diff --git a/tests/test_msel_oracle.py b/tests/test_msel_oracle.py
index 8a28567b0..3eaef27d2 100644
--- a/tests/test_msel_oracle.py
+++ b/tests/test_msel_oracle.py
@@ -17,6 +17,15 @@
 from domainlab.utils.utils_cuda import get_device
 
 
+def mk_model(task):
+    backbone = torchvisionmodels.resnet.resnet50(weights=ResNet50_Weights.IMAGENET1K_V2)
+    num_final_in = backbone.fc.in_features
+    backbone.fc = nn.Linear(num_final_in, task.dim_y)
+
+    # specify model to use
+    model = mk_erm(list_str_y=task.list_str_y)(backbone)
+    return model
+
 def mk_exp(
     task,
     model,
@@ -70,10 +79,7 @@ def mk_exp(
     return exp
 
 
-def test_msel_oracle():
-    """
-    return trainer, model, observer
-    """
+def mk_task():
     task = mk_task_dset(isize=ImSize(3, 28, 28), dim_y=10, taskna="custom_task")
     task.add_domain(
         name="domain1",
@@ -90,8 +96,15 @@ def test_msel_oracle():
         dset_tr=DsetMNISTColorSoloDefault(4),
         dset_val=DsetMNISTColorSoloDefault(5),
     )
+    return task
+
 
+def test_msel_oracle():
+    """
+    return trainer, model, observer
+    """
     # specify backbone to use
+    task = mk_task()
     backbone = torchvisionmodels.resnet.resnet50(weights=ResNet50_Weights.IMAGENET1K_V2)
     num_final_in = backbone.fc.in_features
     backbone.fc = nn.Linear(num_final_in, task.dim_y)
@@ -110,24 +123,8 @@ def test_msel_oracle1():
     """
     return trainer, model, observer
     """
-    task = mk_task_dset(isize=ImSize(3, 28, 28), dim_y=10, taskna="custom_task")
-    task.add_domain(
-        name="domain1",
-        dset_tr=DsetMNISTColorSoloDefault(0),
-        dset_val=DsetMNISTColorSoloDefault(1),
-    )
-    task.add_domain(
-        name="domain2",
-        dset_tr=DsetMNISTColorSoloDefault(2),
-        dset_val=DsetMNISTColorSoloDefault(3),
-    )
-    task.add_domain(
-        name="domain3",
-        dset_tr=DsetMNISTColorSoloDefault(4),
-        dset_val=DsetMNISTColorSoloDefault(5),
-    )
-
-    # specify backbone to use
+    task = mk_task()
+   # specify backbone to use
     backbone = torchvisionmodels.resnet.resnet50(weights=ResNet50_Weights.IMAGENET1K_V2)
     num_final_in = backbone.fc.in_features
     backbone.fc = nn.Linear(num_final_in, task.dim_y)
@@ -150,23 +147,7 @@ def test_msel_oracle2():
     """
     return trainer, model, observer
     """
-    task = mk_task_dset(isize=ImSize(3, 28, 28), dim_y=10, taskna="custom_task")
-    task.add_domain(
-        name="domain1",
-        dset_tr=DsetMNISTColorSoloDefault(0),
-        dset_val=DsetMNISTColorSoloDefault(1),
-    )
-    task.add_domain(
-        name="domain2",
-        dset_tr=DsetMNISTColorSoloDefault(2),
-        dset_val=DsetMNISTColorSoloDefault(3),
-    )
-    task.add_domain(
-        name="domain3",
-        dset_tr=DsetMNISTColorSoloDefault(4),
-        dset_val=DsetMNISTColorSoloDefault(5),
-    )
-
+    task = mk_task()
     # specify backbone to use
     backbone = torchvisionmodels.resnet.resnet50(weights=ResNet50_Weights.IMAGENET1K_V2)
     num_final_in = backbone.fc.in_features
@@ -184,30 +165,8 @@ def test_msel_oracle3():
     """
     return trainer, model, observer
     """
-    task = mk_task_dset(isize=ImSize(3, 28, 28), dim_y=10, taskna="custom_task")
-    task.add_domain(
-        name="domain1",
-        dset_tr=DsetMNISTColorSoloDefault(0),
-        dset_val=DsetMNISTColorSoloDefault(1),
-    )
-    task.add_domain(
-        name="domain2",
-        dset_tr=DsetMNISTColorSoloDefault(2),
-        dset_val=DsetMNISTColorSoloDefault(3),
-    )
-    task.add_domain(
-        name="domain3",
-        dset_tr=DsetMNISTColorSoloDefault(4),
-        dset_val=DsetMNISTColorSoloDefault(5),
-    )
-
-    # specify backbone to use
-    backbone = torchvisionmodels.resnet.resnet50(weights=ResNet50_Weights.IMAGENET1K_V2)
-    num_final_in = backbone.fc.in_features
-    backbone.fc = nn.Linear(num_final_in, task.dim_y)
-
-    # specify model to use
-    model = mk_erm(list_str_y=task.list_str_y)(backbone)
+    task = mk_task()
+    model = mk_model(task)
 
     exp = mk_exp(
         task,
@@ -226,30 +185,9 @@ def test_msel_oracle4():
     """
     return trainer, model, observer
     """
-    task = mk_task_dset(isize=ImSize(3, 28, 28), dim_y=10, taskna="custom_task")
-    task.add_domain(
-        name="domain1",
-        dset_tr=DsetMNISTColorSoloDefault(0),
-        dset_val=DsetMNISTColorSoloDefault(1),
-    )
-    task.add_domain(
-        name="domain2",
-        dset_tr=DsetMNISTColorSoloDefault(2),
-        dset_val=DsetMNISTColorSoloDefault(3),
-    )
-    task.add_domain(
-        name="domain3",
-        dset_tr=DsetMNISTColorSoloDefault(4),
-        dset_val=DsetMNISTColorSoloDefault(5),
-    )
-
+    task = mk_task()
+    model = mk_model(task)
     # specify backbone to use
-    backbone = torchvisionmodels.resnet.resnet50(weights=ResNet50_Weights.IMAGENET1K_V2)
-    num_final_in = backbone.fc.in_features
-    backbone.fc = nn.Linear(num_final_in, task.dim_y)
-
-    # specify model to use
-    model = mk_erm(list_str_y=task.list_str_y)(backbone)
     exp = mk_exp(
         task,
         model,
@@ -261,5 +199,5 @@ def test_msel_oracle4():
     )
     exp.execute(num_epochs=2)
     exp.trainer.observer.model_sel.msel.best_loss = 0
-    exp.trainer.observer.model_sel.msel.update(epoch = 1, clear_counter=True)
+    exp.trainer.observer.model_sel.msel.update(epoch=1, clear_counter=True)
     del exp