What Is the Fastest Way to Automate Tests?

Liviu Lupei

Founder & Solutions Architect, Endtest · May 19, 2026

Software teams are moving faster than ever.

AI coding tools can help developers generate code, refactor features, write boilerplate, and ship changes at a pace that would have sounded unrealistic a few years ago. In a controlled GitHub Copilot productivity study, developers using Copilot completed a coding task 55.8% faster than developers who did not use it. GitHub also published a research summary on Copilot's impact showing that AI assistance can help developers complete tasks faster and conserve mental energy.

That sounds great.

But it creates a new testing problem.

If development speeds up and testing stays slow, the team ships risk faster, not better software.

More AI-assisted code means more changes. More changes mean more chances to introduce regressions. More frequent releases mean less time for slow manual testing cycles. And if automated testing cannot keep up, the team ends up with a delivery process where code creation accelerates but quality feedback remains stuck in the past.

The 2025 DORA State of AI-Assisted Software Development report describes AI as an amplifier. It can magnify the strengths of high-performing teams, but it can also expose and amplify the weaknesses of teams with poor delivery systems, weak feedback loops, and unstable processes.

That is why test automation matters more now, not less.

The faster your team builds, the faster your tests need to be created, maintained, and executed.

So what is the fastest way to automate tests?

The fastest practical approach is to use an AI-powered no-code test automation platform that can generate editable tests, run them on real browsers in the cloud, support complex end-to-end flows, integrate with CI/CD, and reduce maintenance with self-healing. Writing more Selenium code, or generating large amounts of Playwright code with a general-purpose AI assistant, is slower in practice.

That is the approach behind Endtest, where teams can create, run, and maintain automated tests with no-code, AI-driven automation in the cloud.

Fast test automation is more than fast test writing

When people ask, "What is the fastest way to automate tests?", they usually think about the first version of a test.

How quickly can someone automate a login flow?

How quickly can someone automate a checkout flow?

How quickly can someone replace a manual regression checklist?

Those questions matter, but they are incomplete.

A test that is created quickly but breaks every week is technical debt with a head start.

The real question is broader:

How quickly can your team create, understand, run, maintain, and trust automated tests?

That includes five different types of speed:

1. Speed of creation
2. Speed of execution
3. Speed of maintenance
4. Speed of collaboration
5. Speed of feedback in CI/CD

A strategy that only optimizes the first one is not enough.

You can create a Playwright script quickly and still lose time later on infrastructure, debugging, browser coverage, test data, flaky locators, reporting, and maintenance.

The fastest testing strategy is the one that reduces friction across the full lifecycle of automated testing.

Why traditional Selenium and Playwright automation is slow

Selenium and Playwright are powerful tools.

Selenium helped define browser automation for many years. Playwright improved the developer experience with better browser control, auto-waiting, and modern testing APIs.

But both are still code-first approaches.

That means the team has to build, maintain, and understand a test automation codebase.

For some teams, that is acceptable. For teams that want the fastest path to broad, reliable test coverage, it is often too slow.

Framework setup takes time

Before a serious Selenium or Playwright project becomes useful, someone usually needs to make many decisions:

• Programming language
• Test runner
• Project structure
• Page object model
• Locator conventions
• Assertion style
• Test data strategy
• Authentication handling
• Environment configuration
• Secrets management
• Screenshot capture
• Video recording
• Retry policy
• Reporting
• Parallel execution
• Browser grid provider
• CI/CD integration
• Cross-browser coverage

None of these are impossible, but they take time. And every hour spent building the framework is an hour not spent testing the product.

That is one of the biggest hidden costs of code-first test automation. The first few scripts may look fast, but the full system around them takes much longer.

Test automation becomes dependent on a few people

In many companies, test automation starts with one motivated person.

That person builds a Selenium framework or a Playwright framework. They know how the helper functions work. They understand the folder structure. They know which selectors are safe. They know how to debug the pipeline when it fails.

Then the rest of the team avoids touching it.

Manual testers may not feel comfortable editing code. Product managers cannot easily review the test logic. Support teams cannot quickly turn a recurring customer issue into an automated test. Developers may be too busy shipping features to maintain tests for every flow.

The result is a bottleneck.

Automation exists, but it is not shared.

The tests depend on a small group of people, sometimes only one person. And when that person gets busy, changes role, or leaves the company, the test suite starts to decay.

That is a fragile strategy, not a fast one.

Maintenance becomes the real cost

Most test automation initiatives do not fail on day one.

They fail later, when the product changes faster than the tests can be updated, when every UI change breaks locators, when no one wants to maintain old scripts, and when the team spends more time fixing false failures than catching real bugs.

Maintenance is where code-first automation becomes expensive.

A small UI change can require someone to:

• Investigate the failed test
• Reproduce the failure
• Decide whether it is a real bug or a broken test
• Inspect the DOM
• Update selectors
• Adjust waits
• Modify helper methods
• Review the change
• Merge it
• Re-run the pipeline

Do that once and it is fine.

Do that every week across dozens or hundreds of tests and it becomes a tax on the entire engineering process.

This is why the fastest test automation strategy must include maintenance speed from the beginning.

AI-generated Playwright code is not the shortcut it seems

It is tempting to think that AI coding assistants solved the problem.

Why write Playwright or Selenium manually when you can ask AI to generate it?

For a prototype, that can be useful.

For an experienced automation engineer, AI can speed up repetitive coding tasks.

But as a long-term test automation strategy, AI-generated test code can backfire.

AI can generate code. Generating it faster does not remove the complexity of owning it.

If your testing strategy still depends on code, you still need:

• Architecture
• Reviews
• Debugging
• Dependencies
• Execution environments
• Secrets
• Test data
• Browser infrastructure
• Reports
• Maintenance
• Human understanding of the generated output

AI can help you create a Playwright script quickly.

But then you have to live with that script.

AI-generated test code can become difficult to maintain

AI-generated tests often look impressive at first.

But over time, teams can end up with:

• Duplicate helper functions
• Inconsistent selectors
• Hardcoded waits
• Overly broad assertions
• Repeated login flows
• Mixed coding styles
• Hidden assumptions
• Flaky timing logic
• Tests that only the AI seems to understand

A messy test automation codebase created slowly is a problem.

A messy test automation codebase created quickly is a faster problem.

That is why generating more code is not the same as creating maintainable automated tests.

Every change may require more AI intervention

A good automated test should be easy to understand and update.

But if the test is generated as raw code, future changes often require more prompting, more code review, and more debugging.

The workflow becomes:

1. Ask AI to generate a test
2. Review the generated code
3. Fix what does not work
4. Commit it
5. Later, ask AI to update it
6. Review the new changes
7. Hope the AI did not break something else

That is not terrible for a developer-owned codebase.

But it is not the fastest path for a whole team.

A QA tester, product manager, or support engineer should not need to understand generated Playwright code just to update a checkout test.

The fastest strategy is to make the test itself editable and understandable, not only the prompt.

Generic AI does not give you a complete testing platform

A general-purpose AI assistant can generate code.

It does not automatically give you a complete test automation platform.

It does not instantly provide:

• Cross-browser cloud execution
• Real Chrome, Edge, Firefox, and Safari browsers
• Real Safari on macOS
• Windows and macOS execution machines
• Email testing
• SMS testing
• PDF validation
• File upload and download testing
• Visual testing
• Accessibility testing
• Test scheduling
• Reports and dashboards
• Role-based collaboration
• API access for triggering runs
• Self-healing
• Test import workflows
• Team-friendly no-code editing

You can build or connect many of these pieces yourself. But then you are building again, and building a test automation platform is not the fastest way to test your product.

Low-code and no-code testing changed the speed equation

Before AI became the center of software testing discussions, low-code and no-code test automation tools had already changed the market.

People could avoid writing code, but the bigger shift was that teams no longer had to assemble the entire testing stack themselves.

A good no-code testing platform gives you a complete environment for creating, running, maintaining, and analyzing tests.

That means the team does not need to separately build:

• A test framework
• A browser execution layer
• Reporting
• Scheduling
• Cross-browser infrastructure
• Team access controls
• CI/CD integration
• Debugging artifacts
• Test management workflows

The platform becomes the shared workspace for quality.

That is much faster than treating automation as an internal engineering project.

No-code testing democratizes automation

When tests can be created and updated without writing code, more people can contribute.

A QA tester can automate a regression scenario. A product manager can review a critical user journey. A support engineer can turn a recurring customer complaint into a test case. A developer can add coverage before merging a risky change. A designer can understand what is being validated without reading a test framework.

That matters because software quality is not only an engineering concern.

The people closest to the product often know which flows matter most. But in a code-first automation strategy, they usually cannot contribute directly.

No-code testing changes that.

It turns automation from a specialist-only activity into a team-wide capability.

That is one of the biggest reasons it is faster.

A complete platform is faster than a browser automation library

Selenium and Playwright automate browsers.

A complete testing platform automates the testing process.

Those are different things.

Modern end-to-end testing often involves more than clicking buttons in a browser.

A realistic test may need to:

• Create a user
• Log in
• Submit a form
• Receive an email
• Extract a confirmation link
• Enter an SMS code
• Download a PDF
• Validate invoice content
• Make an API call
• Check a database value
• Run across Chrome, Edge, Firefox, and Safari
• Produce a report
• Notify the team
• Block a CI/CD pipeline if something fails

You can build all of that with code and integrations.

But if speed is the goal, using a platform that already includes these capabilities is usually faster.

That is why Endtest includes broad end-to-end testing capabilities in one place, instead of forcing teams to assemble everything from scratch.

AI made no-code testing even faster

No-code test automation was already a major improvement over framework-heavy automation.

Then AI made it faster.

The best use of AI in test automation is helping teams create, import, maintain, and validate tests inside a structured testing platform, rather than generating more code.

If AI generates raw Playwright code, the output is code.

If AI generates standard editable steps inside a testing platform, the output is a maintainable test asset that the whole team can understand.

That is a much stronger foundation for long-term speed.

AI test creation

With the Endtest AI Test Creation Agent, you can describe a test scenario in plain English and let AI generate the test.

You can give granular instructions:

Go to the pricing page, select the Pro plan, choose monthly billing, and verify that the checkout page opens.

Or you can give a higher-level goal:

Test the full sign-up flow, including email confirmation and upgrading to a paid plan.

The AI agent can generate standard Endtest steps that can be reviewed, modified, reused, and executed like any other test.

The generated output becomes a normal editable test, not an opaque AI action that only works inside a black box.

AI helps create the test, but the test does not need to depend on AI for every execution.

That gives teams the speed of AI without giving up control.

AI test import

Many teams already have tests somewhere.

They may have Selenium scripts, Playwright scripts, Cypress tests, manual test cases, spreadsheets, old regression checklists, or documentation pages describing important flows.

Starting from zero would be wasteful.

AI-powered test import helps teams convert existing material into editable platform-native tests.

That can be one of the fastest ways to modernize test automation.

Instead of manually rewriting old tests or copying steps one by one, teams can reuse existing knowledge and transform it into something easier to maintain.

Do not throw away what you already have. Upgrade it into a format the whole team can use.

AI assertions

Traditional assertions can be too rigid for complex workflows.

Sometimes you do not want to validate one exact string or one exact selector.

You want to validate a business outcome.

For example:

• The invoice looks correct.
• The generated email contains the expected confirmation details.
• The dashboard shows a reasonable summary.
• The downloaded PDF includes the right customer and amount.
• The page reflects the expected state after a workflow is completed.

AI Assertions make it easier to validate these outcomes using natural language.

This saves time because the team does not need to write custom parsing logic or fragile low-level checks for every scenario.

The assertion can focus on the meaning of the result, not only the implementation details.

AI-assisted maintenance and self-healing

Fast test creation is not enough.

The test suite must stay useful after the product changes.

That is where self-healing matters.

With Self-Healing Tests, a platform can reduce maintenance work when locators change. Instead of failing immediately because an element changed its class, moved in the DOM, or received a new attribute, the system can use backup locators and AI assistance to identify the intended element.

This does not mean real bugs should be ignored. A broken checkout flow, a missing button, or a real regression should still fail. But harmless UI implementation changes should not consume hours of maintenance time.

The fastest test automation strategy is fast at creating tests and fast at keeping them alive.

The fastest practical workflow for automating tests

The fastest way to automate tests is to start with the flows that matter most and use AI to accelerate the process, instead of automating random screens.

Here is a practical workflow.

1. Start with critical user journeys

Begin with the flows that would hurt the business if they broke.

Examples:

• Sign up
• Log in
• Password reset
• Checkout
• Payment confirmation
• Subscription upgrade
• Trial activation
• Form submission
• Search and filtering
• Booking or reservation
• Email verification
• Invoice generation
• Account cancellation

Do not start by trying to automate everything.

Start by automating what must not break.

That gives you the fastest business value.

2. Use AI to generate the first version

Describe each flow in plain English.

For simple tests, use clear step-by-step instructions.

For broader coverage, use higher-level goals and let the AI agent inspect the application and create the steps.

This is where the speed difference becomes obvious.

Instead of writing framework code, configuring waits, choosing selectors, and building helper functions, you get a working test structure quickly.

Then you review it, and that review step matters. AI should accelerate test creation, not remove human judgment.

3. Edit the generated steps

The generated test should not be locked inside code or hidden inside an AI-only execution layer.

It should be editable.

With Endtest, generated tests become standard test steps. You can adjust locators, add variables, reuse test cases, update assertions, add file checks, and modify the flow without asking AI to rewrite a script.

This is one of the biggest advantages over AI-generated Selenium or Playwright code.

You are creating test assets that the team can understand immediately, not a codebase that needs to be interpreted later.

4. Add real end-to-end coverage

Fast test automation should not stop at browser clicks.

Modern user journeys often include more than one system.

A sign-up flow may require email confirmation.

A checkout flow may generate a PDF invoice.

A login flow may involve SMS verification.

A customer workflow may require API setup, database validation, file downloads, or visual checks.

This is where a complete platform matters.

Endtest supports complex end-to-end capabilities such as email testing, SMS testing, API testing, PDF and file testing, Visual AI, accessibility testing, and database steps.

That makes it possible to test complete workflows rather than isolated browser interactions.

5. Run tests on real browsers in the cloud

A test that only runs locally, in one browser, or in a browser engine inside a Linux container is not enough.

Real users do not all use the same browser, operating system, or device.

That is why cross-browser testing matters.

Endtest can run tests on real browsers across Windows and macOS machines, including real Safari on macOS.

This is an important difference from Playwright, which uses WebKit for Safari-like browser coverage. WebKit is useful, but it is not the same as testing on the real Safari browser.

If Safari users matter to your business, real Safari coverage matters too.

6. Integrate tests into CI/CD

The fastest testing strategy is the one that catches defects early.

That means automated tests should not run only before major releases.

They should run as part of the delivery workflow.

With the Endtest API, teams can trigger test runs, retrieve results, and integrate automated testing into CI/CD pipelines.

Testing becomes a continuous feedback loop instead of a manual checkpoint at the end of the release cycle. Developers get faster feedback, QA teams spend less time repeating the same checks, and product teams release with more confidence.

7. Expand coverage gradually

Once the most critical flows are covered, expand in layers.

A practical order looks like this:

1. Revenue-critical flows
2. Authentication and account flows
3. High-traffic user journeys
4. Recently changed areas
5. Repeated manual regression checks
6. Browser-specific risk areas
7. Mobile and responsive layouts
8. Email, SMS, PDF, and file flows
9. Visual regression checks
10. Data-driven edge cases

This keeps automation practical. The goal is the most useful safety net, not the largest possible test suite.

What makes Endtest faster than Playwright for many teams

Playwright is a strong browser automation framework.

But a framework is not the same thing as a complete testing platform.

For teams that want the fastest path to reliable end-to-end automation, Endtest can be faster in several important ways.

No framework setup

With Playwright, you still need to set up the project, structure the tests, configure browsers, connect reporting, manage CI, and decide how the team will maintain everything.

With Endtest, the platform is already there.

You can focus on creating tests instead of building the system around the tests.

No-code editing

Playwright tests are code.

That limits who can create and maintain them.

Endtest tests are editable through a shared no-code interface, which makes automation accessible to QA, product, support, and non-engineering team members.

That is faster, because the work can be distributed across the team.

Built-in cloud execution

Playwright can run locally or in your own CI environment, but serious cross-browser execution usually requires additional infrastructure or a third-party browser cloud.

Endtest includes cloud execution as part of the platform.

That means less setup, fewer moving parts, and faster time to useful coverage.

Real Safari support

Playwright provides WebKit coverage.

Endtest can execute tests on real Safari running on macOS.

For teams where Safari matters, this is a major difference.

Some browser-specific issues only appear in real Safari. If a checkout bug, date picker issue, file upload problem, or payment widget error appears only in Safari, you want to catch it before users do.

Capabilities beyond browser automation

Playwright automates browser interactions well.

But many real workflows require more than browser clicks.

Endtest brings browser automation together with email testing, SMS testing, PDF validation, API testing, database steps, visual checks, scheduling, reporting, and CI/CD integration.

That makes it easier to test complete user journeys in one place.

AI built for test automation

General-purpose AI coding tools are optimized for many coding tasks.

Endtest AI is built around test automation workflows.

The AI Test Creation Agent is designed to inspect real pages, reason about testing instructions, generate editable test steps, and create maintainable automation assets.

The goal is to produce reliable tests faster, not more code.

Endtest also offers Unlimited AI, without token credits or surprise AI invoices. That makes it easier for teams to use AI naturally across test creation, maintenance, imports, and assertions without worrying about prompt limits or unpredictable AI costs.

You can explore the available plans on the pricing page.

What should you automate first?

Do not automate everything from the bottom up. Start with flows where automation creates immediate value.

High-risk flows

Automate the flows that cause the most damage when they break.

Examples:

• Checkout
• Payment
• Onboarding
• Login
• Password reset
• Subscription management
• Booking
• Account creation
• Email verification

These flows deserve early automation because they protect revenue, access, and customer trust.

High-frequency flows

Automate the flows that users perform most often.

If thousands of users perform a flow every day, it deserves automated coverage.

A small bug in a high-frequency flow can create a large support burden.

Recently changed flows

AI-assisted development can increase the number of changes shipped by a team.

That makes recently modified areas especially important.

If a feature changed this week, add or update tests for it this week.

Do not wait for the regression suite to catch up later.

Flows that are painful to test manually

Some workflows are slow and repetitive for humans.

Examples:

• Creating test accounts
• Waiting for emails
• Checking SMS codes
• Downloading PDFs
• Verifying invoices
• Testing multiple browsers
• Running the same scenario with multiple data sets

These are excellent automation candidates because they immediately save time.

Browser-sensitive flows

Some issues appear only in specific browsers or operating systems.

Checkout, uploads, downloads, drag-and-drop, date pickers, file inputs, responsive layouts, and payment widgets can behave differently across browsers.

If a flow is important, test it across the browsers and operating systems your users actually use.

Why fast automation improves release confidence

Fast test automation changes how teams release.

Without automation, teams often delay releases because they need another manual regression cycle.

Or they release anyway and hope nothing important broke.

Both options are bad.

With fast, reliable automated tests, teams can release more often with less anxiety.

A good test suite gives you confidence that:

• Critical flows still work
• Recent changes did not break core behavior
• Browser-specific issues are less likely to surprise you
• Email and SMS flows still function
• PDFs and files are generated correctly
• Visual regressions are detected earlier
• CI/CD catches problems before users do

That means a Friday release does not have to feel reckless.

If your critical end-to-end flows are tested across real browsers, and the results are clear, you can head into the weekend with confidence. Bugs are still possible, but your safety net is stronger.

The fastest way is not more code

For a long time, test automation speed was measured by how quickly an engineer could write scripts.

That is no longer the right metric.

The better question is:

How quickly can the team create, review, run, maintain, and trust automated tests?

If your strategy depends on writing and maintaining code for every test, it will usually be slower than it looks.

If your strategy depends on AI generating more code, it may feel fast at the beginning but become hard to manage later.

The fastest sustainable approach is different. Use AI to generate tests and keep the output editable. Run those tests on real browsers through a complete cloud platform, integrate the results into CI/CD, use self-healing to reduce maintenance, and let the whole team contribute.

That is how teams create automated tests quickly without turning test automation into another fragile engineering project.

FAQ

What is the fastest way to create automated tests?

The fastest way to create reliable automated tests is to use an AI-powered no-code testing platform. Instead of writing Selenium or Playwright code, you describe the scenario in plain English and generate editable test steps that can run in the cloud.

Is Playwright faster than Selenium?

Playwright is often faster and more modern than Selenium for developers writing browser automation code. But if the goal is fastest team-wide test creation, a no-code AI-powered platform can be faster than both because it removes framework setup, coding, browser infrastructure, and much of the maintenance work.

Should I use AI to generate Playwright tests?

AI can help generate Playwright tests, especially for prototypes or developer-owned suites. But teams should be careful. AI-generated code still has to be maintained, reviewed, debugged, and integrated. For many teams, AI-generated editable steps inside a testing platform are easier to maintain than AI-generated code.

Why is no-code testing faster?

No-code testing is faster because more people can create and update tests without learning a programming framework. It also removes much of the setup work around browser drivers, reporting, cloud execution, debugging artifacts, and CI/CD integration.

Why does real Safari testing matter?

Safari can behave differently from Chrome, Firefox, Edge, and WebKit-based approximations. If your users include Safari users, testing on real Safari can catch issues that may not appear in other browsers or in browser engines running inside Linux containers.

Can automated tests be created in a few hours?

Yes. With an AI-powered platform, teams can create many useful end-to-end test suites in a few hours, especially when starting with critical flows like login, sign-up, checkout, email verification, and subscription changes. Generate, review, and refine tests quickly instead of building a framework first.

Final answer

The fastest way to automate tests is to use an AI-powered test automation platform that creates editable, reliable tests from plain-English instructions, runs them on real browsers in the cloud, supports complex end-to-end workflows, reduces maintenance with self-healing, and integrates with CI/CD.

Writing Selenium code faster, or generating more Playwright code with AI, does not get you there.