Techniques Used in Regression Testing

1. Retest All

This is the most straightforward and comprehensive technique. As the name implies, it involves re-executing the entire existing test suite.

Re-testing v/s Regression Testing :  Retesting is to test the software/ application when a defect is fixed to ensure that the original defect is completely removed while regression testing is performed to make sure that no new defects are introduced when a new feature is developed or existing feature is changed.

• When to Use It: This approach is best reserved for major, high-risk changes to the application, such as a large-scale refactoring or a change to the core architecture.
• Pros: Provides the highest level of assurance and a complete check of the entire application.
• Cons: It is extremely time-consuming and expensive, making it impractical for the frequent releases common in Agile.

2. Selective Regression Testing

This is a more strategic and commonly used approach. Instead of running every test, the team selects a subset of existing test cases to execute based on the nature of the code change.

• When to Use It: This is the standard approach for most day-to-day changes in an Agile environment.
• How It Works: The selection is based on analyzing the impact of the change. If a change was made to the payment processing module, the team would select test cases related to payments, the shopping cart, and order confirmation, as these are the areas most likely to be affected.
• Pros: It provides a good balance between test coverage and efficiency, making it much faster than the “Retest All” approach.
• Cons: Its effectiveness depends entirely on the team’s ability to accurately identify the impacted areas of the application. There is a risk that an unforeseen dependency could be missed.

3. Test Case Prioritization

This technique is often used in conjunction with selective testing. It involves prioritizing the selected test cases to run the most critical ones first.

• When to Use It: This is particularly useful when testing time is extremely limited.
• How It Works: Test cases are prioritized based on their business impact and the criticality of the functionality they cover. For example, a test for “user login” would be run before a test for “changing profile picture.”
• Pros: It provides the fastest possible feedback on the health of the application’s most critical features.
• Cons: It provides less overall coverage and should be supplemented with a more comprehensive run when time allows.

4. Modular Testing

When to Use It:
Ideal when your application has recurring workflows like login, add to cart, checkout, navigation, or record creation. Perfect for large regression suites that need maintainability and reusability.

How It Works:
Tests are broken into smaller reusable components such as “login”, “search”, “add item”, or “submit form”. Larger regression tests call these modules rather than repeating the same steps. If a module changes, updating it once updates every test that uses it.

Pros:

• Reduces maintenance because changes are made in one module and reflected everywhere
  
• Enables scalable test creation for growing applications
  
• Improves debugging since failures can be traced to specific components
  
• Encourages clean, structured regression suites

Cons:

• Requires an upfront investment to design modules properly
  
• Poorly structured modules can lead to dependency issues
  
• Teams must enforce consistency across all modules

5. Data Driven Testing

When to Use It:
Best when the same test must run with multiple inputs, user roles, product types, currencies, or region-specific data. Essential for SaaS, enterprise apps, eCommerce, and workflow-heavy systems.

How It Works:
A single test is linked to external datasets (CSV, Excel, JSON, databases). The test runs once for each row of data. For example, “Create Order” can automatically test multiple customer types, discount structures, and regions in one execution cycle.

Pros:

• Greatly increases coverage without multiplying test cases
  
• Reduces duplication because the workflow is written once
  
• Supports complex scenarios across different users, inputs, and configurations
  
• Makes regression testing more realistic and robust

Cons:

• Requires clean, well-maintained test data
  
• Test failures can be harder to isolate when many data rows are involved
  
• Poorly structured datasets can produce noisy or false-positive failures

6. Visual Testing

When to Use It:
Ideal for UI-heavy applications, branding-sensitive products, dashboards, eCommerce stores, and any product where layout and design must remain consistent. Especially valuable when CSS updates or UI framework upgrades occur.

How It Works:
The tool takes screenshots during testing and compares them to stored baseline images. Differences are highlighted as visual diffs. Teams review and approve changes or flag regressions. This catches layout, spacing, color, icon, and rendering issues that functional tests cannot detect.

Pros:

• Detects UI issues that functional tests miss
  
• Ensures accurate rendering across browsers and devices
  
• Prevents brand or layout inconsistencies
  
• Great for catching pixel-level changes and visual regressions

Cons:

• Can generate false positives if baseline screenshots are not well maintained
  
• Requires storage and organization of visual baselines
  
• May need human review for visual diffs, especially after intentional UI updates