Test Script Generation Using Machine Learning

Test script generation using machine learning (ML) involves analyzing existing test data, user interactions, and code changes to generate automated test cases. This process helps reduce the manual effort needed to write test cases and ensures better coverage of edge cases and potential defects.

Here’s a simple example of how machine learning can be used to generate test scripts in Python. This example will show how we can use ML to predict and generate test cases based on past data.

Example Code: Machine Learning for Test Case Generation

# Importing necessary libraries
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn import metrics
import pandas as pd

# Simulating a dataset for the book ordering system
data = {
    'user_name': [1, 1, 0, 0, 1, 0, 0, 1],  # 1: Valid, 0: Invalid
    'email': [1, 0, 1, 1, 0, 1, 0, 1],     # 1: Valid, 0: Invalid
    'address': [1, 1, 1, 0, 1, 1, 0, 0],   # 1: Valid, 0: Invalid
    'book_id': [123, 124, 125, 126, 123, 127, 125, 128],  # Example book IDs
    'quantity': [1, 2, 1, 1, 0, 2, 1, 3],  # Quantity of books ordered
    'payment_valid': [1, 1, 0, 0, 1, 0, 1, 1],  # 1: Valid payment, 0: Invalid
    'order_outcome': [1, 1, 0, 0, 0, 0, 1, 1]  # 1: Pass, 0: Fail
}

# Creating a DataFrame
df = pd.DataFrame(data)

# Features (inputs: user_name, email, address, payment_valid, quantity) and Labels (order_outcome)
X = df[['user_name', 'email', 'address', 'payment_valid', 'quantity']]  # Features
y = df['order_outcome']  # Label

# Split the dataset into training and testing sets (80% training, 20% testing)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Initialize and train the Decision Tree Classifier
clf = DecisionTreeClassifier()
clf = clf.fit(X_train, y_train)

# Predict outcomes for the test set
y_pred = clf.predict(X_test)

# Output the accuracy of the model
print("Accuracy:", metrics.accuracy_score(y_test, y_pred))

# Now we will use the model to generate test cases for new orders
new_orders = pd.DataFrame({
    'user_name': [1, 0],       # New orders with valid and invalid user names
    'email': [1, 1],           # Both orders have valid emails
    'address': [1, 0],         # One order with a valid address, one with invalid
    'payment_valid': [1, 0],   # One valid payment, one invalid payment
    'quantity': [2, 1]         # Quantities ordered
})

# Predict outcomes for the new orders
predicted_outcomes = clf.predict(new_orders)

# Generate test cases based on the model's predictions
for i, row in new_orders.iterrows():
    outcome = 'Pass' if predicted_outcomes[i] == 1 else 'Fail'
    print(f"Generated Test Case {i+1}:")
    print(f"  Input: user_name={row['user_name']}, email={row['email']}, address={row['address']}, "
          f"payment_valid={row['payment_valid']}, quantity={row['quantity']}")
    print(f"  Expected Outcome: {outcome}")
    print("\n")

** TRY IT YOURSELF ** Google Colab Link

Explanation of the Code

Dataset:

The dataset (data) includes user information (user_name, email, address), book ordering information (book_id, quantity), and payment status (payment_valid).
The order_outcome column indicates whether the order succeeded (1) or failed (0).

Model Training:

We use a Decision Tree Classifier from the scikit-learn library to train on the dataset. The model learns to predict the success or failure of an order based on the input fields.

Test Case Generation:

After training the model, we generate new orders and predict whether they will pass or fail. These predictions are used to automatically generate test cases, which include the inputs (user and order details) and the expected outcomes (pass/fail).

Example Output:

Accuracy: 1.0
Generated Test Case 1:
  Input: user_name=1, email=1, address=1, payment_valid=1, quantity=2
  Expected Outcome: Pass

Generated Test Case 2:
  Input: user_name=0, email=1, address=0, payment_valid=0, quantity=1
  Expected Outcome: Fail

Key Points

Data Features:

The model is trained on several features, such as user information, book quantity, and payment status. These features help the model predict whether an order will pass or fail.

Real-Life Application:

This model can be used in real-life scenarios where you want to test the reliability of an ordering system. You can simulate different inputs and use the machine learning model to predict and generate test cases.

Scalability:

This example can be extended to include more features such as discount codes, shipping methods, or multiple items in the order. More sophisticated machine learning models, such as random forests or neural networks, could also be used for more complex systems.

Real-World Benefits

Regression Testing:

The model can be used to automatically generate regression tests for new features or updates in the ordering system.

Edge Case Detection:

The system can predict unusual or problematic inputs that may cause the application to fail, helping testers focus on critical areas.

Continuous Learning:

As more data is gathered, the model can be continuously improved to generate even more accurate test cases.

Mapping to TMap/ISTQB

In the context of TMap/ISTQB, the example provided for generating test cases using machine learning can be classified as part of the “Specification-based Testing” techniques, specifically under the Data-driven Testing and Regression Testing categories.

Here’s a breakdown of how this fits into TMap:

1. Specification-based Testing:

This form of testing is based on defined requirements or functional specifications of the system. In this case, the test cases are generated based on input parameters such as user information, payment validation, and quantity ordered, which are essentially part of the system’s functional specifications.

2. Data-driven Testing:

  • In the example, test cases are generated based on various input combinations of user_name, email, address, payment_valid, and quantity.
  • These different data combinations are used to test various aspects of the order process.
  • In TMap, data-driven testing is focused on using different data sets to test how the application behaves under different conditions. This aligns with what the machine learning model is doing by using historical data to predict and generate new test cases.

3. Regression Testing:

  • The primary goal of the machine learning model in this scenario is to ensure that the ordering system continues to work as expected with different combinations of input data.
  • Regression Testing in TMap focuses on verifying that previously developed and tested functionality still works after a change or update in the system.
  • The machine learning model helps by predicting whether new test cases (representing new inputs) will pass or fail based on historical outcomes. This aligns with the need for continuous and automated testing in a regression context.

4. Risk-based Testing (Optional):

Although not explicitly stated in the example, AI-based prediction models can be used to implement Risk-based Testing, which is a key principle in TMap. In Risk-based Testing, tests are prioritized based on the risk of failure in specific areas of the system.

  • If the ML model were to predict that certain input combinations are more likely to fail based on past data (for example, invalid payment details), these tests could be prioritized to mitigate risk.
  • This helps to ensure that critical areas are tested first.