The user experience of an app can often generate impacts in unexpected areas. As the industry shifts to a rapid development cycle with more new features being released rapidly and a growing number of code changes each month, development teams find it difficult to deliver new capabilities while also maintaining stability. The question is not whether regression testing is necessary. It is how to decide which form of regression testing is best for a given change.
If an organization chooses a regression test that is not appropriate for the new capability being released, time and money are wasted. However, if the organization chooses the right type of regression testing, it can deliver new features quickly and ensure that end users maintain confidence in the organization’s product(s). This article will cover the various types of regression testing, how and when to apply each of those types, and provide a decision matrix that will assist you in selecting the best approach to take in your organization.
What are the Types of Regression Testing?
There are several common types of regression testing described below, along with an overview of when to use each type and examples for context.
1. Corrective Regression Testing
Corrective regression testing occurs when there is no significant change to the existing codebase, and Retesting of earlier validated test cases, which remain valid. Instead of needing teams to design new test cases for that release, teams can simply re-run previously validated test cases to verify heuristics are still working correctly.
When to Use: Corrective testing requires less time and energy than designing new tests and is performed to verify minor changes or low-level flaws, which do not require changes to the underlying business logic.
Example: Removing a misspelling inside a payment gateway that has no effect on the checkout process.
2. Retest-All Regression Testing
Retesting all means executing every single test case within the regression test suite—including those unrelated to the change in question—for maximum coverage and extreme confidence in system reliability. While this requires a lot of time and resources, it is recommended in situations where there is zero tolerance for failure.
When to Use: Before launching a major product release that carries with it an extremely low risk of failure.
Example: Running the entire test suite prior to globally rolling out the subscription feature to all customers globally.
3. Selective Regression Testing
Selective regression testing only involves running tests that are logically related to the code that changed. This is a testing strategy that reduces testing overhead, since we know the scope of impact is limited logically, instead of testing the entire system. Each of these tests should be selected based on dependency mapping or test execution history.
When to Use: When a limited change is made that is known to affect only a narrow scope of the application.
Example: Updating the logic for authentication token refresh, but only running tests related to login, MFA, or access control.
4. Progressive Regression Testing
Progressive regression testing is necessary when the introduction of new features makes previous test cases obsolete or insufficient. Progressive regression testing requires developing new test cases based upon the new features or changes and assuring that updated functionality does not disrupt the old process.
When to Use: When you add features or change requirements that change existing behavior, while also introducing new functionality paths.
Example: Adding a “recurring delivery” process as part of an upgrade to the checkout process changes existing test flows.
5. Complete Regression Testing
Complete regression testing verifies the entire application end-to-end after a significant amount of code changes have been implemented or the architecture has been changed. This assures the entire product is still functioning correctly and will be as stable in a production environment.
When to Use: After large releases, database migrations, or upgrades of technology, or multiple patches over time.
Example: Migrating a back end from monolith to microservices, and testing all business modules at that point in time.
6. Partial Regression Testing
Partial regression testing involves checking the modified module and the modules it is tightly coupled with. This is useful because when changes are made with a localized impact, those changes can inherently have cascading effects across different touch points in the system.
When To Use: When changes in a module have dependencies or shared logic with other modules.
Example: Any changes to shipping fee calculation would require discounts, tax and invoice totals to be verified.
7. Unit Regression Testing
Unit regression testing occurs during unit testing, which assures that an individual code unit has not broken an internal modification. This is done in isolation in order to ensure that the updated code does not break the internal logic of that specific method, function, or class.
When To Use: Developers develop unit regression tests when they do refactoring or optimization in a unit during the build process.
Example: A code unit refactored by a developer to optimize its performance in terms of price would only have the refactored unit tested while it was being built. This would include using a mock for all dependencies of that unit.
8. Automated Regression Testing
Automated regression testing involves using scripts or automation tools to run regression suites continuously with no human intervention. Automated regression testing speeds up release cycles, improves testing efficiency, and guarantees consistent testing coverage (especially when working with CI/CD pipelines). While automation requires an initial investment into the setup, it greatly reduces the long-term effort.
When to Use: Automated regression testing is best used when changes are coming in frequently, releases are being made quickly, and repetitive test suites require continuous execution.
Example: Running automated UI and API regression tests on all nightly builds to identify early failures.
Regression Testing Approaches Comparison Side-by-Side
Choosing the best regression approach should be based on the extent to which the system has changed, the speed at which you expect the release to occur, and how much risk the team can handle. The table below will help make the recommendation easier, comparing all of the major regression techniques in real-world terms.
| Type | Best Use Case | Release Speed | Risk Coverage | Cost | Example |
|---|---|---|---|---|---|
| Corrective | Stable code changes | Fast | Medium | Low | Fixing UI label |
| Retest-All | Major release | Slow | Very High | Very High | Full product rollout |
| Selective | Targeted changes | Fast | High | Low | Updating auth token |
| Progressive | New features altering flows | Medium | High | Medium | One-Tap Checkout |
| Complete | Architectural overhaul | Slow | Very High | High | Migration to microservices |
| Partial | Shared logic dependencies | Medium | High | Medium | Shipping rule change |
| Unit | Refactoring small component | Fast | Medium | Very Low | Updated pricing function |
| Automated | CI/CD pipelines | Very Fast | High | Medium (setup) | Nightly automated tests |
How to Choose the Right Regression Testing Type — Decision Matrix
Having a solid understanding of the different types of regression testing, teams still have difficulty deciding which to use for each release. That's where a decision matrix is useful; it helps make a sensible test selection and prevents over-testing (wasting time), under-testing (missing defects).
The following matrix matches actual development scenarios to the best-suited regression technique based on scope, timelines for the release, and risk.
| Situation | Change Scope | Timeline | Recommended Type | Reason |
|---|---|---|---|---|
| Bug fix | Small | Short | Corrective | No logic impact |
| Feature update | Medium | Normal | Progressive + Selective | Protects new and old flows |
| Local module change | Medium | Fast | Partial | Limited dependencies |
| Backend logic rewrite | Large | Tight | Risk-Based Selective + Automated | High coverage in little time |
| UI + backend revamp | Large | Long | Retest-All / Complete | Maximum assurance |
| Microservices update | Small services | CI/CD | Unit + Automated | Fast recurring tests |
Regression Testing Best Practices
Identify which test cases should be prioritized based on risk to the business being tested and how often the functions are used.
To avoid bottlenecking the process, begin automating tests that are run often, and/or tests that are executed repetitively.
Make sure your test data is clean and the test environments are consistent.
Apply change impact analysis to help avoid unnecessary test execution.
Try to incorporate regression testing at the beginning of the CI/CD pipeline, rather than waiting until just before a release.
Occasionally review and retire tests that are no longer relevant or are outdated, to maintain a lean test suite.
How Keploy Supports Regression Testing?
Conventional regression testing is primarily reliant on the writing of the test case, fuzzing or mocking data, and some level of expected coverage, which typically leads to gaps between QA environments and interactions with the real user journeys.
Keploy closes the gap in coverage by capturing live production traffic and converting it all into repeatable test cases and mocked data. So instead of testing based on assumptions, teams are testing based on actual, real user interaction.
Replay-based testing helps in facilitating accurate test case selection, reducing noise with flaky tests, and adding reliability in extremely fast release cycles. Keploy integrates with CI/CD workflows, version-controlled repositories, and microservice infrastructure, making it appropriate for backend- or API-heavy products. As a result, teams automatically receive an evolving, always up-to-date regression suite after every release, without spending a significant engineering effort, especially when the product is changing.
Conclusion
Regression testing is not about trying to run more tests; it is about running the right tests at the right time and scope. Each of the types of regression testing has a role to play based on the levels of complexity and risk, and the urgency of release. If you can pair your testing strategy to your business priorities and use some smart techniques like automation and real traffic-based replay, you can protect the stability of your software without slowing down your innovation.
As software becomes more modular and our release cycles become shorter, the future of regression testing is about precision, automation, and creating validation based on production. Teams that embrace this will ship faster, break less, and deliver a consistently reliable experience for all of their users, even as they iterate on their products.
FAQs
Which type of regression testing reduces the most risk?
Retest-all and complete regression testing minimize the most risk as they test or validate the entire application, but risk-based selective regression testing in combination with automation is at a sweet spot of risk vs time for fast-moving teams.
How to handle regression testing in continuous integration?
Use automated regression test suites whenever a change is made to the code (git commit) or on nightly builds (having all automated regression tests run at that time). Always run the high-priority tests first. Regression testing in CI/CD provides quick feedback on the system and helps avoid defect build-up.
What is the difference between regression and non-regression testing?
Regression testing ensures that your new changes haven't impacted the existing functionality. Non-regression testing tests only the new feature to see if it works and does not check the rest of the system.
What is the right time to perform regression testing?
Regression tests should be conducted after bug fixes, feature changes, automated testing refactored, upgrading dependencies, making configuration changes, or any release that would impact the stability of the system.
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