Parametrize and update tests

training/test_train.py

@@ -6,38 +6,45 @@
 import numpy as np
 
 
-def test_sqli_model():
-    MAX_WORDS = 10000
-    MAX_LEN = 100
+MAX_WORDS = 10000
+MAX_LEN = 100
+
+
+@pytest.fixture
+def model():
 
     # Load dataset
     data = pd.read_csv("dataset/sqli_dataset.csv")
 
     # Load TF model from SavedModel
     sqli_model = load_model("sqli_model/1")
 
-    # Create a sample SQL injection data
-    sample = [
-        "select * from users where id='1' or 1=1--",
-        "select * from users",
-        "select * from users where id=10000",
-        (
-            "select * from test where id=1 UNION ALL "
-            "SELECT NULL FROM INFORMATION_SCHEMA.COLUMNS WHERE 1=0; --;"
-        ),
-    ]
-
     # Tokenize the sample
-    tokenizer = Tokenizer(num_words=MAX_WORDS)
+    tokenizer = Tokenizer(num_words=MAX_WORDS, filters="")
     tokenizer.fit_on_texts(data["Query"])
 
+    return {"tokenizer": tokenizer, "sqli_model": sqli_model}
+
+
+@pytest.mark.parametrize("sample",
+    [
+        ("select * from users where id='1' or 1=1--", [92.02]),
+        ("select * from users", [0.077]),
+        ("select * from users where id=10000", [14.83]),
+        ("select '1' union select 'a'; -- -'", [99.99]),
+        ("select '' union select 'malicious php code' \g /var/www/test.php; -- -';", [99.99]),
+        ("select '' || pg_sleep((ascii((select 'a' limit 1)) - 32) / 2); -- -';", [99.99])
+    ]
+)
+def test_sqli_model(model, sample):
     # Vectorize the sample
-    sample_seq = tokenizer.texts_to_sequences(sample)
+    sample_seq = model["tokenizer"].texts_to_sequences([sample[0]])
     sample_vec = pad_sequences(sample_seq, maxlen=MAX_LEN)
 
     # Predict sample
-    predictions = sqli_model.predict(sample_vec)
+    predictions = model["sqli_model"].predict(sample_vec)
+
     # Scale up to 100
     assert predictions * 100 == pytest.approx(
-        np.array([[99.99], [0.005], [0.055], [99.99]]), 0.1
+        np.array([sample[1]]), 0.1
     )

training/train.py

@@ -25,7 +25,7 @@
 MAX_LEN = 100
 
 # Use Tokenizer to encode text
-tokenizer = Tokenizer(num_words=MAX_WORDS, filters='')
+tokenizer = Tokenizer(num_words=MAX_WORDS, filters="")
 tokenizer.fit_on_texts(data["Query"])
 sequences = tokenizer.texts_to_sequences(data["Query"])