9 Types of Regression Testing in Software Testing

Regression testing protects work you’ve already shipped. Every code change, configuration update and dependency upgrade can break something that used to work, often in a part of the application nobody touched.

Plenty of teams still run regression the slow way: long manual passes, results that arrive days after the change, and a hope that nothing slips through. Defects that escape to production are far more expensive to fix than those caught during development, so a slow or patchy regression process costs money as well as time.

There’s no single “regression test”, though. Different types of regression testing do different jobs. Complete regression checks everything but takes the longest. Selective regression is faster but depends on choosing well. Smoke and sanity checks give quick answers on narrow questions. Knowing which type to run, and when, is what makes releases predictable.

The 9 Types of Regression Testing Explained

1. Unit Regression Testing

Unit regression testing reruns unit tests after a change to confirm individual functions, methods and classes still work. It’s the first and fastest layer of regression.

• When to use it: on every commit, before code is merged

• Scope: single units of code, with dependencies replaced by mocks

• Duration: milliseconds per test, with full suites usually finishing in minutes

• Example: a developer changes a payment calculation function, and unit tests confirm tax, discount and total calculations still return the right values for known inputs

• Strengths: very fast feedback, inside the developer’s normal workflow

• Limitations: can’t catch integration problems, UI issues or broken workflows

• Best practice: keep each test fast, run the suite before every merge, and aim for strong coverage of business logic

AI can help here by suggesting edge cases developers miss, and mutation testing tools can show whether unit tests would actually catch a change in the logic.

2. Partial Regression Testing

Partial regression testing checks the modules a change affects, plus the parts of the application that depend on them, while skipping unrelated areas.

• When to use it: when a change is limited to a specific module and its impact is well understood

• Scope: modified components and their direct integrations

• Duration: usually hours, depending on the size of the module

• Example: new discount logic in the shopping cart leads to testing the cart, pricing and checkout, while account management, the product catalogue and reporting are left out

• Strengths: much faster than complete regression, while still checking where the change is most likely to cause trouble

• Limitations: relies on an accurate picture of dependencies, so hidden links between modules can let defects through

• Best practice: keep dependency maps current, and add a handful of checks on high-risk workflows outside the module

3. Complete Regression Testing (Full Regression)

‍Complete regression testing, sometimes called retest-all, runs the entire regression suite across every feature, workflow and integration.

• When to use it: before major releases, after architectural changes or platform upgrades, and before critical business periods such as peak trading or year-end close

• Scope: the full automated suite

• Duration: days if run by hand, hours when automated and run in parallel

• Example: after a payment gateway upgrade, complete regression checks browsing, basket management, checkout, payment, fulfilment, returns and account management

• Strengths: the highest confidence, including against side effects in areas that seemed unrelated

• Limitations: time-consuming and expensive if not automated, and more than a minor change needs

• Best practice: automate the full suite, run it on every release candidate, and run it regularly on integration environments so problems surface early

4. Selective Regression Testing (Risk-Based Testing)

Selective regression testing runs a chosen subset of tests, picked according to what changed, where defects have appeared before and which workflows carry the most business risk.

• When to use it: every sprint or merge, when there isn’t time for a full run

• Scope: tests covering changed code paths, high-risk areas and critical business processes

• Duration: minutes to hours, depending on how many tests are selected

• Example: an update to fraud detection rules leads to running payment, fraud and transaction tests, while catalogue, registration and marketing tests are skipped

• Strengths: a good balance of speed and confidence

• Limitations: the quality of the selection decides the quality of the result, and a poor choice can miss serious defects

• Best practice: always include critical business process tests, base selection on change data and defect history, and update the criteria when production defects show a gap

Some tools use code changes, defect history and past results to suggest which tests to run. Treat those suggestions as a starting point, and keep a fixed set of critical journeys that always run.

5. Progressive Regression Testing

Progressive regression testing checks new functionality together with the existing features it connects to, making sure an addition doesn’t break what’s already there.

• When to use it: when a new feature, module or significant capability is added to an existing application

• Scope: the new feature in full, plus existing features that interact with it or depend on it

• Duration: hours to days, depending on the feature

• Example: an online bank adds the ability to top up an existing loan, so testing covers the new journey plus existing loan statements, repayment schedules and account balances

• Strengths: focused on where new work and old work meet, so it’s more efficient than complete regression

• Limitations: depends on a clear view of how the new feature connects to existing ones, and subtle interactions can be missed

• Best practice: write tests for new features during development, include tests that cross from new to existing functionality, and fold them into the selective suite for later releases

6. Corrective Regression Testing (Retest-All)

Corrective regression testing reuses existing test cases when the code has changed but the requirements haven’t. Because the expected behaviour is the same, the current tests can be rerun as they are.

• When to use it: after refactoring, performance work, library upgrades or internal fixes that shouldn’t change what users see

• Scope: existing tests for the affected areas, used without modification

• Duration: minutes to hours

• Example: a team rewrites the code that calculates delivery dates for better performance, then reruns the existing delivery and checkout tests to confirm the results haven’t changed

• Strengths: no new tests to write, so it’s quick to set up and run

• Limitations: only works when requirements are unchanged, so it won’t cover new behaviour

• Best practice: use it whenever a change is meant to be invisible to users, and treat any difference in results as a defect

Corrective regression is different from retesting. Retesting runs the specific test that found a defect, to confirm the fix works. Regression testing, including corrective regression, checks that the change hasn’t broken anything else.

7. Smoke Regression Testing (Build Verification Testing)

Smoke regression testing, also called build verification testing, runs a small set of critical tests on a new build to decide quickly whether it’s stable enough for further testing.

• When to use it: after every deployment to a test environment, before deeper testing starts

• Scope: happy-path tests for the most important features, usually a small slice of the full suite

• Duration: minutes, since its job is a fast yes or no

• Example: after a new build reaches the QA environment, smoke tests check login, product search, adding to basket, starting checkout and submitting payment, and the build is rejected if any of them fail

• Strengths: stops teams wasting time testing a broken build

• Limitations: only confirms basic stability, not overall quality

• Best practice: keep the smoke suite short, include only critical workflows, run it automatically on every deployment and stop as soon as a test fails

8. Sanity Regression Testing

Sanity regression testing checks that a specific fix or small change works, along with the features right next to it, without running a broader suite.

• When to use it: after a hotfix or minor change that needs a quick check before release

• Scope: the changed functionality and closely related features only

• Duration: from a few minutes up to an hour, depending on the change

• Example: after fixing broken pagination on search results, sanity tests check different page sizes, the first and last pages and the links between pages, while filters, sorting and the basket are left out

• Strengths: a fast way to avoid releasing an obviously broken fix

• Limitations: narrow by design, so it can miss related defects

• Best practice: use it for hotfixes and small updates, write down what it covers so nobody over-trusts it, and follow it with selective or complete regression for larger releases

Smoke testing asks whether a whole build is stable enough to test. Sanity testing asks whether one specific change works.

9. Automated Continuous Regression Testing

‍Continuous regression testing runs the right regression tests automatically throughout development, triggered by commits, merges, schedules and deployments. It brings the other types together into one routine.

• When to use it: all the time, as the backbone of a CI/CD pipeline

• Scope: depends on the trigger, from smoke tests on every commit to complete regression overnight

• Duration: ongoing, with each suite running at its own frequency

• Example: every commit triggers unit and smoke tests, every merge to the main branch triggers selective regression, a full regression run happens overnight, and every deployment to staging triggers smoke tests again

• Strengths: defects are caught soon after they’re introduced, which makes frequent releases realistic

• Limitations: needs reliable automation and infrastructure, and flaky tests quickly turn into noise

• Best practice: tier the suites by speed and trigger, deal with flaky tests promptly, keep an eye on execution costs, and send results straight into developers’ workflows

‍

Start Transforming Your Regression Testing

Comprehensive regression testing doesn't require massive teams or months of effort. It requires the right approach and the right platform.

‍Assess your current state

‍Which types of regression testing do you use? What percentage is automated? How long does regression take? What's your maintenance burden?

Define your target state

What release frequency do you need? What confidence level is acceptable? What coverage is required for critical workflows?

Implement AI-native automation

‍Choose platforms that eliminate maintenance through self-healing, accelerate test creation through Natural Language Programming, and optimize execution through intelligent test selection.

Build iteratively

‍Start with smoke and selective regression for immediate value. Expand to complete regression over time. Continuously improve based on production defect analysis.

Running Every Type of Regression with Virtuoso QA

Different types of regression need different sets of tests, run at different moments. Virtuoso QA lets teams build one regression suite for browser-based applications, run the right slice of it at each stage of the pipeline, and keep a record of which requirement each test proves and who approved it.

• Journeys can be grouped and triggered separately from Jenkins, Azure DevOps or GitHub Actions, so the same suite supports smoke, selective, progressive and complete regression

• Runs execute in parallel across 2,000+ browser and OS combinations, which brings a complete regression run down to hours

• When a release changes the UI, Virtuoso QA works out where each element moved and queues the fix for a reviewer, so suites stay usable between runs without hidden edits

• Testers write journeys in plain English, each step runs live as it’s added, and StepIQ suggests the next check, which speeds up progressive regression for new features

• Touchstone drafts tests from new stories and specs, citing the source for each one, and holds them until a named person approves the requirement

• One journey can cover the UI, the APIs behind it and the database, and Business Process Orchestration runs processes that span several systems

• Composable components mean a fix to a shared step, such as login, flows into every journey that uses it

• When a run fails, the broken step sits beside the same step from the last good run, with a likely cause, so the team can tell a real regression from a test issue quickly

Related Reads

‍Visual Regression Testing - All You Need to Know‍

Functional Testing - Definition, Lifecycle, and Types‍

Automated Regression Testing - Process, Tools, and Frameworks

Regression Testing vs User Acceptance Testing (UAT) - 8 Key Differences

End-to-End vs Regression Testing - When and How to Use Each

Unit Testing vs Regression Testing: Understanding the Core Differences