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diff --git a/neuralnetworks/1.0/vts/functional/Models.cpp b/neuralnetworks/1.0/vts/functional/Models.cpp
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+++ b/neuralnetworks/1.0/vts/functional/Models.cpp
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+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#define LOG_TAG "neuralnetworks_hidl_hal_test"
+
+#include "Models.h"
+#include <android/hidl/memory/1.0/IMemory.h>
+#include <hidlmemory/mapping.h>
+#include <vector>
+
+namespace android {
+namespace hardware {
+namespace neuralnetworks {
+namespace V1_0 {
+namespace vts {
+namespace functional {
+
+// create a valid model
+Model createValidTestModel() {
+    const std::vector<float> operand2Data = {5.0f, 6.0f, 7.0f, 8.0f};
+    const uint32_t size = operand2Data.size() * sizeof(float);
+
+    const uint32_t operand1 = 0;
+    const uint32_t operand2 = 1;
+    const uint32_t operand3 = 2;
+    const uint32_t operand4 = 3;
+
+    const std::vector<Operand> operands = {
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {1, 2, 2, 1},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {1, 2, 2, 1},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 0, .length = size},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = size, .length = sizeof(int32_t)},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {1, 2, 2, 1},
+            .numberOfConsumers = 0,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_OUTPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+    };
+
+    const std::vector<Operation> operations = {{
+        .type = OperationType::ADD, .inputs = {operand1, operand2, operand3}, .outputs = {operand4},
+    }};
+
+    const std::vector<uint32_t> inputIndexes = {operand1};
+    const std::vector<uint32_t> outputIndexes = {operand4};
+    std::vector<uint8_t> operandValues(
+        reinterpret_cast<const uint8_t*>(operand2Data.data()),
+        reinterpret_cast<const uint8_t*>(operand2Data.data()) + size);
+    int32_t activation[1] = {static_cast<int32_t>(FusedActivationFunc::NONE)};
+    operandValues.insert(operandValues.end(), reinterpret_cast<const uint8_t*>(&activation[0]),
+                         reinterpret_cast<const uint8_t*>(&activation[1]));
+
+    const std::vector<hidl_memory> pools = {};
+
+    return {
+        .operands = operands,
+        .operations = operations,
+        .inputIndexes = inputIndexes,
+        .outputIndexes = outputIndexes,
+        .operandValues = operandValues,
+        .pools = pools,
+    };
+}
+
+// create first invalid model
+Model createInvalidTestModel1() {
+    Model model = createValidTestModel();
+    model.operations[0].type = static_cast<OperationType>(0xDEADBEEF); /* INVALID */
+    return model;
+}
+
+// create second invalid model
+Model createInvalidTestModel2() {
+    Model model = createValidTestModel();
+    const uint32_t operand1 = 0;
+    const uint32_t operand5 = 4;  // INVALID OPERAND
+    model.inputIndexes = std::vector<uint32_t>({operand1, operand5 /* INVALID OPERAND */});
+    return model;
+}
+
+// allocator helper
+hidl_memory allocateSharedMemory(int64_t size, const std::string& type = "ashmem") {
+    hidl_memory memory;
+
+    sp<IAllocator> allocator = IAllocator::getService(type);
+    if (!allocator.get()) {
+        return {};
+    }
+
+    Return<void> ret = allocator->allocate(size, [&](bool success, const hidl_memory& mem) {
+        ASSERT_TRUE(success);
+        memory = mem;
+    });
+    if (!ret.isOk()) {
+        return {};
+    }
+
+    return memory;
+}
+
+// create a valid request
+Request createValidTestRequest() {
+    std::vector<float> inputData = {1.0f, 2.0f, 3.0f, 4.0f};
+    std::vector<float> outputData = {-1.0f, -1.0f, -1.0f, -1.0f};
+    const uint32_t INPUT = 0;
+    const uint32_t OUTPUT = 1;
+
+    // prepare inputs
+    uint32_t inputSize = static_cast<uint32_t>(inputData.size() * sizeof(float));
+    uint32_t outputSize = static_cast<uint32_t>(outputData.size() * sizeof(float));
+    std::vector<RequestArgument> inputs = {{
+        .location = {.poolIndex = INPUT, .offset = 0, .length = inputSize}, .dimensions = {},
+    }};
+    std::vector<RequestArgument> outputs = {{
+        .location = {.poolIndex = OUTPUT, .offset = 0, .length = outputSize}, .dimensions = {},
+    }};
+    std::vector<hidl_memory> pools = {allocateSharedMemory(inputSize),
+                                      allocateSharedMemory(outputSize)};
+    if (pools[INPUT].size() == 0 || pools[OUTPUT].size() == 0) {
+        return {};
+    }
+
+    // load data
+    sp<IMemory> inputMemory = mapMemory(pools[INPUT]);
+    sp<IMemory> outputMemory = mapMemory(pools[OUTPUT]);
+    if (inputMemory.get() == nullptr || outputMemory.get() == nullptr) {
+        return {};
+    }
+    float* inputPtr = reinterpret_cast<float*>(static_cast<void*>(inputMemory->getPointer()));
+    float* outputPtr = reinterpret_cast<float*>(static_cast<void*>(outputMemory->getPointer()));
+    if (inputPtr == nullptr || outputPtr == nullptr) {
+        return {};
+    }
+    inputMemory->update();
+    outputMemory->update();
+    std::copy(inputData.begin(), inputData.end(), inputPtr);
+    std::copy(outputData.begin(), outputData.end(), outputPtr);
+    inputMemory->commit();
+    outputMemory->commit();
+
+    return {.inputs = inputs, .outputs = outputs, .pools = pools};
+}
+
+// create first invalid request
+Request createInvalidTestRequest1() {
+    Request request = createValidTestRequest();
+    const uint32_t INVALID = 2;
+    std::vector<float> inputData = {1.0f, 2.0f, 3.0f, 4.0f};
+    uint32_t inputSize = static_cast<uint32_t>(inputData.size() * sizeof(float));
+    request.inputs[0].location = {
+        .poolIndex = INVALID /* INVALID */, .offset = 0, .length = inputSize};
+    return request;
+}
+
+// create second invalid request
+Request createInvalidTestRequest2() {
+    Request request = createValidTestRequest();
+    request.inputs[0].dimensions = std::vector<uint32_t>({1, 2, 3, 4, 5, 6, 7, 8} /* INVALID */);
+    return request;
+}
+
+}  // namespace functional
+}  // namespace vts
+}  // namespace V1_0
+}  // namespace neuralnetworks
+}  // namespace hardware
+}  // namespace android