diff --git a/test/test_pipeline/test_base_component.py b/test/test_pipeline/test_base_component.py
new file mode 100644
index 000000000..cf3c25afe
--- /dev/null
+++ b/test/test_pipeline/test_base_component.py
@@ -0,0 +1,89 @@
+from ConfigSpace.configuration_space import ConfigurationSpace
+from ConfigSpace.hyperparameters import CategoricalHyperparameter, UniformIntegerHyperparameter
+
+import pytest
+
+from autoPyTorch.pipeline.components.base_component import ThirdPartyComponents, autoPyTorchComponent
+
+
+class DummyComponentRequiredFailuire(autoPyTorchComponent):
+    _required_properties = {'required'}
+
+    def __init__(self, random_state=None):
+        self.fitted = False
+        self._cs_updates = {}
+
+    def fit(self, X, y):
+        self.fitted = True
+        return self
+
+    def get_properties(dataset_properties=None):
+        return {"name": 'DummyComponentRequiredFailuire',
+                "shortname": "Dummy"}
+
+
+class DummyComponentExtraPropFailuire(autoPyTorchComponent):
+
+    def __init__(self, random_state=None):
+        self.fitted = False
+        self._cs_updates = {}
+
+    def fit(self, X, y):
+        self.fitted = True
+        return self
+
+    def get_properties(dataset_properties=None):
+        return {"name": 'DummyComponentExtraPropFailuire',
+                "shortname": 'Dummy',
+                "must_not_be_there": True}
+
+
+class DummyComponent(autoPyTorchComponent):
+    def __init__(self, a=0, b='orange', random_state=None):
+        self.a = a
+        self.b = b
+        self.fitted = False
+        self.random_state = random_state
+        self._cs_updates = {}
+
+    def get_hyperparameter_search_space(self, dataset_properties=None):
+        cs = ConfigurationSpace()
+        a = UniformIntegerHyperparameter('a', lower=10, upper=100, log=False)
+        b = CategoricalHyperparameter('b', choices=['red', 'green', 'blue'])
+        cs.add_hyperparameters([a, b])
+        return cs
+
+    def fit(self, X, y):
+        self.fitted = True
+        return self
+
+    def get_properties(dataset_properties=None):
+        return {"name": 'DummyComponent',
+                "shortname": 'Dummy'}
+
+
+def test_third_party_component_failure():
+    _addons = ThirdPartyComponents(autoPyTorchComponent)
+
+    with pytest.raises(ValueError, match=r"Property required not specified for .*"):
+        _addons.add_component(DummyComponentRequiredFailuire)
+
+    with pytest.raises(ValueError, match=r"Property must_not_be_there must not be specified for algorithm .*"):
+        _addons.add_component(DummyComponentExtraPropFailuire)
+
+    with pytest.raises(TypeError, match=r"add_component works only with a subclass of .*"):
+        _addons.add_component(1)
+
+
+def test_set_hyperparameters_not_found_failure():
+    dummy_component = DummyComponent()
+    dummy_config_space = dummy_component.get_hyperparameter_search_space()
+    success_configuration = dummy_config_space.sample_configuration()
+    dummy_config_space.add_hyperparameter(CategoricalHyperparameter('c', choices=[1, 2]))
+    failure_configuration = dummy_config_space.sample_configuration()
+    with pytest.raises(ValueError, match=r"Cannot set hyperparameter c for autoPyTorch.pipeline "
+                                         r"DummyComponent because the hyperparameter does not exist."):
+        dummy_component.set_hyperparameters(failure_configuration)
+    with pytest.raises(ValueError, match=r"Cannot set init param r for autoPyTorch.pipeline "
+                                         r"DummyComponent because the init param does not exist."):
+        dummy_component.set_hyperparameters(success_configuration, init_params={'r': 1})
diff --git a/test/test_pipeline/test_tabular_classification.py b/test/test_pipeline/test_tabular_classification.py
index eb1ffd494..52288b199 100644
--- a/test/test_pipeline/test_tabular_classification.py
+++ b/test/test_pipeline/test_tabular_classification.py
@@ -13,8 +13,10 @@
 import pytest
 
 import torch
+from torch.optim.lr_scheduler import _LRScheduler
 
 from autoPyTorch.pipeline.components.setup.early_preprocessor.utils import get_preprocess_transforms
+from autoPyTorch.pipeline.components.setup.lr_scheduler.NoScheduler import NoScheduler
 from autoPyTorch.pipeline.tabular_classification import TabularClassificationPipeline
 from autoPyTorch.utils.common import FitRequirement
 from autoPyTorch.utils.hyperparameter_search_space_update import HyperparameterSearchSpaceUpdates, \
@@ -223,6 +225,7 @@ def test_network_optimizer_lr_handshake(self, fit_dictionary_tabular):
         # No error when network is passed
         X = pipeline.named_steps['optimizer'].fit(X, None).transform(X)
         assert 'optimizer' in X
+        assert isinstance(pipeline.named_steps['optimizer'].choice.get_optimizer(), torch.optim.Optimizer)
 
         # Then fitting a optimizer should fail if no network:
         assert 'lr_scheduler' in pipeline.named_steps.keys()
@@ -234,7 +237,13 @@ def test_network_optimizer_lr_handshake(self, fit_dictionary_tabular):
 
         # No error when network is passed
         X = pipeline.named_steps['lr_scheduler'].fit(X, None).transform(X)
-        assert 'optimizer' in X
+        assert 'lr_scheduler' in X
+        if isinstance(pipeline.named_steps['lr_scheduler'].choice, NoScheduler):
+            pytest.skip("This scheduler does not support `get_scheduler`")
+        lr_scheduler = pipeline.named_steps['lr_scheduler'].choice.get_scheduler()
+        if isinstance(lr_scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau):
+            pytest.skip("This scheduler is not a child of _LRScheduler")
+        assert isinstance(lr_scheduler, _LRScheduler)
 
     def test_get_fit_requirements(self, fit_dictionary_tabular):
         dataset_properties = {'numerical_columns': [], 'categorical_columns': [],