# How to Get Started with Automated Testing A practical beginner guide to test automation, covering Selenium, Playwright, AI tools, no-code testing, and how to start safely today. Published June 4, 2026 by Liviu Lupei on the Endtest blog. Canonical URL: https://endtest.io/blog/how-to-get-started-with-automated-testing --- ## Why automated testing matters Automated testing can sound intimidating when you first hear about it. It may sound like something only senior developers, QA automation engineers, or large engineering teams can use. It may also sound like a magical solution that replaces manual testing completely. Neither of those ideas is true. Automated testing is simple: you define a set of actions and checks that can be executed repeatedly, without a person manually clicking through the same flow every time. For a web application, that might mean: - Open the login page. - Enter an email and password. - Click the login button. - Verify that the dashboard appears. - Open the billing page. - Verify that the subscription details are displayed correctly. The goal is to free up time and resources, reduce repetitive work, and get faster feedback when something breaks, not to remove humans from the testing process. That feedback matters because modern software changes constantly. A developer updates a button. A designer changes a modal. A backend engineer adjusts an API response. A third-party service changes its behavior. A browser update introduces a subtle compatibility issue. Without automation, someone has to manually verify the same important flows again and again. That gets expensive, slow, and inconsistent. With automation, the team can get a quick answer to a very important question: Is this feature, or even this entire web application, still working as expected? ## My first encounter with test automation My first job in the web development sector was in 2012, as a manual software tester at an online retailer. That was when I first heard about test automation. I remember being excited by the idea. Instead of manually repeating the same test scenarios every day, we could create automated tests that would run for us. It sounded obvious. It sounded useful. It sounded like the future. But I also remember being disappointed by the state of the tools back then. The idea was great, but the experience was not. Most tools were either too technical, too fragile, too slow, or too difficult for the entire team to adopt. You needed programming knowledge, infrastructure, browser drivers, debugging skills, and a lot of patience. For a manual tester who simply understood the product and wanted to validate real user flows, the barrier was too high. That experience stayed with me. Years later, it became one of the reasons we built Endtest. The goal was to democratize test automation, so that more people could automate tests without depending on a programming language, without waiting for a specialized automation engineer, and without ending up with a test suite that only one person knows how to maintain. ## What automated testing actually means Automated testing is not one single thing. There are different levels of testing, and each one answers a different type of question. Unit tests check small pieces of code in isolation. For example, a function that calculates a discount. API tests check whether services and endpoints return the expected data. Integration tests check whether different parts of the system work together. End-to-end tests check whether a real user journey works from start to finish. When most beginners think about automated testing, they usually think about end-to-end testing. That is the type of testing where a browser opens, interacts with the application, clicks buttons, writes text, submits forms, and checks results. This is also the type of testing that often feels most valuable to non-technical people, because it resembles how a real user interacts with the application. For example: A customer signs up, receives an email confirmation, logs in, adds a product to the cart, pays, and sees the order confirmation page. That flow can involve the frontend, backend, database, email provider, payment provider, browser behavior, and multiple pages. A unit test cannot fully validate that. An API test cannot fully validate that. You need an end-to-end test to confirm that the whole journey works. ## Start with the most important user flows The biggest mistake beginners make is trying to automate everything. That usually fails. A better approach is to start with the flows that would hurt the business if they stopped working. For an e-commerce website, that might be: - Sign up - Log in - Search for a product - Add to cart - Checkout - Apply discount code - Complete payment - Receive order confirmation For a SaaS application, that might be: - Sign up - Email verification - Log in - Create a project - Invite a team member - Use the core feature - Update billing details - Cancel subscription For an internal admin tool, that might be: - Log in as admin - Search for a customer - Update customer data - Export a report - Verify permissions for different user roles A good first test suite needs a small number of reliable tests that cover the most important things your users do, not 500 tests. If those tests run consistently, the team starts trusting automation. If the team trusts automation, they start using it. If they start using it, automation becomes part of the development process instead of a side project. ## Automated testing does not replace manual testing Automated testing is powerful, but it cannot fully replace manual testing. Humans are not better at clicking buttons. But some things require human judgment. A test can verify that a button exists, that a page loads, that a modal appears, or that a mobile layout has no obviously broken elements. A person may still need to decide whether that button is confusing, whether the page feels overwhelming, whether the copy sounds trustworthy, or whether the experience feels comfortable on a real phone. Manual testing is still valuable for: - Exploratory testing - Usability impressions - Visual judgment - Product feel - Copy review - Accessibility review with human context - Testing brand-new features before they stabilize - Investigating strange behavior that nobody expected Automation is best for repeatable checks. Manual testing is best for discovery, judgment, and context. The healthiest teams use both. ## The traditional path: Selenium, Puppeteer, Cypress, and Playwright For many years, the common way to automate browser tests was to write code. [Selenium](https://www.selenium.dev/) became the first widely popular library for web UI automation. It allowed teams to control browsers and write tests in programming languages such as Java, Python, C#, Ruby, and JavaScript. Later, other tools became popular. [Puppeteer](https://pptr.dev/) made it easier to control Chrome from JavaScript. [Cypress](https://www.cypress.io/) became popular because it offered a developer-friendly testing experience, especially for frontend teams. [Playwright](https://playwright.dev/) became popular because it supports modern browser automation across Chromium, Firefox, and WebKit, and it provides strong debugging and tracing capabilities. These tools are useful. They are not bad tools. A skilled engineer can build impressive test automation with them. For teams that already have strong development resources, a clear architecture, and long-term ownership, code-based automation can work well. But many teams underestimate one problem. The output is code, and code has a cost. ## Why internal automation frameworks often fail When a company decides to build its own Selenium or Playwright framework, the first demo often looks great. Someone writes a few tests. The browser opens. The test logs in. The test clicks around. Everyone is impressed. Then real life begins. The app changes. Locators break. Tests become flaky. Someone adds custom helpers. Someone else adds another layer. The original author leaves the company or moves to another project. New team members do not understand the framework. Product managers cannot read the tests. Manual testers cannot update them. Developers do not want to review giant test pull requests. Slowly, the test suite becomes something that exists, but does not get used properly. This is the pattern I have seen many times: a company sets out to build tests and accidentally builds another software project. That project needs architecture, code review, debugging, refactoring, documentation, dependency updates, CI configuration, browser maintenance, and ownership. The tests were supposed to save time. Instead, the team spends a huge amount of time maintaining the framework around the tests. This is one reason internally built Selenium and Playwright frameworks often have a low long-term success rate. Selenium and Playwright can automate the browser. The weak part is the team adoption model. If only one or two people can create and maintain the tests, automation becomes a bottleneck. If the output is difficult for non-technical people to understand, the people who know the product best are excluded from the automation process. If every change requires a code review, automation becomes slower than it needs to be. ## Why adding AI to Playwright does not fully solve it AI can generate Playwright code. That sounds useful, and sometimes it is. You can ask an AI model to create a test, update a selector, debug a failure, or explain why a test is not working. But adding AI to a code-based workflow does not automatically solve the core problem. In many cases, it simply changes the problem. Instead of having a codebase that one automation engineer understands, you may end up with a codebase that only the AI really understands. That is dangerous. If a human cannot quickly understand what the test is doing, the test becomes a black box. You are not fully sure if it is testing what it is supposed to test, if a generated assertion is meaningful, or if the AI added a workaround that hides the real issue. And because the output is still code, you still need to manage code. You still need reviews, debugging, maintenance, and people who understand the structure. You still need to pay the token cost every time the AI needs to inspect, reason about, and modify that code. That can become expensive, financially and operationally. You become dependent on AI to maintain something that humans do not really understand anymore. That is a strange outcome. It is silly to use AI to produce an output that is difficult for humans to understand and maintain. The best use of AI in test automation is to accelerate the process while still leaving behind a clean, editable, human-readable test, rather than generating mysterious code. ## The no-code path This is where no-code test automation tools become interesting. No-code means the test is represented in a way that more people can understand, not that there is no logic. Instead of this being hidden inside a code file, the test can be displayed as steps: - Go to URL - Click "Sign up" - Write text in "Email" - Write text in "Password" - Click "Create account" - Verify that the dashboard is displayed That output is easier to read, review, and update. It is easier for a manual tester, product manager, founder, support engineer, or web developer to understand what the test is supposed to do. Modern no-code test automation tools can also use AI to accelerate the process. AI can help create tests faster, suggest assertions, identify elements, repair broken steps, debug failures, and explain what went wrong. But the final test should still be editable by humans. That is the difference between using AI as an assistant and using AI as a black box. No-code tools are not perfect. No tool is. You still need to choose what to test, review results, and maintain tests when the product changes. But for many teams, no-code automation is clearly more efficient because it removes the biggest adoption barrier: the need for everyone to write and maintain test code. ## What a good beginner workflow looks like If you are just getting started with automated testing, do not start by comparing 40 tools. Start by identifying one important flow. Pick something that is stable, valuable, and easy to understand. A good first automated test might be: - Open the homepage. - Click the login button. - Enter valid credentials. - Verify that the user reaches the dashboard. - Log out. That is enough. Then ask a few questions: Does the test run reliably? Can someone else understand it? Can someone else update it? Can it run in the browser your users actually use? Can it run before a release? Can it show you what failed? Can it save time compared to doing the same check manually? If the answer is yes, automate another flow. If the answer is no, fix the workflow before scaling. The worst thing you can do is create 100 unreliable tests and then spend the next six months trying to understand why nobody trusts them. A small reliable test suite is better than a large fragile one. ## What should you automate first? Start with regression tests. Regression tests verify that existing functionality still works after new changes. These are usually the best candidates because they are repetitive and business-critical. Good first candidates include: - Login - Sign up - Password reset - Checkout - Payment - Search - Form submission - User invitation - File upload - Report generation - Email verification - Subscription management Avoid starting with unstable features that are still changing every day, flows that require too much manual judgment, or edge cases that rarely happen. Start with the boring but important flows. Those are the flows that waste the most time when checked manually and cause the most damage when broken. ## What makes a test maintainable? A test is maintainable when someone can quickly answer three questions. What is this test doing? Why is this test important? How do I update it when the product changes? This is where many code-based frameworks become difficult for beginners. The test might technically work, but the meaning is hidden inside helper functions, page objects, selectors, fixtures, and custom abstractions. A simple login test may be spread across multiple files. That may be fine for engineers, but it is not ideal for a broader team. For beginner-friendly automation, clarity matters more than cleverness. A maintainable test should be: - Easy to read - Easy to edit - Easy to debug - Easy to run - Easy to explain - Connected to a real user journey - Stable enough to trust The test should not feel like a puzzle. ## Where Endtest fits in We built [Endtest](https://endtest.io/) around the idea that test automation should be accessible. My goal was to democratize test automation. A manual tester should be able to automate a flow. A developer should be able to quickly validate a feature. A manager should be able to understand what is being tested. A team should not need to spend months building and maintaining an internal framework before they get value from automation. Endtest was designed to make tests easy to create, easy to read, and easy to maintain. It includes AI capabilities to speed up creation, maintenance, and debugging. But the output remains clean and editable. You are not left with a mysterious codebase that nobody wants to touch. Endtest also includes unlimited AI, so teams can use AI throughout the testing process without constantly worrying that every maintenance task is consuming a limited AI budget. Beyond AI, Endtest supports the things teams usually need when they move from a simple demo to real test automation: - Web application testing - Cross-browser testing - Real browser execution - Safari testing - API testing - Email testing - SMS testing - Variables and reusable data - Logical steps such as conditions - Screenshots and videos for debugging - Test scheduling - Team collaboration - Clear test steps that can be reviewed by humans You can also review the available plans on the [Endtest pricing page](https://endtest.io/pricing). No-code automation does not remove every challenge, but it removes unnecessary friction. You should spend your time improving test coverage, not building test infrastructure from scratch. ## A simple first-week plan If you are new to automated testing, here is a practical way to start. Day 1: Write down your five most important user flows. Do not automate yet. Just list the flows that would cause real damage if they broke. Day 2: Pick one flow and define the expected result. For example, if the flow is login, define what a successful login means. Does the dashboard appear? Does the URL change? Is the user name displayed? Day 3: Automate that one flow. Use a tool that lets you create the test quickly and understand the output. Do not worry about advanced architecture yet. Day 4: Run the test multiple times. A test that passes once is not enough. Run it again. Run it in another browser if relevant. Run it after a small product change. Day 5: Break something on purpose. Change a button label, modify a selector, or use invalid data. See how the failure is reported. A good automation tool should make failures understandable. Day 6: Ask someone else to review the test. If another person can understand it without a long explanation, that is a good sign. Day 7: Add one more test. Only scale after the first test is reliable and understandable. That is how automation becomes useful. Start small, make it reliable, make it understandable, then expand. A giant framework, thousands of lines of AI-written code, or trying to automate everything at once will not get you there. ## Common beginner mistakes The most common mistakes are automating too much too soon, choosing a tool only because developers like it, ignoring maintenance, treating automation as a one-person responsibility, and believing that AI can magically handle the whole process. Good automation is a team asset. It should not belong only to the automation engineer. The people who understand the product should be able to contribute. The people who release the product should be able to trust it. The people who investigate failures should be able to understand what happened. If automation is hidden behind code that nobody wants to touch, adoption will stay low. If automation is clear, useful, and easy to maintain, adoption grows naturally. ## Final thoughts Automated testing is about giving teams faster feedback and removing repetitive work. It is not about replacing testers or developers, and it is not about pretending that AI can do everything. The best place to start is with one important user flow that your team currently checks manually, not with a huge framework or a complex strategy document. Automate it, run it, review it, improve it, then automate the next one. If the output is clean, editable, and understandable by humans, the entire team has a much better chance of actually using it.