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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →A regression test checks that software behavior that worked before still works after a change. It helps a team find unintended effects of a feature, bug fix, configuration or data update, or relevant environment change. Regression testing is not a separate test level: it can be done manually or automatically at different levels, with scope chosen to match the change and the risks of failure.
What does regression testing mean?
When software changes, a team needs to check more than whether the new or corrected behavior works. A change can also disrupt functionality that was already working. Regression testing checks previously tested behavior after a modification to find defects introduced or exposed by that modification, including problems in areas that were not intended to change.
The ISTQB glossary definition, as presented by the ISTQB glossary page, focuses on testing a previously tested program after modification to ensure that defects have not been introduced or uncovered in unchanged areas. Microsoft Learn expresses the practical goal in its Dynamics 365 implementation guidance: verify after a change or update that the solution still works as expected and the change did not cause problems.
“Regression” does not mean that the software must have become worse. It describes the possibility that a change has made a previously working behavior fail. A regression test is the check intended to detect that outcome.
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When should regression testing be done?
Run regression tests when a change could affect behavior beyond the specific feature being edited. That may mean testing after changes to application code, configuration, data, or the environment on which the application depends. It is especially useful before changes are introduced into production, when a failure could affect users or important business processes.
- After a feature change: Check the new behavior and related existing workflows that might share code, data, or integrations.
- After a bug fix: Confirm the reported defect is fixed, then check other previously working behavior the fix could have affected.
- After configuration or data updates: Verify processes that rely on the changed settings or data, not just the screen or task that prompted the update.
- After an environment change: Check application behavior that could be affected by the changed environment.
- Before release: Run a scope of regression checks appropriate to the potential impact of the release.
The amount of testing need not be identical after every change. A small, isolated change may call for a focused set of checks; a change with broad dependencies or high business impact may justify a wider run.
Regression testing vs. confirmation testing (retesting)
These checks answer different questions, and both can be appropriate after a bug fix.
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| Check | Question it answers | What to run |
|---|---|---|
| Confirmation testing, often called retesting a fix | Was the specific reported defect corrected? | Rerun the relevant test that failed because of the defect, using conditions that can demonstrate the fix. |
| Regression testing | Did the modification cause a problem in other behavior that used to work? | Run tests for other previously working functionality that could have been affected by the change. |
Passing confirmation testing does not establish that surrounding functionality still works. Conversely, a regression test that passes does not by itself prove the original defect was fixed. The distinction is also reflected in ASTQB’s presentation of ISTQB Foundation Level material: subsequent regression testing can check whether fixes cause failures elsewhere in the test object.
How to choose the scope of a regression test
There is a trade-off between breadth and effort. Microsoft Learn describes broad testing, business-impact prioritization, and change-focused testing as possible approaches, which teams can also combine. Broad coverage checks more processes but costs more to run and maintain. A focused suite reduces effort, but any behavior left outside its scope is less checked.
| Approach | Coverage | Effort and exposure |
|---|---|---|
| Broad suite | Tests almost all relevant processes. | Higher execution and maintenance effort; less reliance on choosing only a narrow set of affected areas. |
| Risk-prioritized | Emphasizes processes whose failure would have the greatest business impact. | Concentrates effort on critical workflows; lower-priority or overlooked processes may still regress. |
| Change-focused | Targets areas directly affected by the modification. | Can reduce effort, but may miss indirect effects or failures outside the selected scope. |
A practical way to apply these choices is to start with critical user journeys and business processes, add functionality directly touched by the change and nearby integrations, then widen the test set according to risk and failures found. This is a useful selection approach, not a universal mandated sequence. Microsoft Azure testing guidance also supports examining coverage and using CI/CD integration for tests, with scheduled full-suite runs when some tests take too long to run on every commit.
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Trace likely effects before selecting tests
Identify what the change uses, updates, or depends on: related processes, shared components, data, configuration, and integrations. Then choose existing tests that exercise those connections. If the team does not know what could be affected, a very narrow scope is harder to justify; broader coverage may be prudent until dependencies are better understood.
Protect critical workflows
Prioritize flows where failure would seriously affect users or the business. A critical workflow may cross several features, so test its end-to-end outcome rather than assuming that checks of the changed component alone are enough.
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Expand based on risk and what you learn
Failures in the selected checks can reveal additional affected areas to test. Likewise, a change with wider dependencies may call for broader coverage than a local change. A risk-prioritized suite saves time only by accepting that untested areas receive less assurance.
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Is regression testing manual or automated?
It can be either. Manual checks can be appropriate when a workflow is unusual, exploratory, or not yet practical to automate. Automated tests are useful for important workflows that need to be repeated regularly: they can make recurring runs faster and more repeatable, but they still require maintenance as software and expected behavior change.
Microsoft Learn recommends building automation progressively around key business processes. Microsoft Azure guidance recommends integrating tests into CI/CD and scheduling full-suite runs for tests that are too long to run on every commit. A team can therefore use a layered routine: run a suitably quick, focused set frequently and schedule broader coverage less often where execution time makes that more practical. The exact cadence depends on the product, risks, and suite duration; there is no single interval that suits every team.
Example: changing an order or checkout flow
Microsoft Learn gives a Dynamics 365 example in which an update adds order-processing functionality. The relevant check is not only whether the added function works: automated tests for key connected business processes should also confirm their expected outcomes.
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For an illustrative checkout-page change, a team might first test the changed checkout behavior, then select existing checks for related payment, discount, shipping, and order-confirmation flows. The exact set depends on how that application is built and which parts the modification could affect. If the change also alters shared data or configuration, tests that depend on those changes may belong in scope too.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Using page screenshots as one regression artifact
Some regression checks concern whether a rendered page still looks as expected, in addition to whether its underlying interactions work. A screenshot can help a reviewer compare page output across changes, but an image alone does not prove that a workflow, payment, or integration behaves correctly. Treat it as one artifact within a testing approach, not as a substitute for functional checks.
For a capture step in a page-checking workflow, ScreenshotNeo is a website screenshot API and MCP server. Its stated options include full-page captures with lazy images loaded, element capture by CSS selector, device and viewport settings, dark mode, custom CSS and JavaScript, waiting for a selector or network idle, and PNG, JPEG, WebP, or PDF output. Its cookie/consent-banner handling, newsletter-popup and chat-widget removal can be turned off per step. Those settings can affect what appears in a capture, so keep the capture conditions consistent when comparing output.
Example API calls
Replace the example URL with the page you want to capture and use your API key. See the ScreenshotNeo documentation for API details and available parameters.
cURL:
curl -G "https://api.screenshotneo.com/v1/shot" -d access_key=YOUR_API_KEY --data-urlencode url=https://stripe.com -o shot.webpPython:
import requests
r = requests.get("https://api.screenshotneo.com/v1/shot", params={"access_key": "YOUR_API_KEY", "url": "https://stripe.com"}, timeout=90)
open("shot.webp", "wb").write(r.content)Node.js:
const q = new URLSearchParams({ access_key: 'YOUR_API_KEY', url: 'https://stripe.com' });
const res = await fetch(`https://api.screenshotneo.com/v1/shot?${q}`);ScreenshotNeo says bot checks or CAPTCHAs, blank pages, timeouts, failed loads, and cache hits are not billed; response headers identify the page verdict and billing status. Its MCP server provides take_screenshot, get_page_info, and capture_pdf tools for AI agents and MCP clients. The service is not a regression-test framework: your checks and comparison logic still need to determine whether a capture meets your expected result.
Or skip the browser setup
A single GET request can capture a URL as an image or PDF. Cookie banners, popups, and chat widgets are removed before the shot; bot checks, blank pages, and failed loads are never billed. An MCP server lets AI agents take screenshots. The Free plan includes 1,000 screenshots a month with no card; paid plans start at $5 for 3,000. Sign up for ScreenshotNeo free.
Common mistakes and how to avoid them
- Only checking the change itself: That can confirm new behavior but miss side effects. Add checks for related, previously working workflows.
- Treating a fix retest as the whole regression run: Retesting answers whether the defect was fixed; select separate checks for other behavior at risk.
- Assuming a focused suite covers everything: Change-focused and impact-based suites leave untested areas less checked. Make that trade-off explicit when deciding scope.
- Trying to run every test on every change regardless of duration: Broad testing has execution and maintenance costs. Consider a focused frequent run plus scheduled full-suite execution for tests that are too long to run on each commit.
- Automating checks without maintaining them: Automation is repeatable only while tests and their expected outcomes continue to reflect the product. Review and update affected tests as the software changes.
- Using a screenshot as proof of functional correctness: A page image can show rendered output, but it does not establish that interactions, data changes, or connected business processes succeeded.
Performance, reliability, and cost considerations
Regression testing consumes engineering time in test execution, investigation, and maintenance. A broad suite offers wider coverage at greater cost; a narrower suite is cheaper to run but leaves more behavior outside the selected checks. The useful balance depends on failure impact, change reach, suite duration, and how much risk the team can accept in untested areas.
Automation can reduce the time and variability involved in repeated checks, but it does not eliminate upkeep or guarantee that the chosen tests cover every regression. Integrating suitable checks into CI/CD can surface problems during delivery, while scheduling longer suites can preserve broader coverage without requiring every long-running test on every commit. No universal cost, defect-reduction, or coverage percentage follows from these practices; evaluate the suite against the application and its risks.
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