Regression testing after every deploy

Regression testing checks that what worked yesterday still works after today’s change. For small teams shipping often, a short regression suite run after every deploy prevents most "it used to work" bugs from reaching users.

Who it’s for

Teams that deploy several times a week
Startups without a full automated test suite
Anyone who has seen a fixed bug come back

Updated 2026-10-19.

Regression testing after deploys is how you make sure that what worked yesterday still works today. Every new feature, refactor or dependency update can quietly break something else, and the people most likely to notice are your users. A small, well-chosen regression suite run after every deploy catches those problems first.

What regression testing is

A regression is a bug in something that used to work, introduced by a change elsewhere: a new feature, a refactor, a dependency update or a config change. Regression testing is re-running checks on existing functionality after each change to catch these early.

It matters most during and after a beta test, when fixes ship quickly and each fix can break something beta testers already checked.

What to include in a regression suite

Start small and grow it from real problems:

Must-work flows: signup, login, password reset, the main job of the product, upgrade or checkout in test mode.
Past bugs: every serious bug you fixed becomes a check, so it cannot quietly return.
Risky areas: code that changes often, integrations, payments, permissions.
Key pages on mobile: layouts break easily with CSS changes.
Console errors on the main pages.

Keep the suite short enough to run after every deploy. A regression suite nobody runs is worse than a small one that always runs.

Building a regression suite, step by step

List must-work flows and write one check per flow, with the user goal and expected outcome.
Go through your bug tracker and add a check for each fixed critical or major bug.
Mark risky areas (payments, permissions, integrations) and add a check for each.
Split into two tiers: a quick tier for every deploy and a full tier for release candidates and big upgrades.
Prepare test data: accounts for each role, seed data, test-mode payments.
Run it once manually to confirm every check is clear and passes on a good build.
Schedule or automate it after each deploy and nightly.
Add a check every time a bug escapes to users or beta testers.

When to run it

After every production deploy (a quick smoke test of must-work flows).
Before a release candidate or a big launch (the full suite).
After dependency, framework or infrastructure upgrades.
On a schedule, for example nightly, to catch problems from third-party services.

Example regression test case

Check: REG-07 Coupon applies at checkout (from bug #214, fixed last month)
Tier: quick (every deploy)
Environment: production, Chrome desktop and phone width, test mode
Account: [email protected]

Steps:
1. Log in and add any product to the cart
2. Open checkout
3. Enter coupon TEST10 and click "Apply"

Expected: discount line shows 10% off; total updates; "Pay" button is enabled
Stop before paying.

Example regression bug report

Title: Checkout: "Pay" button disabled after applying a coupon (regression of #214)

Environment: iPhone, iOS Safari, 390px width, production, deploy of today 09:30
Account: [email protected]

Steps to reproduce:
1. Log in, add "Starter kit" to the cart
2. Open checkout, enter coupon TEST10, tap "Apply"

Expected: 10% discount shown and "Pay" button enabled
Actual: discount shown, but "Pay" stays disabled; worked on yesterday's deploy
Severity: Critical (no customer using a coupon can pay)
Frequency: Always on mobile; desktop Chrome works
Suspected cause: today's deploy changed the checkout form layout
Evidence: annotated screenshot, recording of steps 1–2

Manual, scripted or AI

Automated end-to-end scripts are fast and precise but need maintenance whenever the UI changes.
Manual regression is flexible but slow and often skipped under pressure.
An AI beta tester sits in between: you describe the flows in plain language, and it runs them in a real browser like a person would, so small UI changes do not break the test.

Many teams combine unit and integration tests in CI with a plain-language regression run after each deploy.

Writing regression checks in plain language

Describe the user goal and the expected outcome, not the clicks:

"Sign up with a new email and reach the empty dashboard with the welcome checklist."
"Create a project, rename it, and see the new name in the sidebar."
"Apply coupon TEST10 at checkout in test mode and see 10% off before paying."

Prioritizing regressions

Fix or roll back now: regressions in signup, login, payment or the main flow, and anything that loses data.
Fix this release: regressions in secondary features with no easy workaround.
Schedule: minor and cosmetic regressions, but still before they pile up.

A regression is usually more urgent than a new bug of the same size, because it breaks something users already rely on.

Handling results

Treat a regression in a must-work flow as at least major severity.
Link the regression to the deploy that caused it.
After the fix, re-run the same check before closing the bug.
Add a new check whenever a bug escapes to users.

Common mistakes

A suite that only grows. Remove checks for features you removed, and keep the quick tier quick.
Running it only before big launches, so regressions from small deploys reach users.
Not linking checks to past bugs, so nobody knows why a check exists.
Flaky checks that everyone ignores. Fix or delete them.
Testing only on desktop when many regressions are mobile layout breaks.

Regression testing with an AI beta tester

betatest.ing's AI beta tester can run your plain-language regression checks after each deploy or on a schedule, in a real browser or on Android and iOS devices. It files bugs with steps to reproduce, severity, annotated screenshots and recordings, and can re-test previously reported bugs to confirm they are fixed. It uses your saved test login without ever seeing the password, and stops before any real payment.

It covers the functional side. It does not replace unit tests in CI, load or security testing, or real users' opinions, and it cannot reproduce real-world conditions like poor networks, push notifications or real payments.

The test task

What you send your AI beta tester. It works out the steps and tests like a real user.

Run a regression test on [YOUR APP URL] after today’s deploy. Use the saved test login, payment test mode only, and ask me before anything irreversible. Check that these still work, each from a clean start: [YOUR MUST-WORK FLOWS, ONE PER LINE]. Also check signup and login, the main pages for console errors and failed requests, and the main flow at phone width. Then re-test every bug from the previous report that is marked fixed and say whether it is really fixed. File each new problem as a finding marked "regression" if it worked before, with severity, steps to reproduce, expected vs actual and an annotated screenshot, and record any failing flow. End with a pass/fail table per flow and a list of new regressions.

Fill in your details

Here, or later in the chat with your tester.

[YOUR APP URL] Production or staging, after the deploy.
[YOUR MUST-WORK FLOWS, ONE PER LINE] e.g. "create an invoice", "invite a teammate", "export CSV".

What the AI beta tester checks

Each must-work flow from a clean start
Signup and login
Console errors and failed requests on the main pages
The main flow at phone width
Bugs previously marked fixed
Upgrade or checkout in test mode

What you get

A pass/fail table per flow
New regressions with severity, steps and annotated screenshots
Recordings of failing flows
Confirmation of which fixed bugs are really fixed

How to run it with betatest.ing

Add your app as a project with a test login.
List your must-work flows in the task.
Run it after a deploy, or schedule it to run daily.
Add every escaped bug as a new flow to check.

Tips

Keep the suite short enough to run after every deploy.
Turn every serious fixed bug into a regression check.
Describe user goals, not clicks, so UI changes do not break the test.
Schedule a nightly run to catch third-party breakages.

Regression testing after deploys: FAQ

What is regression testing?

Re-testing existing functionality after a change to make sure nothing that used to work is now broken.

How often should regression tests run?

A short suite after every deploy, the full suite before big releases, and ideally a scheduled run such as nightly.

What is the difference between regression and smoke testing?

A smoke test is a quick, shallow check that the build works at all. Regression testing is deeper and checks specific flows and past bugs.

Can regression testing be automated without writing scripts?

Yes. An AI beta tester can run plain-language regression checks in a real browser on a schedule and report failures with evidence.

What should I do when a regression is found?

Rate it at least major if it breaks a must-work flow, link it to the deploy that caused it, fix it, and re-run the same check before closing.

How big should a regression test suite be?

Small enough that it actually runs after every deploy. Start with your must-work flows and the serious bugs you have fixed, then add checks only when a bug escapes. Keep a larger full suite for release candidates and big upgrades.

Do I need a test account for regression testing?

Yes. Use dedicated test accounts for each role with known data, so results are comparable between runs and real customer data is never touched. Keep payments in test mode.

What is the difference between retesting and regression testing?

Retesting checks that a specific bug is fixed by repeating its steps. Regression testing checks that the fix, or any other change, did not break things that already worked. You usually do both after a fix.

Related beta testing guides

Smoke testing — A smoke test is a short, shallow test that answers one question: is this build working well enough to use and test further? It takes minutes, and it is the first thing to run after any deploy.
Beta testing vs QA testing — QA testing checks that the product works as specified; beta testing checks how it holds up with real people in real conditions. You need both, at different moments. Here is how they differ and how to combine them.
Bug severity levels — A shared severity scale lets beta testers, developers and product managers agree on how bad a bug is in seconds. Here is a simple four-level scale with examples, plus how severity differs from priority.
Test case template — A test case is a short, repeatable check: start here, do this, expect that. A set of good test cases lets anyone, human or AI, run the same beta test on every build and get comparable results. Copy the template, see a filled-in example, and run your test cases with an AI beta tester.
Beta testing checklist — Use this beta testing checklist to prepare, run and close a beta test without forgetting the basics. It works for websites, web apps and mobile apps, and you can hand the product part of it to an AI beta tester.
Free tool: Test case generator — A table of test cases with steps, data and expected results, edge cases included. CSV and Markdown export.