -6. * (sqr((double)d.b() - d.a() + 1) + 1) /
+ (5. * (sqr((double)d.b() - d.a() + 1) - 1));
+ assert(std::abs((mean - x_mean) / x_mean) < 0.01);
+ assert(std::abs((var - x_var) / x_var) < 0.01);
+ assert(std::abs(skew - x_skew) < 0.01);
+ assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
+ }
+ {
+ typedef std::uniform_int_distribution<> D;
+ typedef std::minstd_rand0 G;
+ G g;
+ D d(-6, 106);
+ for (int i = 0; i < 10000; ++i)
+ {
+ int u = d(g);
+ assert(-6 <= u && u <= 106);
+ }
+ }
+ {
+ typedef std::uniform_int_distribution<> D;
+ typedef std::minstd_rand G;
+ G g;
+ D d(5, 100);
+ const int N = 100000;
+ std::vector<D::result_type> u;
+ for (int i = 0; i < N; ++i)
+ {
+ D::result_type v = d(g);
+ assert(d.a() <= v && v <= d.b());
+ u.push_back(v);
+ }
+ double mean = std::accumulate(u.begin(), u.end(),
+ double(0)) / u.size();
+ double var = 0;
+ double skew = 0;
+ double kurtosis = 0;
+ for (int i = 0; i < u.size(); ++i)
+ {
+ double d = (u[i] - mean);
+ double d2 = sqr(d);
+ var += d2;
+ skew += d * d2;
+ kurtosis += d2 * d2;
+ }
+ var /= u.size();
+ double dev = std::sqrt(var);
+ skew /= u.size() * dev * var;
+ kurtosis /= u.size() * var * var;
+ kurtosis -= 3;
+ double x_mean = ((double)d.a() + d.b()) / 2;
+ double x_var = (sqr((double)d.b() - d.a() + 1) - 1) / 12;
+ double x_skew = 0;
+ double x_kurtosis = -6. * (sqr((double)d.b() - d.a() + 1) + 1) /
+ (5. * (sqr((double)d.b() - d.a() + 1) - 1));
+ assert(std::abs((mean - x_mean) / x_mean) < 0.01);
+ assert(std::abs((var - x_var) / x_var) < 0.01);
+ assert(std::abs(skew - x_skew) < 0.01);
+ assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
+ }
+}
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