What Is Regression Testing? Definition, Types & Examples

A small software change rarely stays small. Updating a payment API can affect checkout totals, saved cards, refunds, order creation, notifications, and accounting records—even when nobody intentionally changed those features.

Regression testing is how teams find these side effects before users do.

It remains a mix of manual and automated work. According to Katalon’s research among more than 1,500 QA professionals, 82% of teams still use manual testing, while 45% use automated regression testing. The challenge is deciding what to test, how deeply to test it, and how quickly the team needs feedback.

This guide explains regression testing with practical examples, comparison tables, prioritization methods, tools, and workflows for Agile and CI/CD teams.

What is regression testing?

Regression testing is a type of software testing that verifies that recent changes have not broken existing functionality that previously worked.

During regression testing, the team reruns relevant tests after changes such as:

• Adding or modifying a feature;

• Fixing a defect;

• Refactoring code;

• Updating an API or third-party integration;

• Changing configuration or infrastructure;

• Migrating a database;

• Updating a framework, library, browser, or operating system;

• Deploying a new build.

The word “regression” describes a situation in which working software returns to an incorrect or less stable state after a change.

Regression testing does not focus only on the feature that was changed. It also checks connected workflows, shared components, integrations, data, permissions, and business rules that could be affected indirectly.

A simple regression testing example

Suppose an eCommerce team adds support for a new digital wallet. The team must test the new payment method, but that is feature testing – not the entire regression scope.

Regression testing should also confirm that:

• Credit and debit card payments still work;

• Existing saved cards can still be used;

• Discounts, taxes, and delivery fees are calculated correctly;

• An order is created only after a successful payment;

• Inventory is updated once, not twice;

• Failed payments do not create completed orders;

• Customers can still request refunds;

• Transaction information appears in the order history;

• Confirmation emails contain the correct payment details.

The new wallet may work perfectly while a previously supported payment flow fails. Regression testing is designed to detect that kind of unintended impact.

Why regression testing matters

Modern applications contain connected modules, APIs, databases, services, and third-party systems. A change in one part can alter behavior elsewhere.

For example:

• Changing authentication can affect checkout, account settings, and administrative access.

• Updating an address form can affect shipping prices, tax calculations, invoices, and fraud checks.

• Refactoring a shared UI component can break multiple pages.

• Changing a database schema can affect reports and integrations.

• Updating a dependency can introduce compatibility problems without changing the product’s business logic.

Regression testing gives the team evidence that important existing behavior still works after these changes.

It helps teams:

• Detect side effects before release;

• Protect critical user journeys;

• Reduce production incidents;

• Refactor code with greater confidence;

• Release more frequently;

• Verify compatibility after environmental changes;

• Identify affected components that were missed during change analysis.

The difficulty is that a mature product may have thousands of tests, while release windows remain short. In Applause’s 2025 survey, 36.8% of respondents described insufficient time for pre-release testing as very or extremely challenging.

Effective regression testing therefore does not mean blindly running every test after every change. It means selecting the right coverage for the current risk.

Regression testing example: a payment module change

Consider a platform that replaces its payment provider and adds Apple Pay and Google Pay. The change touches the payment service, but its possible impact extends across the purchase lifecycle.

The team should first confirm that the new wallet works. It should then run high-priority regression tests for card payments, totals, order creation, duplicate prevention, and refunds. Lower-risk reporting and presentation checks can follow.

This approach provides useful feedback sooner than treating every existing test as equally urgent.

Regression testing vs retesting vs confirmation testing

Regression testing, retesting, and confirmation testing all involve executing tests again, but their purposes and scopes differ.

In many teams, “retesting” and “confirmation testing” are used as synonyms. When teams distinguish them, confirmation testing usually means verifying a specific defect fix, while retesting can describe the broader act of rerunning a previously failed test.

A typical defect workflow includes both confirmation and regression:

For a deeper comparison, see the guide to the difference between regression and confirmation testing.

Types of regression testing

Regression testing can be classified according to the extent of a change and the required coverage. Terminology varies between organizations, so teams should document what each type means in their own QA process.

Corrective regression testing

Corrective regression uses existing test cases when product requirements remain unchanged. It is appropriate for code cleanup, performance optimization, internal refactoring, and similar technical work that should not alter user-visible behavior.

For example, developers may replace the algorithm used to calculate a cart total. If the pricing requirements have not changed, the QA team can reuse the existing pricing tests.

Progressive regression testing

Progressive regression is required when specifications or expected behavior change. Existing tests must be updated, and new tests should be added for the new requirements.

If a subscription platform introduces prorated plan upgrades, the team must create tests for proration while also checking existing renewals, cancellations, invoices, and payment retries.

Selective regression testing

Selective regression testing uses impact analysis to choose only the tests relevant to a change. It is useful when fast feedback is required and the product’s dependencies are well understood.

The main risk is an incomplete impact assessment. A team might select only payment tests after an API change and overlook that transaction data is also used by reporting and customer support tools.

Partial regression testing

Partial regression covers the changed functionality and the modules with which it directly interacts. Its scope is broader than verifying the change itself but narrower than a complete product regression.

For example, after modifying an address service, the team may test customer profiles, checkout, shipping prices, taxes, and invoices without testing unrelated content-management features.

Complete regression testing

Complete regression reruns the broadest applicable suite. It is usually reserved for:

• Major releases;

• Platform migrations;

• Changes to shared services;

• Large architectural refactoring;

• Significant security or permissions changes;

• Products with strict regulatory requirements;

• Situations in which the impact cannot be confidently isolated.

Complete regression provides wider coverage, but it can be expensive and slow. Teams still need to prioritize execution so that critical failures are reported early.

When should regression testing be performed?

Regression testing should follow any change that could affect existing behavior. The appropriate scope depends on when the tests run and how much risk the change introduces.

Not every build requires a full regression run. A fast build pipeline may execute a focused suite in minutes, while a broader suite runs overnight or before release.

How to perform regression testing step by step

1. Analyze the change

Start with what changed and why. Review the requirement, defect, pull request, technical design, changed configuration, or dependency update.

Do not assess impact from the number of changed lines alone. A one-line permissions change may carry more risk than a large isolated UI refactor.

2. Identify affected components

Map the modified component to its dependencies:

• Which modules call it?

• Which workflows use its data?

• Which roles and permissions are involved?

• Which APIs or third-party services depend on it?

• Which reports, notifications, and background jobs consume its output?

• Does it use shared code or UI components?

This creates an impact map from which the regression scope can be selected.

3. Assess the risk

Estimate both the probability of failure and its potential impact.

A simple model is:

Risk score = likelihood of regression × business impact

Both values can be scored from 1 to 5.

The scores do not need to be mathematically perfect. Their purpose is to make prioritization transparent and consistent.

4. Select regression test cases

Include tests that cover:

• The modified component;

• Critical business workflows;

• Direct integrations;

• Shared services or components;

• Areas with a history of defects;

• Frequently used customer journeys;

• Error handling and recovery;

• Data integrity;

• Roles, permissions, and security boundaries.

Avoid selecting tests only because they already exist. An outdated test suite may provide a large number of passing results without covering the actual risk.

5. Update the test suite

Changes may make some tests obsolete or create new paths that need coverage. Before execution:

• Update expected results;

• Remove duplicate tests;

• Add missing negative scenarios;

• Review test data;

• Mark priority and ownership;

• Identify automation candidates.

A failed outdated test is not a product regression. It is test maintenance work.

6. Prepare the environment and test data

Confirm that the test environment represents the relevant production configuration closely enough for the planned scope.

Prepare data for:

• Valid and invalid inputs;

• Different user roles;

• Existing and new customers;

• Successful and failed transactions;

• Boundary values;

• Integration failures;

• Retries and recovery.

Unstable environments and shared test data can create false failures that make the regression suite difficult to trust.

7. Execute tests in risk order

Start with tests that provide the fastest useful signal:

If a build cannot complete checkout or authentication, the team should know before spending hours on low-priority tests.

8. Investigate failures

A regression test can fail because of:

• A genuine product defect;

• An intended requirement change;

• An outdated test;

• Incorrect test data;

• Environment instability;

• A third-party service problem;

• A flaky automation script.

Do not report every failed automated test as a product bug. First determine what actually failed and whether the application behavior is incorrect.

9. Confirm fixes and rerun affected regression tests

Once a defect is fixed, confirm the original problem and rerun tests for connected functionality. A fix to prevent duplicate payments, for example, may affect retries, timeouts, and order creation.

10. Report release risk

A useful regression report should explain:

• What was tested;

• What was not tested;

• Which environments were used;

• Pass, fail, and blocked results;

• Critical open defects;

• Known flaky tests;

• Remaining product risk;

• The team’s release recommendation.

A pass rate without scope and risk context can be misleading.

How to prioritize regression tests using risk

Risk-based regression testing is especially useful when the team cannot run the complete suite before every release.

Use the following factors to prioritize tests:

Business criticality

What happens if the feature fails? Payments, authentication, data integrity, and access control usually deserve higher priority than cosmetic behavior.

Change exposure

Was the component directly changed, or does it depend on changed code, data, or configuration?

Usage frequency

A failure in a workflow used by most customers usually carries more risk than one in a rarely used administrative option.

Dependency reach

Shared services, libraries, components, and APIs can affect many product areas.

Defect history

Modules with repeated defects deserve additional regression coverage even when the latest change seems small.

Failure detectability and recovery

Can the issue be detected and corrected easily after release, or could it silently corrupt data?

A practical priority model might look like this:

Risk prioritization should not permanently exclude low-priority areas. Lower-risk tests can run on a schedule so that blind spots do not accumulate.

Manual regression testing

Manual regression testing means that testers execute selected scenarios without automation scripts.

It is useful when:

• The feature is new or changing frequently;

• Expected behavior requires human judgment;

• The team is investigating an unfamiliar risk;

• Automation has not yet been created;

• Visual or usability effects must be evaluated;

• A scenario is difficult or expensive to automate;

• The test is unlikely to be repeated often.

Manual testing is particularly valuable near a changed feature. A scripted test validates predefined behavior, while a tester can explore unexpected combinations, question assumptions, and follow suspicious results.

However, large manual regression suites become difficult to scale. They take time, depend on team capacity, and may produce inconsistent results when repetitive checks are performed under deadline pressure.

A sensible manual regression process should still be structured. Testers need a defined scope, priorities, expected results, suitable test data, and a record of what was executed.

Regression testing in Agile and CI/CD

In Agile development, regression testing should happen throughout the sprint rather than being postponed until the final release stage.

A layered CI/CD model provides feedback at different speeds:

This structure prevents the team from waiting until release day to discover that a change from the beginning of the sprint broke an existing workflow.

Regression tests also need clear ownership. When a pipeline test fails, the team should know who investigates it, how quickly it must be reviewed, and when a flaky test should be repaired or removed from the blocking suite.

The 2026 State of Testing report found that 56.4% of teams are evaluated using test coverage and 40.1% using automation coverage, but only 8.6% are evaluated according to business impact. This is an important distinction for CI/CD: the goal is not to run the largest possible suite. It is to provide timely evidence that the release does not put important customer and business outcomes at unacceptable risk.

Automated regression testing

Regression testing is a strong automation candidate because many checks are stable, repeatable, and required after numerous builds.

Good candidates include:

• Critical customer journeys;

• Stable, frequently executed scenarios;

• API and integration checks;

• Data-driven tests;

• Cross-browser checks;

• Permission and role combinations;

• Scenarios that require repetitive setup;

• Tests that need to run in parallel.

Automation is less suitable for rapidly changing features, one-time checks, exploratory testing, or scenarios whose expected results require subjective judgment.

Automation also introduces maintenance work. Test data, selectors, integrations, and expected results must stay aligned with the product. Flaky or outdated tests can slow delivery instead of protecting it.

The World Quality Report 2025–26 found that organizations with test automation had automated an average of only 33% of their test cases. The useful target is therefore not “100% automation.” It is sufficient reliable coverage of repeatable, high-value risks.

For implementation details, framework design, and automation steps, see our automated regression testing guide. Teams that need help designing, stabilizing, or scaling their suite can also use DeviQA’s automated regression testing services.

AI is increasingly used for change-impact analysis, test selection, failure classification, and script maintenance. Our guide explains how AI is changing regression testing and where human review remains necessary.

What regression testing looks like in real projects

Regression improvements should be measured in release speed and risk reduction, not only in the number of scripts created. Several DeviQA engagements illustrate how the approach changes according to the product.

Reducing a seven-day regression cycle to two days

For the Compass real estate platform, regression testing initially took seven days. The project also had limited automated end-to-end and API coverage.

DeviQA developed more than 1,600 automation scripts for smoke and regression testing and integrated them into CI. Regression execution was reduced to two days, overall test coverage reached 85%, and automation coverage reached 80%.

The important result was not simply having more tests. The suite was connected to the delivery process, with relevant tests running against new pull requests.

Supporting daily releases with a two-hour regression run

For the Xola booking platform, the test suite grew to more than 3,200 automation scripts containing over 8,300 checks. Parallel execution and six CI pipelines reduced automated regression testing to approximately two hours.

That made it possible to support one or two releases per day. The case shows why execution architecture matters: a large suite provides limited value if it cannot return results within the release window.

Cutting regression from 2.5 weeks to one day

The Abbott LibreView case involved a healthcare product with 27 localized versions and multiple browsers and devices. Before automation, regression testing took approximately 2.5 weeks.

After the automated infrastructure and a suite of around 1,700 tests were introduced, regression testing took one day. Test coverage reached 90%, while the manual QA team gained more time for new features, test design, and maintenance.

These results are not universal benchmarks. Product complexity, environments, data requirements, suite quality, and available infrastructure all affect regression time. The shared pattern is that teams selected valuable coverage, integrated it into delivery, and treated test maintenance as ongoing engineering work.

Regression testing tools in 2026

There is no single best regression testing tool. The right choice depends on the application, technology stack, test level, team skills, and CI environment.

Tool selection should follow the regression strategy. Switching tools will not solve unclear priorities, poor test data, duplicated coverage, or unstable environments.

Common regression testing mistakes

Running the same suite after every change

A complete suite may be appropriate before a major release but excessive for a small isolated change. Match the scope to the risk and available feedback window.

Testing only the happy path

Regressions frequently appear in failures, retries, timeouts, permissions, boundary values, and integration errors.

Relying only on UI tests

A regression suite built entirely at the UI level is usually slow and difficult to maintain. Use unit, component, API, integration, and UI tests at the most appropriate layers.

Keeping outdated tests

Old tests create false failures and misleading coverage. Review the suite as the product changes.

Ignoring flaky tests

Repeatedly rerunning a flaky test until it passes hides uncertainty. Investigate whether the problem lies in the product, environment, data, or test code.

Measuring only the number of tests

A suite with 5,000 weak tests may protect the product less effectively than 500 carefully selected tests covering critical risks.

Reporting a pass percentage without context

“98% passed” is not enough if the failed 2% includes payment or authentication. Reports should explain affected business flows and residual risk.

Regression testing checklist

Before starting:

• Define the change and its intended behavior.

• Map affected modules, integrations, data, and permissions.

• Assign risk and priority.

• Select relevant existing tests.

• Add tests for changed requirements.

• Review test data and environments.

• Decide which tests will be manual and automated.

During execution:

• Run smoke and critical tests first.

• Record environment and build information.

• Investigate failures instead of automatically rerunning them.

• Check negative, recovery, and boundary scenarios.

• Confirm that integrations and data remain consistent.

• Track blocked and untested areas.

Before release:

• Confirm that critical defects are resolved.

• Rerun tests affected by fixes.

• Document excluded or deferred coverage.

• Review flaky tests and environmental issues.

• Communicate remaining risk.

• Record the release decision and supporting evidence.

Conclusion

Regression testing protects the parts of a product that users already depend on. Its value does not come from rerunning every test after every change. It comes from understanding impact, prioritizing business risk, selecting meaningful coverage, and returning results early enough to influence the release.

Start with critical user journeys and the components affected by the change. Combine targeted manual testing with reliable automation, distribute checks across the CI/CD pipeline, and review the suite as the product evolves.

Done well, regression testing becomes more than a final release gate. It becomes a continuous source of confidence that new development has not compromised existing value.

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Frequently asked questions

What is regression testing in simple terms?

Regression testing checks whether a software change has broken functionality that worked before. It involves rerunning relevant existing tests after a feature, fix, refactoring, integration, or configuration change.

What is an example of regression testing?

After adding a digital wallet to an online store, the team tests the new payment method and reruns existing tests for card payments, discounts, taxes, order creation, refunds, inventory, and notifications.

What is the difference between regression testing and retesting?

Retesting verifies a previously failed scenario after it has been corrected. Regression testing checks whether the change has unintentionally affected other existing functionality.

What are the main types of regression testing?

Common types include corrective, progressive, selective, partial, and complete regression testing. The appropriate type depends on whether requirements changed, how far the impact extends, and how much risk the release carries.

When should regression testing be performed?

Regression testing should be performed after code changes, bug fixes, new features, refactoring, dependency updates, configuration changes, migrations, and infrastructure updates. The scope may range from a small targeted suite on each build to complete regression before a major release.

Can regression testing be performed manually?

Yes. Manual regression testing is useful for new or changing features, exploratory work, visual validation, and scenarios that require human judgment. Repetitive and stable tests are usually stronger candidates for automation.

How much regression testing is enough?

There is no fixed percentage. The suite should cover the changed area, critical business flows, connected components, high-risk integrations, frequently used features, and modules with a history of defects. The remaining risk should be documented when time prevents full coverage.

Should every regression test be automated?

No. Automate stable, repeatable, high-value tests that run frequently. Keep rapidly changing, exploratory, usability-focused, or rarely repeated scenarios manual until automation provides a clear benefit.

About the author

Ievgen Ievdokymov

Senior AQA engineer

Ievgen Ievdokymov is a Senior AQA Engineer at DeviQA, focused on building efficient, scalable testing processes for modern software products.