diff --git a/test/integration/models/test_model.py b/test/integration/models/test_model.py
index 09873db356..a8684e8964 100644
--- a/test/integration/models/test_model.py
+++ b/test/integration/models/test_model.py
@@ -461,22 +461,44 @@ def test_locf_forecast_correctly():
     assert np.array_equal(predict_forecast.predict, np.array([[110, 120, 130, 110]]))
 
 
-def test_models_does_not_fall_on_constant_data():
+@pytest.mark.parametrize('operation', OperationTypesRepository('all')._repo, ids=lambda x: x.id)
+def test_models_does_not_fall_on_constant_data(operation):
     """ Run models on constant data """
     # models that raise exception
     to_skip = ['custom', 'arima', 'catboost', 'catboostreg', 'cgru',
                'lda', 'fast_ica', 'decompose', 'class_decompose']
+    if operation.id in to_skip:
+        return
 
-    for operation in OperationTypesRepository('all')._repo:
-        if operation.id in to_skip:
-            continue
-        for task_type in operation.task_type:
-            for data_type in operation.input_types:
-                data = get_data_for_testing(task_type, data_type,
-                                            length=100, features_count=2,
-                                            random=False)
-                if data is not None:
+    for task_type in operation.task_type:
+        for data_type in operation.input_types:
+            data = get_data_for_testing(task_type, data_type,
+                                        length=100, features_count=2,
+                                        random=False)
+            if data is not None:
+                nodes_from = []
+                if task_type is TaskTypesEnum.ts_forecasting:
+                    if 'non_lagged' not in operation.tags:
+                        nodes_from = [PipelineNode('lagged')]
+                node = PipelineNode(operation.id, nodes_from=nodes_from)
+                pipeline = Pipeline(node)
+                pipeline.fit(data)
+                assert pipeline.predict(data) is not None
+
+
+@pytest.mark.parametrize('operation', OperationTypesRepository('all')._repo, ids=lambda x: x.id)
+def test_operations_are_serializable(operation):
+    to_skip = ['custom', 'decompose', 'class_decompose']
+    if operation.id in to_skip:
+        return
 
+    for task_type in operation.task_type:
+        for data_type in operation.input_types:
+            data = get_data_for_testing(task_type, data_type,
+                                        length=100, features_count=2,
+                                        random=True)
+            if data is not None:
+                try:
                     nodes_from = []
                     if task_type is TaskTypesEnum.ts_forecasting:
                         if 'non_lagged' not in operation.tags:
@@ -484,33 +506,10 @@ def test_models_does_not_fall_on_constant_data():
                     node = PipelineNode(operation.id, nodes_from=nodes_from)
                     pipeline = Pipeline(node)
                     pipeline.fit(data)
-                    assert pipeline.predict(data) is not None
-
-
-def test_operations_are_serializable():
-    to_skip = ['custom', 'decompose', 'class_decompose']
-
-    for operation in OperationTypesRepository('all')._repo:
-        if operation.id in to_skip:
-            continue
-        for task_type in operation.task_type:
-            for data_type in operation.input_types:
-                data = get_data_for_testing(task_type, data_type,
-                                            length=100, features_count=2,
-                                            random=True)
-                if data is not None:
-                    try:
-                        nodes_from = []
-                        if task_type is TaskTypesEnum.ts_forecasting:
-                            if 'non_lagged' not in operation.tags:
-                                nodes_from = [PipelineNode('lagged')]
-                        node = PipelineNode(operation.id, nodes_from=nodes_from)
-                        pipeline = Pipeline(node)
-                        pipeline.fit(data)
-                        serialized = pickle.dumps(pipeline, pickle.HIGHEST_PROTOCOL)
-                        assert isinstance(serialized, bytes)
-                    except NotImplementedError:
-                        pass
+                    serialized = pickle.dumps(pipeline, pickle.HIGHEST_PROTOCOL)
+                    assert isinstance(serialized, bytes)
+                except NotImplementedError:
+                    pass
 
 
 def test_operations_are_fast():
@@ -534,7 +533,7 @@ def test_operations_are_fast():
             reference_time = tuple(map(min, zip(perfomance_values, reference_time)))
 
     for operation in OperationTypesRepository('all')._repo:
-        if (operation.id not in to_skip and operation.presets and FAST_TRAIN_PRESET_NAME in operation.presets):
+        if operation.id not in to_skip and operation.presets and FAST_TRAIN_PRESET_NAME in operation.presets:
             for _ in range(attempt):
                 perfomance_values = get_operation_perfomance(operation, data_lengths)
                 # if attempt is successful then stop
@@ -548,7 +547,6 @@ def test_all_operations_are_documented():
     # All operations and presets should be listed in `docs/source/introduction/fedot_features/automation_features.rst`
     to_skip = {'custom', 'data_source_img', 'data_source_text', 'data_source_table', 'data_source_ts', 'exog_ts'}
     path_to_docs = fedot_project_root() / 'docs/source/introduction/fedot_features/automation_features.rst'
-    docs_lines = None
 
     with open(path_to_docs, 'r') as docs_:
         docs_lines = docs_.readlines()