Intelligent Test Case Prioritization

Intelligent Test Case Prioritization

Intelligent Test Case Prioritization is a process in software testing where test cases are ranked and executed based on various factors such as risk, probability of failure, criticality, and the impact of code changes. The primary goal is to optimize the testing process by running the most important tests first, ensuring that the most critical defects are found early. By leveraging AI and machine learning techniques, this prioritization can be enhanced to make smarter, data-driven decisions, leading to faster feedback loops and more efficient use of resources.

Why Test Case Prioritization is Important:

1. Time and Resource Constraints:

2. Faster Defect Detection:

3. Optimized Regression Testing:

Factors Considered in Intelligent Test Case Prioritization:

1. Code Changes:

2. Historical Data:

3. Risk Assessment:

4. Execution Time:

5. Code Complexity:

6. Criticality of Features:

Machine Learning Techniques for Intelligent Test Case Prioritization:

1. Supervised Learning for Defect Prediction:

2. Clustering Algorithms:

3. Reinforcement Learning for Dynamic Prioritization:

4. Bayesian Networks for Risk-based Testing:

Example of Implementing Test Case Prioritization in Python:

Sample Rows as Test Cases

Here’s how each row (test case) looks with the code_block and additional context. Each row represents a test case that is associated with a specific part of the application. Key attributes such as Code Change, Risk Level, Execution Time, and Failure Rate are used to prioritize test cases.

Test Case Code Block Code Change Risk Level Execution Time (s) Failure Rate Test Outcome
1 authentication 1 3 120 0.80 1
2 user_profile 0 2 60 0.10 0
3 checkout 1 3 180 0.80 1
4 reporting_tool 1 1 240 0.30 1
5 notification_service 0 2 60 0.20 0
6 dashboard 0 1 30 0.05 0
7 payment_gateway 1 3 150 0.90 1
8 settings 0 2 90 0.40 0

Explanation of Each Attribute

Sample Analysis of Priority

This structured data enables efficient prioritization for testing by focusing on areas with the highest likelihood of failure or impact on user experience We can implement a simple version of test case prioritization using a Decision Tree Classifier. The idea is to predict which test cases are likely to fail based on historical data and prioritize them for execution.

Dataset Example:

Features:

Label:

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

# Simulated dataset with adjustments for expected outcome
data = {
    'code_block': [
        'authentication', 'user_profile', 'checkout', 'reporting_tool', 
        'notification_service', 'dashboard', 'payment_gateway', 'settings'
    ],  # Module or component in the application
    'code_change': [1, 0, 1, 1, 0, 0, 1, 0],  # 1: Code changed, 0: No change
    'risk_level': [3, 2, 3, 1, 2, 1, 3, 2],  # 1: Low, 2: Medium, 3: High
    'execution_time': [120, 60, 180, 240, 60, 30, 150, 90],  # Time in seconds to execute each test case
    'failure_rate': [0.8, 0.1, 0.8, 0.3, 0.2, 0.05, 0.9, 0.4],  # Historical failure rate for each test case
    'test_outcome': [1, 0, 1, 1, 0, 0, 1, 0]  # 1: Failure (High priority), 0: Pass (Low priority)
}



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

# Features and Labels
X = df[['code_change', 'risk_level', 'execution_time', 'failure_rate']]  # Features
y = df['test_outcome']  # Label

# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42, stratify=y)

# 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))

# Define a function to assign priority labels based on the model's prediction
def assign_priority(test_case_data):
    """Assign priority based on risk level and failure rate."""
    risk_level = test_case_data['risk_level']
    failure_rate = test_case_data['failure_rate']

    if risk_level == 3 and failure_rate >= 0.7:
        return "High"
    elif risk_level == 2 or failure_rate >= 0.3:
        return "Medium"
    else:
        return "Low"

# Create a list to hold the test case priority information
prioritized_test_cases = []

# Store the test case index, data, and assigned priority
for i, (index, test_case) in enumerate(X_test.iterrows(), start=1):
    priority_label = assign_priority(test_case)
    prioritized_test_cases.append((index + 1, priority_label))

# Sort the test cases by priority (e.g., "High", "Medium", "Low")
priority_order = {"High": 1, "Medium": 2, "Low": 3}
prioritized_test_cases.sort(key=lambda x: priority_order[x[1]])

# Print the sorted prioritized test cases
print("\nPrioritized Test Cases (Sorted by Priority):")
for test_case_num, priority_label in prioritized_test_cases:
    print(f"Test Case {test_case_num} - Priority: {priority_label}")

Accuracy: 0.75

Prioritized Test Cases (Sorted by Priority):
Test Case 1 - Priority: High
Test Case 2 - Priority: Medium
Test Case 4 - Priority: Medium
Test Case 6 - Priority: Low

** TRY IT YOURSELF ** Google Colab Link

Explanation

The decision tree is trained using features like code changes, risk level, execution time, and historical failure rates to predict the outcome of test cases.
Once trained, the model can be used to predict the likelihood of a test case failing, which helps in prioritizing high-risk tests for execution.

Benefits of Intelligent Test Case Prioritization:

1. Reduced Test Execution Time:

2. Faster Feedback in CI/CD:

3. Resource Optimization:

4. Increased Test Coverage:

5. Proactive Defect Detection:

if we translate this to ISTQB terms

1. Risk-Based Testing:

2. Test Prioritization:

3. Test Effectiveness: