What is Automated Regression Testing and How to Do It

Automated regression testing is the practice of automatically re-running your existing test cases after every change to confirm that previously working functionality still works. Regression means going backward, a software system regressing to a worse state than before, and it happens constantly in normal development: a new feature ships and looks great, but quietly breaks something that used to work. This guide covers what regression testing is, why it matters on every release, how to automate it step by step, and how modern tooling detects regressions automatically across CI runs rather than leaving you to spot them by hand.

Regression does not happen because developers are bad at their jobs. It happens because software systems are complex, and the interactions between all the components inside them are complicated, sometimes impossible, to keep track of without deep analysis. A change in one place can break something seemingly unrelated somewhere else, and no one notices until a test catches it, or a customer does.

Testing for regression is critical on every release. It is far better to know about an issue before release than afterward, under the pressure of a production incident that you or a customer just discovered. Having a set of test cases recorded and ready to run each release is essential, and because manually testing all functionality can take days, this is exactly where automated regression testing earns its place. Even so, not every test can or should be automated without real investment, so it helps to prioritise test cases and set severity levels. Signing in to the application is a top priority; a light and dark mode toggle not working is not.

What is the difference between retesting and regression testing?

These two are often confused, so it is worth being precise.

Retesting is confirming a specific thing after a change to that thing. Examples: a new feature had a bug, a fix was made, and you test that feature again. Or you load test a system, view the results, and step up the number of requests to test again. Or you repeatedly change a referenced image and check it looks right in the interface each time. Retesting is narrow and targeted at what you just changed.

Regression testing is confirming that existing, previously released functionality still works correctly after an unrelated change. It is broad, a general health check across the system, hunting for side effects you did not intend and would not think to look for.

Regression testing is easy to overlook, because the thinking goes: that is old, it already works, it has been tested before. But a change elsewhere can be linked to old functionality in ways that are hard to see without testing. One practical point on timing: regression testing for a release has to wait until the new feature being introduced has finished its own testing. Running regression any earlier tests the system in the wrong state. And if a bug fix is made during regression testing, a new build is produced and regression testing may need to repeat.

What is automated regression testing?

Automated regression testing is simply taking regression test cases that have been written down and are currently run manually, and automating them. Some or all of the test steps can be automated. Automation also includes setting up the infrastructure to run those regression tests automatically at key points in the software development life cycle, for example on every push to a repository, with continuous integration and quality gating configured so a regression blocks the pipeline.

Why does automated regression testing matter?

Because the same tests have to run again and again. If an issue is found during regression testing and a fix is required, another round of regression testing follows once the new build is ready. If that surfaces a follow up issue, another round follows again. Regression tests run at least once on every release, which means the same tests run frequently, and frequent repetition is exactly what automation is good at. The time saved compounds into a high return on investment.

What are the benefits of automated regression testing?

1. Improved test coverage and accuracy. Automation makes it possible to get high coverage quickly, with consistent repeated runs.
2. Higher ROI. Because regression tests run so often, manual testing is expensive, and automation delivers a strong return on the time invested.
3. Earlier detection of bugs. With automation handling the time consuming regression pass, people are freed for new features and exploratory testing, and regressions are caught faster.
4. Cost effectiveness and resource optimisation. Automated regression makes shipping robust releases more efficient and lets engineers and testers focus on more original work.
5. Consistency. Automated regression testing runs the same way every time, so quality is held to the same standard on each release rather than varying with human attention.
6. Round the clock running. Comprehensive regression can take a long time even when automated, but tests can run at any hour, so the total volume of testing far exceeds what manual effort achieves.
7. Reduced turnaround time. By running tests in parallel, you can cut total test time sharply without adding much cost.

What are automated regression testing tools?

Automated regression tools are any software that helps you write and run automated regression tests. Writing the tests usually means using a general test framework. Popular choices by language include:

• JUnit, TestNG for Java
• pytest, Robot Framework for Python
• Mocha, Jasmine, Jest, Vitest for JavaScript
• NUnit, xUnit for C#
• RSpec for Ruby
• XCTest for Swift
• go test for Go

For web front end testing, more specialised tools exist, including Playwright, Cypress, WebdriverIO, CodeceptJS, Nightwatch, and TestCafe. You could also build a custom framework on top of Selenium, but the tools above are powerful out of the box.

For native mobile apps, XCUITest covers iOS and Espresso covers Android. You could build on top of Appium, but there are an increasing number of capable mobile automation tools. For API endpoints, consider Postman or Rest Assured, or write a custom framework, for example using Axios in Node.js to make requests and validate responses.

How do you do automated regression testing?

Five steps, in order.

1. Write test cases down and run them manually first. With any testing, manual or automated, the most important step is writing out the test cases. A common approach groups test cases into suites for each area of the system. Each case can be as simple as the steps to take and the expected outcome. A popular format is the BDD (Behaviour Driven Development) three part structure:

• GIVEN: context, for example, as a new user I navigate to the Sign Up view.
• WHEN: the action, for example, I enter my email and a password.
• THEN: the expected outcome, for example, a new account is successfully created.

Writing well formed, comprehensive test cases is the foundation. You can use the Tesults manual testing features, Lists and Runs, to document them. After documenting, run them manually a few times to work out which run most frequently, which take longest, and which make the most sense to automate for the best return on your time.

2. Automate the test cases that offer the best return on investment. With a comprehensive list in hand and a few manual runs behind you, start automating, beginning with the cases that take the most time to run. Automating regression cases is the same as automating any test case, using a framework suited to your language and layer (unit, integration, system, or end to end). Tesults integrates with a wide range of frameworks including JUnit, TestNG, pytest, Robot Framework, NUnit, xUnit, MSTest, RSpec, Mocha, Jasmine, Jest, Vitest, Playwright, Cypress, WebdriverIO, CodeceptJS, Nightwatch, Postman, TestCafe, XCTest, and Espresso, and supports languages including Java, C#, Python, Ruby, JavaScript and Node.js, Kotlin, Swift, Go, Rust, C++, and PHP for when you need a custom framework.

3. Set up automated runs. Running tests locally before committing is good practice. When a pull request is opened for review, tests should run automatically through your continuous integration system, GitLab CI, GitHub Actions, CircleCI, Jenkins, TeamCity, and others.

4. Set up effective test reporting. How you report results matters for visibility, regression analysis, diagnosis, and gaining assurance you are meeting quality targets. It can be the difference between a high and a low return on your testing investment. It might seem that regression is trivial to spot: if every test is expected to pass, any failure is a regression. That often works, but usually you need more, a stored history of results so you can analyse regression over time and catch patterns and subtle issues rather than treating each run in isolation. The next section covers how that detection actually works.

5. Follow up, address failures, and find gaps. With steps one to four done, analyse the failures in your reporting, identify the genuine regressions, and action them. Once a fixed build is ready, repeat the cycle, view the results again, and keep assigning failures for fixes. It is also worth looking for gaps in coverage based on the suites and cases in the report.

How do you detect regressions automatically across CI runs?

This is the part that turns regression testing from a manual comparison into something the tooling does for you. A single test run tells you what failed in that run. What it does not tell you is whether a failure is new, a genuine regression introduced by the latest change, or something that was already failing, or a flaky test that fails intermittently regardless of your change. Answering that by hand means digging out the previous run and diffing it, every time.

Tesults computes this from your retained result history. Comparing consecutive runs produces a delta of which tests newly failed, which were resolved, and which are continuing to fail, so a genuine regression is separated from long standing failures and from noise automatically. Flaky tests, the ones that alternate between pass and fail across runs, are identified and separated out, so you do not mistake intermittent failures for a real regression. And because this analysis is available through the Tesults Insights API as well as the dashboard, you can surface regressions directly in a CI pipeline, calling an endpoint to get the tests that newly failed against a baseline, and gate a deploy on a real regression rather than on a test that has been red for a week. The same information is available to AI agents through the Tesults MCP server, so you can ask, in plain language, what regressed between the last two runs.

The point is that identifying regression across runs is a job the reporting layer should do, not one you should do by eye. Keeping a retained history is the prerequisite (see how to keep a history of test results instead of losing them after each CI run), and once you have it, regression detection, flaky separation, and run to run deltas follow from it.

Which test scenarios are a good fit for automated regression testing?

• Frequently repeated, reusable cases, such as the highest severity cases run on every release.
• Critical flows that can render the whole system unusable, such as authentication and authorisation.
• Cases where the balance between the cost to automate and the time saved makes it a clear win, which depends heavily on how often the case is re-run.
• Scenarios that need no human verification, for example pure API integration tests, where usability and UX judgement are not required.

Automated regression testing best practices

• Before automating, record your manual test cases with clear context, actions, and expected outcomes, ensure functional coverage is broad, and categorise by severity.
• Automate the tests that would cause the highest severity issue if they failed (such as being unable to sign in) and that need little to no human verification.
• Once automated, run them locally and in CI on every release.
• Automate regression analysis with a reporting system that stores result history, identifies regression over time, presents error detail, notifies the right people, consolidates results across your systems, and makes manual cases easy to record and view.

Do all of the above and you will be releasing high quality software every release, with real assurance about the robustness of what you are putting in front of customers.

FAQs

1. Is regression testing manual or automated? It can be either. Early on it makes sense to record and run cases manually. Once you want to release frequently, automate the highest severity cases to save time and reach a faster cadence.
2. Can automated regression testing completely replace manual testing? Cases that need human usability or UX judgement are hard to replace fully, but the large majority of cases can usually be automated.
3. How do I choose the right automated regression testing tool? It depends on the language your software is written in and whether you are testing back end or front end (web, mobile, or other devices). In some cases a custom framework is the right answer.
4. What areas of regression testing can be automated? Cases that need little to no human verification and run at high frequency, on every release, where the time saved makes automation a large return on investment.
5. How do I tell a real regression from a flaky test? Look at the failure across runs rather than in isolation. A regression newly appears and persists after a change; a flaky test alternates between pass and fail regardless. A reporting system that analyses run history can separate the two for you automatically.

To detect regressions and analyse failures across your CI runs, see the Tesults Insights API documentation, and to get started with reporting for your framework, the getting started guide.