Regression testing is а crucial part of the software development life cycle. It involves repeatedly testing an application after changes have been made to ensure that existing functionality still works as expected. However, as an application evolves, Regression testing can become increasingly complex. Keeping your regression tests up-to-date is essential for detecting bugs and ensuring quality, but it’s also one of the biggest challenges in testing.
In this article, we’ll provide а brief overview of some effective strategies to keep Regression testing current. Following these best practices can optimize your Regression testing efforts so you spend less time maintaining tests and more time enhancing test coverage. The result is faster feedback on regressions and more reliable software releases.
Some effective strategies to keep Regression testing up-to-date
- Visual Testing for Regression Testing
Visual testing is а technique that can help keep regression testing up-to-date by automating visual comparisons of the application’s user interface.
With visual testing, the system captures screenshots of the UI during test execution. It then compares these new screenshots to baseline screenshots captured in previous test runs. Any visual differences, like changes in button position, font sizes, or images, can indicate а regression in the UI.
Let’s say а website is undergoing development and visual testing is а part of the regression testing process. During the first test run, the tool captures screenshots of the homepage, including the header, navigation bar, and footer. These screenshots are saved as baseline images.
Now, the development team makes changes to the navigation bar, altering its color and position. When the second test run is executed, the automated tool captures new screenshots of the homepage, including the modified navigation bar.
The tool compares the new screenshots with the baseline images. Since the navigation bar has been intentionally changed, the pixel comparison shows а difference. However, this is not а regression, but а legitimate UI update. Testers update the baseline images to account for the new navigation bar appearance.
In another instance, during the third test run, the tool captures screenshots again, but this time, the footer’s alignment has been unintentionally altered, resulting in а visual regression compared to the baseline images. This is а genuine issue and needs to be reported to the development team for resolution.
With LambdaTest’s SmartUI, the system captures screenshots of the homepage during test execution and saves them as baseline images. Now, as the development team makes changes to the navigation bar, SmartUI intelligently detects intentional updates, avoiding false alarms.
With the power of pixel-by-pixel comparison, SmartUI ensures that any unintended alterations, such as misaligned footers, are promptly identified and flagged as genuine regressions. By promptly addressing these issues, your team can collaborate seamlessly and deliver visually perfect applications to users across multiple browsers and devices.
- Service Virtualization for Regression Testing
Service Virtualization is а technique that enables testers to mimic dependencies such as APIs and databases, creating simulated test environments for regression testing. By replicating the behavior of these dependencies, testers can execute tests against the virtualized services, reducing the need for maintaining complex stubs or mocks. This approach ensures faster and more reliable regression testing, even when actual dependencies are unavailable or evolving.
Consider а scenario where а web application relies on an external API to fetch user data. For regression testing, the tester needs to verify how the application performs with different types of user data. However, accessing the real API may be slow or costly, and its availability might be limited during testing.
Using service virtualization, the tester records typical interactions with the API, such as retrieving user data for various scenarios (e.g., new user, existing user, multiple users). The recorded behavior is then used to create virtual services that mimic the API responses.
Now, during regression testing, the application is directed to interact with the virtualized API instead of the real one. The tester can validate the application’s behavior with different user data without relying on the actual API’s availability.
With LambdaTest’s Service Virtualization, you can create virtual services that accurately mimic your APIs and databases. LambdaTest fosters seamless collaboration between your development and testing teams. Stay informed about dependency changes and adapt your virtual services accordingly, ensuring consistent testing across the board.
- Model-Based Testing for Regression Testing
Model-based testing utilizes behavioral models that represent the expected functionality of an application. These models can then be leveraged to generate test cases for regression testing automatically.
Some examples:
State Machine Models: The different states an application can be in are modeled along with the transitions between them. New test cases can be generated by applying tools to traverse the state machine.
Decision Table Models: All the logical conditions and resulting actions of а function are mapped in а table. Test tools generate combinations of inputs to cover each decision rule.
User Workflow Models: Typical user workflows are modeled using а visual notation. Updates to the workflows can produce new user journey test scenarios.
Usage Models: Probabilistic models of how users interact with features can drive random model-based testing.
When the application changes, the models can be updated to represent the new or modified functionality. Running model-based testing tools again generates а new set of test cases adapted to the changes.
LambdaTest is а cloud-based digital experience testing platform that makes test automation faster and easier. It allows running tests across а massive grid of 3000+ browsers, browser versions, and operating systems.
LambdaTest offers smart test intelligence features that can optimize regression test creation and maintenance. One capability is LambdaTest Snapshots, which auto-captures DOM states during test execution. Any changes in the interface can be pinpointed as regression faults for rapid debugging.
- Impact Analysis for Regression Testing
Impact analysis involves evaluating code changes to identify which specific parts of the system are likely to be affected. This allows focusing regression testing on only those tests that are relevant to the changes.
For example:
– If а new field is added to а form, only tests related to that form need to be run rather than the full regression suite.
– Changing а database schema may only require re-running the related back-end tests rather than all UI tests.
– Refactoring а component’s internal implementation can be validated by its unit tests before rerunning integration and end-to-end tests.
Impact analysis requires tracing code dependencies to map out how changes can propagate through the system. Test selection relies on this understanding of code change impact.
This selective regression testing strategy significantly reduces maintenance effort. Only those test cases that are directly relevant based on code change impact need to be updated and run. Test selection is precise and optimized.
LambdaTest’s platform can supplement test optimization with impact analysis by providing high-velocity test execution. LambdaTest HyperExecute is а smart test orchestration platform that can help optimize test execution.
HyperExecute provides а hosted infrastructure of test runners spanning major operating systems – Windows, MacOS and Linux. The Linux runners come in Docker containers for flexible configuration.
- Test Case Design Techniques for Regression testing
Carefully designing test cases using techniques like boundary value analysis can maximize test coverage and defect finding ability with а minimal set of test cases. This helps keep regression testing lean and maintainable.
For example:
Boundary Values: Testing boundary conditions like maximum length, 0 values, limits, etc. has а high chance of exposing bugs.
Equivalence Partitioning: Dividing inputs into valid and invalid classes reduces cases needed for coverage.
Decision Tables: Modelling inputs/actions in rules helps generate optimal combinations to cover all decisions.
State Transition Testing: Design test cases to cover each valid state change in the system.
Use Case Testing: Base test cases on actual user workflow scenarios to maximize real-world coverage.
LambdaTest’s detailed test analytics can supplement optimized test case design. Powerful analytics provide insight into which tests are finding more defects. Tests can be prioritized, strengthened or removed based on actual effectiveness data.
- Test Data Generators for Regression testing
Manually maintaining and updating test data sets for regression testing can be time-consuming. Test data generators streamline this by intelligently creating parameterized test data.
For example:
Realistic Name Generator: Automatically generates random but valid names, addresses, phone numbers, etc. for testing forms.
Test Credit Card Numbers: Generates fake credit card numbers that pass checksum validation for e-commerce testing.
Custom Data Fakers: Create data generators tailored to application-specific entities like products, users, geographies, etc.
Bulk Data Factories: Produces high volumes of consistent, structured test data for load and performance testing.
By incorporating test data generation, testers avoid spending time manually updating rigid datasets. Tests can work with any volume of fresh, valid test data on demand.
LambdaTest offers intelligent test analytics that can enhance test data generation. Powerful analytics identify gaps in test data coverage. Metrics pinpoint areas where additional or better test data is needed to improve coverage.
Conclusion
Regression testing is а crucial activity for validating software quality throughout development. However, keeping regression test suites current as code evolves poses challenges. Outdated and unmaintained tests fail to detect regressions effectively.
Various strategies exist to optimize regression testing and make maintenance easier. Prioritizing automation, designing optimized test cases, generating test data, and integrating with development workflows are some key techniques.
