diff --git a/tests/validation/fixtures/ConcatenateLayerFixture.h b/tests/validation/fixtures/ConcatenateLayerFixture.h
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+/*
+ * Copyright (c) 2018-2019 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#ifndef ARM_COMPUTE_TEST_WIDTHCONCATENATE_LAYER_FIXTURE
+#define ARM_COMPUTE_TEST_WIDTHCONCATENATE_LAYER_FIXTURE
+
+#include "arm_compute/core/TensorShape.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+#include "tests/AssetsLibrary.h"
+#include "tests/Globals.h"
+#include "tests/IAccessor.h"
+#include "tests/framework/Asserts.h"
+#include "tests/framework/Fixture.h"
+#include "tests/validation/Helpers.h"
+#include "tests/validation/reference/ConcatenateLayer.h"
+
+#include <random>
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+template <typename TensorType, typename ITensorType, typename AccessorType, typename FunctionType, typename T>
+class ConcatenateLayerValidationFixture : public framework::Fixture
+{
+public:
+    template <typename...>
+    void setup(TensorShape shape, DataType data_type, unsigned int axis)
+    {
+        // Create input shapes
+        std::mt19937                    gen(library->seed());
+        std::uniform_int_distribution<> num_dis(2, 8);
+        std::uniform_int_distribution<> offset_dis(0, 20);
+
+        const int num_tensors = num_dis(gen);
+
+        std::vector<TensorShape> shapes(num_tensors, shape);
+
+        // vector holding the quantization info:
+        //      the last element is the output quantization info
+        //      all other elements are the quantization info for the input tensors
+        std::vector<QuantizationInfo> qinfo(num_tensors + 1, QuantizationInfo());
+        for(auto &qi : qinfo)
+        {
+            qi = QuantizationInfo(1.f / 255.f, offset_dis(gen));
+        }
+        std::bernoulli_distribution      mutate_dis(0.5f);
+        std::uniform_real_distribution<> change_dis(-0.25f, 0.f);
+
+        // Generate more shapes based on the input
+        for(auto &s : shapes)
+        {
+            // Randomly change the first dimension
+            if(mutate_dis(gen))
+            {
+                // Decrease the dimension by a small percentage. Don't increase
+                // as that could make tensor too large.
+                s.set(axis, s[axis] + 2 * static_cast<int>(s[axis] * change_dis(gen)));
+            }
+        }
+
+        _target    = compute_target(shapes, qinfo, data_type, axis);
+        _reference = compute_reference(shapes, qinfo, data_type, axis);
+    }
+
+protected:
+    template <typename U>
+    void fill(U &&tensor, int i)
+    {
+        library->fill_tensor_uniform(tensor, i);
+    }
+
+    TensorType compute_target(const std::vector<TensorShape> &shapes, const std::vector<QuantizationInfo> &qinfo, DataType data_type, unsigned int axis)
+    {
+        std::vector<TensorType>    srcs;
+        std::vector<ITensorType *> src_ptrs;
+
+        // Create tensors
+        srcs.reserve(shapes.size());
+
+        for(size_t j = 0; j < shapes.size(); ++j)
+        {
+            srcs.emplace_back(create_tensor<TensorType>(shapes[j], data_type, 1, qinfo[j]));
+            src_ptrs.emplace_back(&srcs.back());
+        }
+
+        const TensorShape dst_shape = misc::shape_calculator::calculate_concatenate_shape(src_ptrs, axis);
+        TensorType        dst       = create_tensor<TensorType>(dst_shape, data_type, 1, qinfo[shapes.size()]);
+
+        // Create and configure function
+        FunctionType concat;
+        switch(axis)
+        {
+            case 0:
+                concat.configure(src_ptrs, &dst, DataLayoutDimension::WIDTH);
+                break;
+            case 1:
+                concat.configure(src_ptrs, &dst, DataLayoutDimension::HEIGHT);
+                break;
+            case 2:
+                concat.configure(src_ptrs, &dst, DataLayoutDimension::CHANNEL);
+                break;
+            default:
+                ARM_COMPUTE_ERROR("Not supported");
+                break;
+        }
+
+        for(auto &src : srcs)
+        {
+            ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
+        }
+
+        ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+        // Allocate tensors
+        for(auto &src : srcs)
+        {
+            src.allocator()->allocate();
+            ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS);
+        }
+
+        dst.allocator()->allocate();
+        ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+        // Fill tensors
+        int i = 0;
+        for(auto &src : srcs)
+        {
+            fill(AccessorType(src), i++);
+        }
+
+        // Compute function
+        concat.run();
+
+        return dst;
+    }
+
+    SimpleTensor<T> compute_reference(const std::vector<TensorShape> &shapes, const std::vector<QuantizationInfo> &qinfo, DataType data_type, unsigned int axis)
+    {
+        std::vector<SimpleTensor<T>> srcs;
+
+        // Create and fill tensors
+        for(size_t j = 0; j < shapes.size(); ++j)
+        {
+            srcs.emplace_back(shapes[j], data_type, 1, qinfo[j]);
+            fill(srcs.back(), j);
+        }
+
+        const TensorShape dst_shape = calculate_concatenate_shape(shapes, axis);
+        SimpleTensor<T>   dst{ dst_shape, data_type, 1, qinfo[shapes.size()] };
+        return reference::concatenate_layer<T>(srcs, dst, axis);
+    }
+
+    TensorType      _target{};
+    SimpleTensor<T> _reference{};
+};
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
+#endif /* ARM_COMPUTE_TEST_WIDTHCONCATENATE_LAYER_FIXTURE */