How to Build a Regression Test Plan

Regression testing is notoriously repetitive and time-consuming. As your software grows in complexity, manually testing every existing features is just counter-productive. This is why regression testing is an ideal potential for automation.

One of Katalon's customers, Liberty Latin America, upon adopting regression test automation, has seen 50% decrease in testing time, which ultimately translates into rippling benefits across the entire QA process.

However, to truly reap the benefits of regression test automation, you need a solid regression test plan.

Here's a playbook for you.

Why build a regression test plan?

A regression test plan is the strategic blueprint that ensures the continuity, reliability, and quality of a software product as it evolves.

The goal of a regression test plan is threefold:

1. Prevent defect leakage in stable features
   Even small code changes can introduce subtle bugs in unrelated areas. A well-structured regression plan ensures these bugs are caught early before they make it to production and affect end users. This is especially crucial in high-traffic, business-critical applications where downtime means revenue loss.
2. Verify bug fixes
   When a defect is resolved, it's not enough to confirm that the specific issue is fixed. You need to ensure that the fix hasn’t inadvertently caused other parts of the system to fail. A regression plan guarantees that test cases related to the fix as well as any dependent workflows are executed consistently.
3. Maintain overall product stability
   Continuous delivery and Agile development introduce frequent changes. A regression plan is a quality gatekeeper. Thanks to this immediate feedback loop, dev teams can move fast more confidently.

How to build a regression test plan

Step 1: Define regression testing scope

It is crucial to identify the boundaries for regression testing so that time and resources are used wisely. You can't test everything, all the time.

Ask yourself:

1. What will be included in regression testing?

Start with the essentials. What are your core business workflows that receive high volume of request? These high-impact areas must never break. Good examples include login, checkout, and data processing, so automate regression tests for these areas is usually recommended.

Next, focus on previously failed test cases. they’ve caused trouble before and could again.

Don’t forget the dependencies of modified components. If component A is connected to component B and C, even a small tweak in component A area can break the other two. Similarly, keep an eye on high-risk areas, especially those that are technically complex or deeply integrated with other systems.

2. When will regression testing be executed?

Regression testing can be resource-intensive, so understanding when to trigger it ensures efficiency without compromising quality. Common triggers include:

• After each code commit or pull request (in CI pipelines)

• At the end of each sprint (Agile)

• Before major releases or hotfixes

• After performance optimizations or infrastructure changes

• Post-integration of third-party services or APIs

Step 2: Prioritize regression test cases

Once you know what to test, it is time to prioritize the more critical test cases.

Not all failures are created equal.

A bug in your login system can lock users out, while a minor styling issue in a rarely used form might go unnoticed. Risk-based analysis helps you test what’s most likely to break and what would hurt the most if it did.

You can assign risk levels to each module based on:

• Probability of change (how often the code changes)

• Complexity (more moving parts = more things to break)

• User visibility (how often users interact with it)

• Business impact (revenue, compliance, brand reputation)

This can be a cross-functional effort. Regression testing should not exist in a silo. Your team may be technically focused, but the product team understands the strategic importance of features. They know what’s tied to KPIs, revenue, or customer SLAs. Work with product owners or stakeholders to access user analytics and uncover which features are most frequently used.

Step 3: Define entry and exit criteria

Clearly document these criteria in your test plan so you know when to start and when to stop. You can set up automated gatekeeping policies in your CI/CD system (e.g., block merge if critical tests fail) to control this.

Entry criteria include:

• Successful completion of smoke or sanity testing

• Code freeze or completion of all related dev tasks

• A stable build deployed to a testing environment

• Availability of finalized regression test cases and test data

• Automated pipelines prepared and configured

Exit criteria include:

• 100% execution of prioritized regression tests

• Zero critical or high-severity bugs remaining open

• Acceptable pass rate defined by your quality thresholds (e.g., 95% or above)

• Test coverage goals met (e.g., 85% line coverage or critical path coverage)

Step 4: Prepare the test environment

Your test environment should replicate production environment as closely as possible, technically and behaviorally, to give you confidence in test results.

What to include:

• Frontend: browser versions, screen sizes, responsive views

• Backend services: app servers, APIs, background jobs

• Databases: schema versions, seeded test data

• Middleware: message queues (e.g., Kafka, RabbitMQ), caching layers (e.g., Redis, Memcached)

• 3rd-party services: payment gateways, email/SMS APIs, authentication services

• Infrastructure: load balancers, CDNs, proxies, firewalls

📌 Tip: Create an environment matrix that lists all components with their required versions. Update it alongside your release plan.

To avoid test interference, environments should be isolated and versioned. One team's tests shouldn’t pollute another's data or state. Make sure you use separate environments for development, QA, UAT, staging, and performance testing. For regression specifically, you can dedicate a QA or Pre-Production environment where automated and manual tests can safely run.

Step 5: Prepare the test data

Regression suites span hundreds (or thousands) of test cases. Maintaining one massive dataset creates fragility. Instead, prepare modular, composable data snippets that can be combined as needed.

During test case design, make sure to:

• Document all data dependencies in the test case repository.

• Tag test cases with data types or states required and prepare suitable dataset for them. Your test data should also be customized to align with environment requirements.

Determine which test data strategy best fits your application and team size:

• Static Test Data: Pre-defined and version-controlled data snapshots, ideal for small-to-medium suites. Good for fast execution but requires maintenance over time.

• Dynamic Test Data: Generated on-the-fly using scripts, APIs, or test data management tools. Offers greater flexibility and reduces conflicts in shared environments.

• Hybrid Approach: Use static data for common flows and dynamic data for complex, stateful, or interdependent scenarios.

For large test suites or enterprise-grade applications, consider implementing a Test Data Management (TDM) system.

Step 6: Schedule and execute Tests

Now that your plan, cases, and environment are ready, it’s time to schedule and execute your regression tests, ideally in parallel with your release cycle.

How to do it:

• Integrate test execution into your CI/CD pipeline using tools like Jenkins, GitHub Actions, CircleCI

• Schedule automated test runs during nightly builds, sprint-end, or pre-release freezes

• Use test orchestration tools (e.g., TestGrid, BrowserStack, LambdaTest) to run tests across devices, OSes, and browsers

• Monitor execution logs and alerting systems in real-time to catch failures early

Step 7: Measure regression testing effectiveness

Planning doesn't end at execution. You need to track performance metrics to validate the quality of your regression strategy and optimize it over time.

Key metrics include:

• Test pass/fail rate

• Test coverage (% of critical paths tested)

• Time to execute full suite

• Defect leakage rate (bugs missed during regression)

• Flaky test ratio (non-deterministic failures)

• Test ROI (manual effort saved by automation)

How to do it:

• Use dashboards and reporting tools (e.g., Allure Reports, TestRail analytics, custom Grafana dashboards)

• Regularly review regression outcomes with QA, Dev, and Product stakeholders

• Refactor or retire obsolete tests to maintain suite efficiency.

How to use Katalon to do regression testing

Katalon is a comprehensive end-to-end AI-augmented automation testing platform that can take your regression testing to the next level. It is the all-in-one regression testing tool for your website, web services, mobile application, and even API testing.

Thanks to Katalon's Record-and-Playback features, any team member can easily capture test objects and record actions that simulate real users’ activity. This sequence can be re-executed in regression testing sessions, saving tremendous time compared to manual testing.

Katalon also supports running scripts on a wide range of devices, browsers, and environments, allowing QA teams to perform a lot of testing activities in just 1 place instead of having to spend time configuring environments and constantly switching tools.

After executing tests, Katalon Platform enables teams to review test results with the comprehensive and customizable test reports in LOG, HTML, CSV, and PDF formats before forwarding them as email attachments. There is a debugging mode for testers to conduct a root cause analysis for a specific failed case.