The first regression suite should help a team make a release decision. Start by choosing a few user journeys whose failure would matter, then define what evidence would tell you that each journey still works. A suite built around those decisions is easier to maintain than a collection of checks added simply because a screen was easy to automate.
For a small team, three well-chosen journeys can be a useful first exercise. Each choice needs a reason. A check should have a user, a starting condition, an action and an observable result. Someone on the team should be able to explain what a failure means and who would investigate it before the next release.
List journeys before screens
Begin with what people are trying to do. “Checkout page” names a screen. “A returning customer completes a purchase using a saved address” describes a journey with a result the team can observe. That distinction helps the team avoid treating every field and button as equally important.
Ask the product lead, support contact and a developer to make a short list together. Include the main task the product exists to support, a way for a new user to begin and a recovery route when something goes wrong. Write each as a sentence a teammate outside engineering can understand. If the sentence needs several exceptions to explain it, divide the journey into smaller decisions before writing code.
Use recent release history to make the list concrete. Which journeys changed? Which failures created extra support work? Which paths contain a dependency the team has struggled to understand? The answers can guide the initial selection without requiring a complex scoring system or a claim that every possible risk has been measured.
Score three practical dimensions
A simple workshop can use impact, likelihood and observability. Treat these as discussion prompts rather than a scientific prediction. Impact asks what happens when the journey fails. Likelihood asks why the team considers that path vulnerable in the current product. Observability asks whether a test can produce a clear signal that a person can interpret.
Assign a rough low, medium or high rating to each dimension and write one sentence explaining the rating. The sentence is the useful part. “High impact because a customer cannot complete the order” is actionable. “High priority” alone gives the next person little basis for changing the decision when the product evolves.
Observability deserves special attention. A journey may be important but difficult to test with the current environment. That can lead to a manual check, a smaller automated boundary or work to improve the test setup. The selection process should make that choice visible instead of quietly leaving the journey out because another path is easier to automate.
Work through a checkout example
Imagine a team preparing an update to an online ordering flow. Its candidate list contains browsing a category, adding an item to a basket, checking out, viewing an old order and changing an account password. The release changes the basket and address selection. The first regression exercise could focus on the purchase path, account access and one meaningful failure state.
For the purchase journey, define a known item and a test account. The starting condition includes the account state and the available item. The action is completing the checkout steps in the intended test environment. The expected observation is a confirmation tied to the created order, with the item and address the test selected. The team should decide how it will inspect that result before automating the clicks.
For account access, the starting condition could be a valid test account with a known password. The journey ends when the account can reach the area required to place an order. For the failure-state check, choose a case the release affects, such as an unavailable item in the basket. The observation should show that the user receives the intended explanation and can take the next action the product supports.
These examples give three different signals. The first checks the main result. The second checks access to that result. The third checks a recovery decision. A team can review that coverage in a few sentences, then compare it with the actual change being released.
Write the result before the script
For each selected journey, write the expected observation in language the team can review. “The button was clicked” describes an action. “The new order appears with the selected item” describes a result. That result gives the test a purpose and helps a reviewer judge whether it is checking enough.
The Playwright best-practices guide recommends testing behavior visible to users and keeping tests isolated. Applied to this exercise, the team can focus its check on the outcome a user needs while making the starting state explicit. The exact implementation will depend on the product and test environment.
Record the environment next to the result. A check against a local build answers a different release question from a check against the deployed application. A result can be useful at either stage, provided the release owner knows what it covered. The test name and the run link should make that scope easy to find.
Make the starting data repeatable
A small suite quickly becomes frustrating when its data changes unexpectedly. Name the account, item or record each journey needs and decide how that state will be created. The person investigating a failure should not have to guess whether an old test run used up the item or changed the account.
Keep each journey understandable on its own. If a checkout test requires a previous test to create the basket, a failure in the earlier step can produce confusing results later. A clear setup makes it easier to distinguish a product defect from a problem preparing the test. It also helps someone run a single check while investigating a change.
Document the teardown decision too. Some test records can be removed after a run; others may be retained for inspection in a dedicated environment. Choose deliberately and keep the environment suitable for the next run. The goal is a predictable starting point and evidence that remains available long enough to investigate.
Decide what happens when a check fails
Before the suite becomes part of a release process, assign ownership. A failed result should reach someone who can inspect the evidence and decide whether to hold the release, fix the issue or record an accepted exception. The release record should keep that decision beside the result.
Suppose the purchase check fails because the test item was removed from the environment. Repairing the setup may be appropriate, but the original failure still needs an explanation. Suppose it fails because the selected address is lost. That is a product observation requiring a different decision. A single red icon should lead to enough context to tell those cases apart.
Set a small maintenance habit after the first few releases. Review checks that failed, checks that were hard to interpret and important manual observations the suite missed. Add a new automated check when it answers a recurring release question. Remove or revise one when it no longer represents the product’s actual behavior.
Leave the workshop with three decisions
Choose three critical journeys. Give each one a repeatable starting condition, an observable result and an owner for investigation. Run them against one real release and discuss the evidence before expanding the suite. That creates a useful baseline for a release process or a product being built around it.
The next version of the suite should follow what the team learns from that release. Clear signals and explainable decisions give a small collection of checks a durable purpose. They also give the team a practical way to describe the next piece of testing work it needs.