diff --git a/src/sdk/pynni/nni/evo_nas_tuner/evo_nas_tuner.py b/src/sdk/pynni/nni/evo_nas_tuner/evo_nas_tuner.py
index adbf9609ab..3b41dbefc6 100644
--- a/src/sdk/pynni/nni/evo_nas_tuner/evo_nas_tuner.py
+++ b/src/sdk/pynni/nni/evo_nas_tuner/evo_nas_tuner.py
@@ -148,7 +148,7 @@ def _random_model(self):
 
     def _mutate_model(self, model):
         new_individual = copy.deepcopy(model.parameters)
-        mutate_key = random.choice(new_individual.keys())
+        mutate_key = random.choice(list(new_individual.keys()))
         mutate_val = self.search_space[mutate_key]
         if mutate_val['_type'] == 'layer_choice':
             idx = random.randint(0, len(mutate_val['_value']) - 1)
diff --git a/src/sdk/pynni/tests/assets/classic_nas_search_space.json b/src/sdk/pynni/tests/assets/classic_nas_search_space.json
new file mode 100644
index 0000000000..bac470b72f
--- /dev/null
+++ b/src/sdk/pynni/tests/assets/classic_nas_search_space.json
@@ -0,0 +1,26 @@
+{
+  "first_conv": {
+    "_type": "layer_choice",
+    "_value": [
+      "conv5x5",
+      "conv3x3"
+    ]
+  },
+  "mid_conv": {
+    "_type": "layer_choice",
+    "_value": [
+      "0",
+      "1"
+    ]
+  },
+  "skip": {
+    "_type": "input_choice",
+    "_value": {
+      "candidates": [
+        "",
+        ""
+      ],
+      "n_chosen": 1
+    }
+  }
+}
diff --git a/src/sdk/pynni/tests/test_classic_nas.py b/src/sdk/pynni/tests/test_classic_nas.py
new file mode 100644
index 0000000000..1982be67bf
--- /dev/null
+++ b/src/sdk/pynni/tests/test_classic_nas.py
@@ -0,0 +1,90 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+import glob
+import json
+import logging
+import os
+import random
+import shutil
+import sys
+from collections import deque
+from unittest import TestCase, main
+
+from nni.tuner import Tuner
+from nni.evo_nas_tuner.evo_nas_tuner import EvoNasTuner
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger('test_tuner')
+
+
+class ClassicNasTestCase(TestCase):
+    def setUp(self):
+        self.test_round = 3
+        self.params_each_round = 50
+        self.exhaustive = False
+
+    def check_range(self, generated_params, search_space):
+        for params in generated_params:
+            for k in params:
+                v = params[k]
+                items = search_space[k]
+                if items['_type'] == 'layer_choice':
+                    self.assertIn(v['_value'], items['_value'])
+                elif items['_type'] == 'input_choice':
+                    for choice in v['_value']:
+                        self.assertIn(choice, items['_value']['candidates'])
+                else:
+                    raise KeyError
+
+    def send_trial_result(self, tuner, parameter_id, parameters, metrics):
+        tuner.receive_trial_result(parameter_id, parameters, metrics)
+        tuner.trial_end(parameter_id, True)
+
+    def search_space_test_one(self, tuner_factory, search_space):
+        tuner = tuner_factory()
+        self.assertIsInstance(tuner, Tuner)
+        tuner.update_search_space(search_space)
+        for i in range(self.test_round):
+            queue = deque()
+            parameters = tuner.generate_multiple_parameters(list(range(i * self.params_each_round,
+                                                                       (i + 1) * self.params_each_round)),
+                                                            st_callback=self.send_trial_callback(queue))
+            logger.debug(parameters)
+            self.check_range(parameters, search_space)
+            for k in range(min(len(parameters), self.params_each_round)):
+                self.send_trial_result(tuner, self.params_each_round * i + k, parameters[k], random.uniform(-100, 100))
+            while queue:
+                id_, params = queue.popleft()
+                self.check_range([params], search_space)
+                self.send_trial_result(tuner, id_, params, random.uniform(-100, 100))
+            if not parameters and not self.exhaustive:
+                raise ValueError("No parameters generated")
+
+    def send_trial_callback(self, param_queue):
+        def receive(*args):
+            param_queue.append(tuple(args))
+        return receive
+
+    def search_space_test_all(self, tuner_factory):
+        # Since classic tuner should support only LayerChoice and InputChoice,
+        # ignore type and fail type are dismissed here. 
+        with open(os.path.join(os.path.dirname(__file__), "assets/classic_nas_search_space.json"), "r") as fp:
+            search_space_all = json.load(fp)
+        full_supported_search_space = dict()
+        for single in search_space_all:
+            space = search_space_all[single]
+            single_search_space = {single: space}
+            self.search_space_test_one(tuner_factory, single_search_space)
+            full_supported_search_space.update(single_search_space)
+        logger.info("Full supported search space: %s", full_supported_search_space)
+        self.search_space_test_one(tuner_factory, full_supported_search_space)
+
+    def test_evo_nas_tuner(self):
+        tuner_fn = lambda: EvoNasTuner()
+        self.search_space_test_all(tuner_fn)
+
+
+if __name__ == '__main__':
+    main()
+