diff --git a/test/scripts/dummy_audio_folder.py b/test/scripts/dummy_audio_folder.py
index aeeaba0..ae30b76 100644
--- a/test/scripts/dummy_audio_folder.py
+++ b/test/scripts/dummy_audio_folder.py
@@ -35,8 +35,14 @@ class DummyAudioFolder:
         return min + (max - min) * np.random.random() * pr.buffer_t
 
     def generate_samples(self, folder, name, value, duration):
+        """Generate sample file.
+
+        The file is generated in the specified folder, with the specified name,
+        dummy value and duration.
+        """
         for i in range(self.count):
-            save_audio(join(folder, name.format(i)), np.array([value] * int(duration * pr.sample_rate)))
+            save_audio(join(folder, name.format(i)),
+                       np.array([value] * int(duration * pr.sample_rate)))
 
     def subdir(self, *parts):
         folder = self.path(*parts)
diff --git a/test/scripts/test_combined.py b/test/scripts/test_combined.py
index 6d02ead..175a8cc 100644
--- a/test/scripts/test_combined.py
+++ b/test/scripts/test_combined.py
@@ -26,18 +26,27 @@ def read_content(filename):
 
 
 def test_combined(train_folder, train_script):
+    """Test a "normal" development cycle, train, evaluate and calc threshold.
+    """
     train_script.run()
     params_file = train_folder.model + '.params'
     assert isfile(train_folder.model)
     assert isfile(params_file)
 
-    EvalScript.create(folder=train_folder.root, models=[train_folder.model]).run()
+    EvalScript.create(folder=train_folder.root,
+                      models=[train_folder.model]).run()
 
+    # Ensure that the graph script generates a numpy savez file
     out_file = train_folder.path('outputs.npz')
-    graph_script = GraphScript.create(folder=train_folder.root, models=[train_folder.model], output_file=out_file)
+    graph_script = GraphScript.create(folder=train_folder.root,
+                                      models=[train_folder.model],
+                                      output_file=out_file)
     graph_script.run()
     assert isfile(out_file)
 
+    # Esure the params are updated after threshold is calculated
     params_before = read_content(params_file)
-    CalcThresholdScript.create(folder=train_folder.root, model=train_folder.model, input_file=out_file).run()
+    CalcThresholdScript.create(folder=train_folder.root,
+                               model=train_folder.model,
+                               input_file=out_file).run()
     assert params_before != read_content(params_file)
diff --git a/test/scripts/test_engine.py b/test/scripts/test_engine.py
index 560548f..09129cd 100755
--- a/test/scripts/test_engine.py
+++ b/test/scripts/test_engine.py
@@ -36,6 +36,10 @@ class FakeStdout:
 
 
 def test_engine(train_folder, train_script):
+    """
+    Test t hat the output format of the engina matches a decimal form in the
+    range 0.0 - 1.0.
+    """
     train_script.run()
     with open(glob.glob(join(train_folder.root, 'wake-word', '*.wav'))[0], 'rb') as f:
         data = f.read()
diff --git a/test/scripts/test_train.py b/test/scripts/test_train.py
index 696c5d7..20e1ccb 100644
--- a/test/scripts/test_train.py
+++ b/test/scripts/test_train.py
@@ -22,15 +22,20 @@ from test.scripts.dummy_audio_folder import DummyAudioFolder
 class DummyTrainFolder(DummyAudioFolder):
     def __init__(self, count=10):
         super().__init__(count)
-        self.generate_samples(self.subdir('wake-word'), 'ww-{}.wav', 1.0, self.rand(0, 2 * pr.buffer_t))
-        self.generate_samples(self.subdir('not-wake-word'), 'nww-{}.wav', 0.0, self.rand(0, 2 * pr.buffer_t))
-        self.generate_samples(self.subdir('test', 'wake-word'), 'ww-{}.wav', 1.0, self.rand(0, 2 * pr.buffer_t))
-        self.generate_samples(self.subdir('test', 'not-wake-word'), 'nww-{}.wav', 0.0, self.rand(0, 2 * pr.buffer_t))
+        self.generate_samples(self.subdir('wake-word'), 'ww-{}.wav', 1.0,
+                              self.rand(0, 2 * pr.buffer_t))
+        self.generate_samples(self.subdir('not-wake-word'), 'nww-{}.wav', 0.0,
+                              self.rand(0, 2 * pr.buffer_t))
+        self.generate_samples(self.subdir('test', 'wake-word'), 'ww-{}.wav',
+                              1.0, self.rand(0, 2 * pr.buffer_t))
+        self.generate_samples(self.subdir('test', 'not-wake-word'),
+                              'nww-{}.wav', 0.0, self.rand(0, 2 * pr.buffer_t))
         self.model = self.path('model.net')
 
 
 class TestTrain:
     def test_run_basic(self):
+        """Run a training and check that a model is generated."""
         folders = DummyTrainFolder(10)
         script = TrainScript.create(model=folders.model, folder=folders.root)
         script.run()