Fixed coverity issues (#8448)

ngraph/core/include/openvino/op/matmul.hpp

@@ -50,8 +50,8 @@ public:
     }
 
 private:
-    bool m_transpose_a;
-    bool m_transpose_b;
+    bool m_transpose_a{false};
+    bool m_transpose_b{false};
 };
 }  // namespace v0
 }  // namespace op

ngraph/core/include/openvino/op/shuffle_channels.hpp

@@ -49,8 +49,8 @@ public:
 private:
     bool evaluate_shuffle_channels(const HostTensorVector& outputs, const HostTensorVector& inputs) const;
 
-    int64_t m_axis;
-    int64_t m_group;
+    int64_t m_axis{1};
+    int64_t m_group{1};
 };
 }  // namespace v0
 }  // namespace op

ngraph/frontend/onnx/frontend/src/edge_mapper.cpp

@@ -150,7 +150,7 @@ InputEdge onnx_editor::EdgeMapper::find_input_edge(const EditorNode& node, const
 }
 
 OutputEdge onnx_editor::EdgeMapper::find_output_edge(const EditorNode& node, const EditorOutput& out) const {
-    int node_index = node_index = node.m_node_index;
+    int node_index = node.m_node_index;
     if (node_index == -1) {  // the node index is not provided
         // identification can be both based on node name and output name (if the node index is not provided)
         const auto& node_indexes = find_node_indexes(node.m_node_name, node.m_output_name);

ngraph/frontend/onnx/frontend/src/editor.cpp

@@ -183,8 +183,11 @@ public:
         m_infer_shapes_was_run = true;
     }
     ~InferShapesAutoRelease() {
-        if (m_infer_shapes_was_run) {
-            m_model_proto->mutable_graph()->clear_value_info();
+        try {
+            if (m_infer_shapes_was_run) {
+                m_model_proto->mutable_graph()->clear_value_info();
+            }
+        } catch (...) {
         }
     }
 

ngraph/frontend/onnx/frontend/src/utils/random_normal.cpp

@@ -4,6 +4,8 @@
 
 #include "random_normal.hpp"
 
+#include <random>
+
 #include "default_opset.hpp"
 #include "ngraph/opsets/opset8.hpp"
 
@@ -16,6 +18,10 @@ OutputVector make_random_normal(const Output<ngraph::Node>& shape,
                                 float mean,
                                 float scale,
                                 float seed) {
+    std::random_device rd;
+    std::mt19937 gen(rd());
+    std::uniform_int_distribution<uint64_t> distrib(0, 9999);
+
     // We start by generating two random series from a uniform distribution
     const uint64_t global_seed = 0;
 
@@ -23,8 +29,8 @@ OutputVector make_random_normal(const Output<ngraph::Node>& shape,
     const auto op_seed = static_cast<uint64_t>(seed * 1000);
 
     // We need to use two op_seeds to make sure we get different results for two RandomUniform series
-    const uint64_t seed_1 = (op_seed == 0 ? rand() % 10000 : op_seed);
-    const uint64_t seed_2 = (op_seed == 0 ? rand() % 10000 : op_seed + 10000);
+    const uint64_t seed_1 = (op_seed == 0 ? distrib(gen) : op_seed);
+    const uint64_t seed_2 = (op_seed == 0 ? distrib(gen) : op_seed + 10000);
 
     const auto min_val = default_opset::Constant::create(target_type, Shape{1}, {0});
     const auto max_val = default_opset::Constant::create(target_type, Shape{1}, {1});

ngraph/frontend/paddlepaddle/src/op/fill_any_like.cpp

@@ -18,7 +18,11 @@ NamedOutputs fill_any_like(const NodeContext& node) {
         // when type does not define, use the input type
         dtype = x.get_element_type();
     }
-    const auto supported_type = {element::i32, element::i64, element::f16, element::f32, element::f64};
+    const std::vector<element::Type> supported_type = {element::i32,
+                                                       element::i64,
+                                                       element::f16,
+                                                       element::f32,
+                                                       element::f64};
     const bool valid_type =
         std::any_of(supported_type.begin(), supported_type.end(), [dtype](const element::Type& type) {
             return dtype == type;