Why this matters
The first automated tests shape the future suite. If a team starts with fragile edge cases or visually complex screens, maintenance grows before trust is established. A better starting point is a small set of stable, business-critical journeys.
Playwright is powerful, but tool choice does not replace prioritisation. The aim is a fast and reliable signal that catches costly regressions early.
How to do it well
Map critical user journeys
Identify login, creation, payment, export or other flows whose failure would stop users or the business.
Estimate repetition and risk
Prioritise tests that are executed often and cover high-impact changes.
Stabilise test data
Create predictable accounts and data, with setup and cleanup independent of previous runs.
Use resilient locators
Prefer role, label and test identifiers over CSS structure or visible text that changes frequently.
Keep tests focused
Test one business outcome per scenario and move setup to APIs or fixtures where practical.
Measure reliability
Track duration, failure causes and flakiness; quarantine and repair unstable tests quickly.
What to avoid
- Automating every manual test without considering value.
- Building long end-to-end chains where one early failure hides all later checks.
- Depending on shared mutable data or execution order.
- Using arbitrary sleeps instead of waiting for observable conditions.
- Counting test quantity as success while the suite is slow or ignored.
Practical example
A marketing platform may start with login, audience creation, campaign scheduling and final status verification. Less stable visual customisation can remain exploratory until the core suite is trusted.
When a regression test fails, the report should include a trace, screenshot and relevant network evidence so the failure can be diagnosed without rerunning it locally.
Lesson for practice
A small dependable Playwright suite is more valuable than a large noisy one. Automate the highest-value stable journeys, design for diagnosis and expand only when the signal remains trustworthy.
A good practice does not have to be complicated. It should be intentional, repeatable and explainable: the team should understand why the control exists, what evidence it provides and how feedback will improve the next iteration.
A real case: TSB and the difference between migrated data and a working service
In December 2022 the UK FCA described TSB’s service problems following its April 2018 platform migration. It stated that the data migrated successfully but technical failures disrupted banking services. This neither proves nor disproves the value of Playwright, and the notice does not identify the bank’s testing framework. It illustrates the difference between completing a technical step and validating an end-to-end customer outcome.
That is why I would not prioritise automation by listing screens. I would first describe what the customer must achieve and where the result appears elsewhere in the system. Sign-in can work while payment confirmation, application status or exports fail. A test that opens each page may miss those relationships. Equally, no browser test alone can establish the overall readiness of a complex migration.
A hypothetical first-month suite
For a fictional loan application service I would start with three journeys: authorised sign-in, saving an application in progress and submitting a completed application. Each needs an important negative variant. An invalid user must not gain access; an incomplete application must not appear submitted; and a double click must not create two business cases. This illustrates a risk-based selection rather than a ready-made suite suitable for every bank.
I would verify the outcome that matters to the user: the application has an identifier and the right status, survives a page refresh and is available to the next authorised role. Some assertions may be faster and clearer at API or database-test level. I would use browser automation where the purpose is to confirm the connection between the form, permissions and the final message or visible result.
How I would use Playwright
Playwright’s official guidance recommends testing user-visible behaviour and isolating tests. In practice I would give each test its own data and a reliably defined starting state. The outcome of one test should not decide the outcome of another. For elements I would favour a meaningful role and accessible name over a long selector that depends on the page’s exact internal structure.
For asynchronous processing I would not insert a fixed pause merely because it usually works. I would wait for the expected condition in a way that reflects the application’s processing. The wait needs a limit and failure needs useful evidence. If the team cannot distinguish a product fault from an environment or test problem, confidence in the suite will decline even when the pipeline is frequently green.
A hypothetical execution budget and maintenance approach
Imagine six scenarios averaging twenty seconds each. A sequential run would take roughly two minutes, but that is an illustration, not a performance promise for a particular project. I would assess further additions against the risk covered and the maintenance burden. Twenty visual variations may be better checked elsewhere than one interrupted operation whose safe resumption is central to the business outcome.
Each scenario should identify the risk it protects and the person who understands the expected result. When a requirement owner changes a business rule, the affected test should be discoverable. Tests must not freeze accidental historical behaviour. Before adjusting an assertion I would compare the proposed change with the current requirement rather than simply rewrite the expected value to make the suite pass again.
Deciding when to expand
After repeated runs I would review stability, diagnostic value and genuine defects detected. Incidents, new functionality and gaps in critical journeys would guide additions; redundant coverage would be removed. If the suite constantly needs manual intervention, the next investment may be better test data and a clearer application contract, not more scenarios competing for the same unreliable environment.
I would also retain a short maintenance decision log. When a test is removed, the reason should explain whether the protected risk disappeared, moved to a faster test level or remains uncovered. Otherwise a faster pipeline can be mistaken for improved quality while an important check has quietly vanished. The suite’s size is an implementation detail; its agreed coverage is the decision-relevant information.
The handover should state what the automation does not establish, including long-running load behaviour, availability of every dependency and the quality of a specialist credit decision. Honest boundaries are more informative than saying the product is “tested with Playwright”. A tool name is not evidence of a particular verification, and broad confidence should not be inferred from a narrow set of successful browser interactions.
Summary and recommendations
Begin with a small suite protecting essential outcomes, add meaningful negative variants and keep tests independent. Provide reproducible data and useful failure evidence. The TSB case supports looking at the complete service, not claiming that one framework would prevent every incident. My approach would connect risk priority with straightforward, explainable verification, expanding only when the initial checks are stable enough that their results deserve the team’s attention.