Regression Testing Strategies: Selective and Complete Regression

In the dynamic field of software development, maintaining the integrity of applications amidst continuous changes is a significant challenge. This necessitates the adoption of regression testing, a pivotal software testing type that ensures new changes or enhancements don’t disrupt existing functionalities. In this comprehensive blog, we look into two critical methodologies of regression testing: Selective and Complete Regression Testing, exploring their nuances, advantages, disadvantages, complemented by detailed examples.

What is Regression Testing?

Regression testing is a quality assurance practice crucial for verifying that previously developed and tested software still performs as expected after any modifications. This type of testing is integral in various development methodologies, particularly Agile and DevOps, where frequent changes are a norm. In Continuous Integration/Continuous Deployment (CI/CD) pipelines, regression tests are automated and run regularly, providing immediate feedback on the impact of code changes.

Complete Regression Testing

Complete Regression Testing entails re-executing the entire suite of test cases. It’s exhaustive and guarantees comprehensive coverage of the application’s functionalities.

Scenarios for Complete Regression Testing

Major System Overhaul: Complete regression is indispensable when significant changes or updates are made to the system.

Poorly Documented Changes: When changes lack clear documentation, complete regression ensures all functionalities are checked for potential impacts.

Uncertain Impact: If the impact of changes on existing features is ambiguous, complete regression is the safest approach.

Advantages and Disadvantages

Advantages:

• Guarantees thorough testing, uncovering hidden bugs.
• Ideal for complex systems with interconnected components.

Disadvantages:

• Time-consuming and resource-intensive.
• May include unnecessary testing, leading to inefficiency.

An Example Scenario

Consider an enterprise resource planning (ERP) system undergoing a major update. Here, complete regression testing is crucial to ensure that all modules – finance, HR, supply chain – function seamlessly post-update.

Selective Regression Testing

Selective Regression Testing involves executing a portion of the total test cases, focusing on areas most likely to be affected by the recent changes.

Criteria for Selecting Test Cases

Risk-Based: Focus on functionalities with high failure impact.

Requirement-Based: Target areas directly related to recent changes.

Historical Data: Prioritize functionalities with frequent past failures.

Advantages and Disadvantages

Advantages:

• Efficient and less time-consuming.
• Direct focus on the most impactful areas.

Disadvantages:

• Potential to overlook affected areas not included in the selected tests.
• Requires deep analysis to identify the right test cases.

An Example Scenario

In a mobile application, if a new feature is added to enhance user interface (UI) elements, selective regression can focus on UI-related test cases and the new feature’s integration with existing functionalities.

Test Case Prioritization in Selective Regression Testing

Prioritizing test cases is crucial for optimizing the effectiveness of selective regression testing. Methods include code-coverage analysis, risk-based prioritization, and utilizing historical data.

Prioritization Example in Python

python

# Python code for test case prioritization based on historical failure rates

test_cases = [{‘id’: 1, ‘failure_rate’: 0.1}, {‘id’: 2, ‘failure_rate’: 0.5}, {‘id’: 3, ‘failure_rate’: 0.2}]

def prioritize_test_cases(test_cases):

    return sorted(test_cases, key=lambda x: x[‘failure_rate’], reverse=True)

prioritized_test_cases = prioritize_test_cases(test_cases)

print(prioritized_test_cases)

This script sorts test cases based on historical failure rates, ensuring cases with higher failure rates are tested first.

Tools and Frameworks for Regression Testing

Selenium

Selenium is a powerful tool for automating web browsers, enabling testing of web applications across different browsers and platforms.

python

# Selenium Python example for a simple login test

from selenium import webdriver

driver = webdriver.Chrome()

driver.get(“https://www.example.com/login”)

username = driver.find_element_by_id(“username”)

password = driver.find_element_by_id(“password”)

submit = driver.find_element_by_id(“submit”)

username.send_keys(“user1”)

password.send_keys(“passwd”)

submit.click()

# Assertions to verify successful login

QTP/UFT

QuickTest Professional (QTP), now Unified Functional Testing (UFT), is a commercial tool by Micro Focus used for functional and regression testing. It supports a wide range of applications and is known for its robust record and playback features.

TestComplete

TestComplete is a commercial automated UI testing tool supporting various scripting languages like JavaScript, Python, and VBScript. It’s known for its object recognition engine and ability to create complex automated tests.

Pros:

• Increased efficiency and accuracy.
• Reduces manual testing efforts and time.

Cons:

• Requires time to learn and master.
• Involves initial setup and maintenance costs.
• Implementing an Effective Regression Testing Strategy
• An effective regression testing strategy encompasses:

Advanced Techniques in Regression Testing

Beyond the traditional methods of regression testing, advanced techniques leverage modern technologies and strategies to enhance the testing process. One such approach is the implementation of machine learning (ML) algorithms to predict which test cases are most likely to uncover new defects based on historical data and code changes. This method can significantly optimize the test suite and improve the efficiency of both selective and complete regression testing.

Predictive Test Case Selection Example in Python

python

import pandas as pd

from sklearn.ensemble import RandomForestClassifier

# Sample dataset: Historical test case execution and failure data

data = {‘test_case_id’: [1, 2, 3, 4, 5],

        ‘code_changes_related’: [1, 0, 0, 1, 1],

        ‘previous_failures’: [3, 1, 0, 4, 2],

        ‘failure_probability’: [0.75, 0.25, 0, 0.8, 0.5]}

df = pd.DataFrame(data)

# Feature columns and target column

features = [‘code_changes_related’, ‘previous_failures’]

target = ‘failure_probability’

# Random Forest Classifier

clf = RandomForestClassifier()

clf.fit(df[features], df[target])

# Predicting failure probability for new test cases

new_test_cases = pd.DataFrame({‘code_changes_related’: [0, 1],

                               ‘previous_failures’: [2, 3]})

predictions = clf.predict(new_test_cases)

print(predictions)

This Python script uses a RandomForestClassifier from Scikit-learn to predict the failure probability of new test cases. The model is trained on historical data, including whether test cases are related to recent code changes and their previous failure rates. Such predictive models can guide testers in prioritizing test cases for regression testing, especially in selective regression scenarios.

With the integration of advanced techniques like machine learning, regression testing becomes more intelligent and adaptive, further enhancing the effectiveness and efficiency of software testing strategies.

Hypothetical Scenario

Imagine a financial software company releasing an update to its core banking system while also fixing several bugs. The update includes changes to the transaction processing module and user interface enhancements. The testing team implements a mixed strategy of Selective and Complete Regression Testing. Selective Regression focuses on the transaction module and UI enhancements, ensuring the new features work seamlessly with existing functionalities. For the bug fixes, since they are dispersed across various modules, Complete Regression Testing is conducted to ensure overall system stability.

This approach ensured comprehensive coverage, optimized resource utilization, and successfully mitigated the risk of post-deployment issues.

Conclusion

Regression testing, both Selective and Complete, is indispensable in the realm of software development for ensuring software reliability and performance consistency. The choice between them hinges on various factors, including the scope of changes, project constraints, and resource availability. An adept understanding and application of these strategies enable teams to deliver high-quality software products efficiently. The field of software testing is continually evolving, and staying informed and adaptable is key to a successful testing strategy.