diff --git a/src/frontends/onnx/frontend/src/op/group_normalization.cpp b/src/frontends/onnx/frontend/src/op/group_normalization.cpp
index d0f32a89ae4..b0dc8f786d8 100644
--- a/src/frontends/onnx/frontend/src/op/group_normalization.cpp
+++ b/src/frontends/onnx/frontend/src/op/group_normalization.cpp
@@ -12,30 +12,34 @@ namespace onnx_import {
 namespace op {
 namespace set_1 {
 OutputVector group_normalization(const Node& node) {
-    const auto data = node.get_ng_inputs().at(0);  // Shape [N, C, ...]
-    auto scale = node.get_ng_inputs().at(1);       // Shape [num_groups]
-    auto bias = node.get_ng_inputs().at(2);        // Shape [num_groups]
+    const auto inputs = node.get_ng_inputs();
+    OPENVINO_ASSERT(inputs.size() == 3);
 
-    auto eps = node.get_attribute_value<float>("epsilon", 1e-05f);
-    auto num_groups = node.get_attribute_value<int64_t>("num_groups");
+    const auto& data = inputs[0];   // Shape [N, C, ...]
+    const auto& scale = inputs[1];  // Shape [num_groups]
+    const auto& bias = inputs[2];   // Shape [num_groups]
 
-    auto zero = default_opset::Constant::create(element::i64, Shape{1}, {0});
-    auto one = default_opset::Constant::create(element::i64, Shape{1}, {1});
-    auto c_dim = std::make_shared<default_opset::Gather>(std::make_shared<default_opset::ShapeOf>(data), one, zero);
-    auto g_dim = default_opset::Constant::create(element::i64, Shape{1}, {num_groups});
+    const auto eps = node.get_attribute_value<float>("epsilon", 1e-05f);
+    const auto num_groups = node.get_attribute_value<int64_t>("num_groups");
 
-    auto c_g_div = std::make_shared<default_opset::Divide>(c_dim, g_dim);
+    const auto zero = default_opset::Constant::create(element::i64, Shape{1}, {0});
+    const auto one = default_opset::Constant::create(element::i64, Shape{1}, {1});
+    const auto c_dim =
+        std::make_shared<default_opset::Gather>(std::make_shared<default_opset::ShapeOf>(data), one, zero);
+    const auto g_dim = default_opset::Constant::create(element::i64, Shape{1}, {num_groups});
+
+    const auto c_g_div = std::make_shared<default_opset::Divide>(c_dim, g_dim);
 
     // Adjust scale and bias shape, [G] -> [G, C/G] -> [C]
-    scale = std::make_shared<default_opset::Unsqueeze>(scale, one);
-    auto broadcast_scale =
-        std::make_shared<default_opset::Broadcast>(scale, c_g_div, ov::op::BroadcastType::BIDIRECTIONAL);
-    auto c_scale = std::make_shared<default_opset::Reshape>(broadcast_scale, c_dim, false);
+    const auto scale_unsq = std::make_shared<default_opset::Unsqueeze>(scale, one);
+    const auto broadcast_scale =
+        std::make_shared<default_opset::Broadcast>(scale_unsq, c_g_div, ov::op::BroadcastType::BIDIRECTIONAL);
+    const auto c_scale = std::make_shared<default_opset::Reshape>(broadcast_scale, c_dim, false);
 
-    bias = std::make_shared<default_opset::Unsqueeze>(bias, one);
-    auto broadcast_bias =
-        std::make_shared<default_opset::Broadcast>(bias, c_g_div, ov::op::BroadcastType::BIDIRECTIONAL);
-    auto c_bias = std::make_shared<default_opset::Reshape>(broadcast_bias, c_dim, false);
+    const auto bias_unsq = std::make_shared<default_opset::Unsqueeze>(bias, one);
+    const auto broadcast_bias =
+        std::make_shared<default_opset::Broadcast>(bias_unsq, c_g_div, ov::op::BroadcastType::BIDIRECTIONAL);
+    const auto c_bias = std::make_shared<default_opset::Reshape>(broadcast_bias, c_dim, false);
 
     return {std::make_shared<default_opset::GroupNormalization>(data, c_scale, c_bias, num_groups, eps)};
 }