diff --git a/frameworks/native/neural_network_core/neural_network_core.cpp b/frameworks/native/neural_network_core/neural_network_core.cpp
index ec2753ebd81e1c755c22d92ecc84e61427c5d2f9..1b6483dda21e66ee1d98f3d9bf9ce441c5f470b8 100644
--- a/frameworks/native/neural_network_core/neural_network_core.cpp
+++ b/frameworks/native/neural_network_core/neural_network_core.cpp
@@ -601,7 +601,7 @@ OH_NN_ReturnCode Authentication(Compilation** compilation)
         compilationImpl->callingPid = std::atoi((iter->second).data());
     }
 
-    NNRtServiceApi& nnrtService = NNRtServiceApi::GetInstance();
+    const NNRtServiceApi& nnrtService = NNRtServiceApi::GetInstance();
     if (!nnrtService.IsServiceAvaliable()) {
         LOGW("Authentication failed, fail to get nnrt service, skip Authentication.");
         return OH_NN_SUCCESS;
@@ -1624,8 +1624,6 @@ OH_NN_ReturnCode RunSync(Executor *executor,
     }
 
     long timeStart = 0;
-    long timeEnd = 0;
-    int32_t modelLatency = 0;
     if (configPtr->isNeedModelLatency) {
         timeStart = std::chrono::duration_cast<std::chrono::milliseconds>(
             std::chrono::system_clock::now().time_since_epoch()).count();
@@ -1638,9 +1636,9 @@ OH_NN_ReturnCode RunSync(Executor *executor,
     }
 
     if (configPtr->isNeedModelLatency) {
-        timeEnd = std::chrono::duration_cast<std::chrono::milliseconds>(
+        long timeEnd = std::chrono::duration_cast<std::chrono::milliseconds>(
             std::chrono::system_clock::now().time_since_epoch()).count();
-        modelLatency = static_cast<int32_t>((timeEnd - timeStart));
+        int32_t modelLatency = static_cast<int32_t>((timeEnd - timeStart));
         std::thread t(UpdateModelLatency, configPtr, modelLatency);
         t.detach();
         LOGE("update async start.");
diff --git a/frameworks/native/neural_network_runtime/neural_network_runtime.cpp b/frameworks/native/neural_network_runtime/neural_network_runtime.cpp
index c56ffdef50644e75e4bf2debf6b0ad3e82e4d77a..25d71c3cd060130c5bf9f91f339d2654d86e56c2 100644
--- a/frameworks/native/neural_network_runtime/neural_network_runtime.cpp
+++ b/frameworks/native/neural_network_runtime/neural_network_runtime.cpp
@@ -314,7 +314,6 @@ OH_NN_ReturnCode ParseInputDimsFromExtensions(char* data, size_t dataSize, const
 
     size_t inputCount = liteGraph->input_indices_.size(); // LiteGraph输入个数
     size_t allTensorSize = liteGraph->all_tensors_.size(); // LiteGraph所有tensor个数
-    size_t inputDimSize = 0; // 存放每个输入的维度
     std::vector<int32_t> inputDim;
     size_t dataIndex = 0;
     for (size_t i = 0; i < inputCount; ++i) {
@@ -328,7 +327,7 @@ OH_NN_ReturnCode ParseInputDimsFromExtensions(char* data, size_t dataSize, const
         //获取当前输入的维度
         mindspore::lite::TensorPtr tensor = liteGraph->all_tensors_[liteGraph->input_indices_[i]];
         auto tensorDims = mindspore::lite::MindIR_Tensor_GetDims(tensor);
-        inputDimSize = tensorDims.size();
+        size_t inputDimSize = tensorDims.size();
         if (allDimsSize < inputDimSize) {
             LOGE("ParseInputDimsFromExtensions failed, dataSize is invalid.");
             extensionConfig.inputDims.clear();
@@ -552,9 +551,9 @@ NNRT_API bool OH_NNModel_HasCache(const char *cacheDir, const char *modelName)
     ifs.close();
 
     // determine whether cache model files exist
-    std::string cacheModelPath;
     for (int64_t i = 0; i < fileNumber; ++i) {
-        cacheModelPath = std::string(cacheDir) + "/" + std::string(modelName) + std::to_string(i) + ".nncache";
+        std::string cacheModelPath =
+            std::string(cacheDir) + "/" + std::string(modelName) + std::to_string(i) + ".nncache";
         exist = (exist && (stat(cacheModelPath.c_str(), &buffer) == 0));
     }