And software testing of any kind (including this one) is performed via the utilization of comprehensive test cases (aka test scenarios). What is the first screen you see when you start interacting with a software product? Right, it is its login page. But this page is not only a user’s entry path to the solution. It is also the product’s first line of defense against unauthorized access and credentials theft.

That is why the fast and secure login process is mission-critical for solutions of all kinds, which can be ensured during their software development through out-and-out testing. And software testing of any kind (including this one) is performed via the utilization of comprehensive test cases (aka test scenarios).

This article explains what a test scenario for Login page is, enumerates login page components that should undergo a testing process, and helps QA teams write test case for Login page by showcasing their types and tools useful in automation test cases for Login pages, giving practical tips on how to write test scenarios for Login page, and specifying the procedure of generating test cases using Testomat.io.

Understanding Test Cases for Login Page

First, let’s clarify what a test case is. In QA, test cases are thoroughly defined and documented checking procedures that aim to ensure a software product’s function or feature works according to expectations and requirements. It contains detailed instructions concerning the testing preconditions, objectives, input data, steps, and both expected and actual results. Such a roadmap enables conducting a structured, repeatable, and effective checking routine that helps identify and eliminate defects.

The same is true for login page test cases that are honed to validate a solution’s login functionality, covering such aspects as UI behavior, valid/invalid login attempts, password requirements, error handling, security strength, etc. The ultimate goal of software testing test cases for Login page is to guarantee a swift and safe sign-in process across different devices and environments, which contributes to the overall seamless user experience of an application. When preparing to write test cases for Login page, you should have a clear vision of what you are going to test.

Dissecting Components of a Login Page

No matter whether you build a Magento e-store, a gaming mobile app, or a digital wallet, their login pages contain basically identical elements.

• User name. As a variation, this item may be extended by the phone number or email address. In short, any valid credentials of a user are entered here.
• Password. This field should mask (and unmask on demand) the user’s password.
• Two-factor authentication. This is an optional element present on the login pages of software products with extra-high security requirements. As a rule, this second verification step involves sending a one-time password to the user via email or SMS.
• “Submit” button. If the above-mentioned details are correct, it initiates the authentication process, thus confirming it.
• “Remember me” checkbox. It is called to streamline future logins by retaining the user’s credentials.
• “Forgot Password” link. If someone forgets their password, this functionality allows them to reset it.
• Social login buttons. Thanks to these Login page functions, a user can sign in via social media (like Facebook or LinkedIn) or third-party services (for instance, a Google account).
• Bot protection box. Also known as CAPTCHA, the box verifies the user as a human and rules out automated login attempts.

Naturally, test scenarios for Login page should cover all those components with a series of comprehensive checkups.

Types of Test Cases for Login Page in Software Testing

Let’s divide them into categories.

Functional test cases for Login page

They are divided into positive and negative test cases for Login page. The difference between them lies in the data they use and the objectives they pursue. Positive test cases for Login page operate expected data and focus on confirming the page’s functionalities. Negative test cases for Login page rely on unexpected data to expose vulnerabilities.

Each positive test scenario example for Login page in this class aims to validate the page’s ability to authenticate users properly and direct users to the dashboard. Positive test cases include.

• Successful login with valid credentials (not only the actual name but also email address or phone number).
• Login with the enabled multi-factor and/or biometric authentication.
• Login with uppercase or lowercase in the username and password (aka case sensitivity test). The login should be permitted only when the expected case is present in the input fields.
• Login with a valid username and a case-insensitive password.
• Successful login with a remembered username and password.
• Login with the minimum/required length of the username and password.
• Successful login with a password containing special characters.
• Login after password reset and/or account recovery.
• Login with the “Remember Me” option.
• Valid login using a social media account.
• Login with localization settings (for example, different languages).
• Simultaneous login attempts from multiple devices.
• Login with different browsers (Firefox, Chrome, Edge).

Negative functional test cases for a login page presuppose denial of further entry and displaying an error message. The most common negative scenarios are:

• Login with invalid credentials (incorrect username plus valid password, valid username plus incorrect password, or both invalid user input data).
• Login without credentials (empty username and/or password fields).
• Login with an incorrect case (lower or upper) in the username field.
• Login with incorrect multi-factor authentication codes sent to users.
• Login with expired, deactivated, suspended, or locked (after multiple failed login attempts) accounts.
• Login with a password that doesn’t meet strength requirements.
• Login with excessively long passwords or usernames (aka edge cases).
• Login after the session has expired (because of the user’s inactivity).

Non-functional test cases for Login page

While functional tests focus on the technical aspects of login pages in web or mobile applications, non-functional testing centers around user experience, ensuring the page is secure, efficient, responsive, and reliable. This category encompasses two basic types of test cases.

Security test cases

The overarching goal of security testing is to guarantee the safety of the login page. The sample test cases for Login page’s security are as follows:

• Verify the page uses HTTPS to encrypt data transmission in transit and at rest.
• Check automatic logout after inactivity (timeout functionality).
• Enter JavaScript code in the login fields (cross-site scripting (XSS)).
• Test for weak password requirements.
• Attempt to hijack a user’s session to identify session fixation vulnerabilities.
• Ensure the page doesn’t reveal whether a username exists in the system.
• Secure hashing and salting of passwords in the database.
• Attempt to overlay the page with malicious content (the so-called clickjacking).
• Ensure secure generation and storage of session management tokens and cookies.
• Test the security of account recovery and password reset procedures.
• Assess SQL injection vulnerabilities (see details below in a special section).
• Check the page’s resistance to DDoS attacks.
• Gauge the system’s compliance with industry-specific and general security regulations.

Usability test cases

The purpose of each sample test case for Login page of this class is to ensure the landing page has superb user experience parameters (design intuitiveness, accessibility, visibility, responsiveness, cross-browser compatibility, localization, and others).

• Verify the visibility of design elements (username and password fields, login button, “Forgot Password” link, “Remember Password” checkbox, etc.) and error messages for failed login attempts.
• Check that all buttons have identical placement and spacing on different devices.
• Ensure clear instructions and accessible options enabling users to easily find the registration page.
• Test the page’s response time on devices with different screen sizes.
• Verify the font size adjustment for each screen size.
• Test the UI’s responsiveness to landscape/portrait transitions when the device’s orientation changes.
• Check the page’s efficient operation across various browsers.
• Make sure the page is accessible for visually and kinetically disadvantaged users.
• Verify the page’s operation across different regions, time zones, and languages.

BDD test cases for Login page

Conventionally, manual test cases for Login page rely on test scripts written in a specific programming language. What if you lack specialists in any of them? BDD (behavior-driven development) tests are just what the doctor ordered.

A typical BDD test case example for Login page consists of three statements following a Given-When-Then pattern. The Given statement defines the system’s starting point and establishes the context for the behavior.

The When statement contains the factor triggering a change in the system’s behavior. The Then statement describes the outcome expected after the event in the previous statement occurs. Here are some typical functional BDD test cases for the Login page.

Testing successful login
Given a valid username and password,
When I log in,
Then I should be allowed to log into the system.
Testing username with special characters
Given a username with special characters,
When I log in,
Then I should successfully log in. 
Testing an invalid password with a valid username
Given an invalid password for a valid username,
When I log in,
Then I should see an error message indicating the incorrect password
Testing empty username field
Given an empty username field,
When I log in,
Then I should see an error message indicating the username field is required.
Testing multi-factor authentication
Given a valid username and password with multi-factor authentication enabled,
When I log in,
Then I should see a message prompting to enter an authentication code.
Testing locked account
Given a locked account due to multiple failed login attempts,
When I log in,
Then I should see an error message indicating that my account is locked.
Testing the Remember Me option
Given a valid username and password with "Remember Me" selected,
When I log in,
Then I should remain logged in across sessions.
Testing password reset request
Given a password reset request,
When I follow the password reset process,
Then I should be able to enter a new password.
Testing account recovery request
Given an account recovery request,
When I follow the account recovery process,
Then I should be able to regain access to my account.

UI test cases for Login page

In some aspects, UI testing is related to usability checks, but there is a crucial difference. While usability test cases are called to ensure UX of the login page, UI test cases verify that its graphical elements (buttons, icons, menus, text fields, and more) appear correctly, are consistent across multiple devices and platforms, and function according to expectations. Here are some UI test cases for Login page examples.

• Check the presence of all input fields on the page.
• Verify the input fields accept valid credentials.
• Ensure the system rejects login attempts after reaching a stipulated limit and displays a corresponding message.
• Verify that the system displays an error message when a login is attempted with empty username and/or password fields and invalid username and/or password.
• Confirm that the “Remember Password” checkbox selection results in saving credentials for future sessions.
• Ensure the password isn’t compromised when using the “Remember Password” option.
• Validate the presence and functionality of the “Forgot Password” link.
• Confirm users receive instructions on how to reset their password.
• Test the procedure of receiving and verifying the email to reset the password.
• Check the system’s response when a user enters an invalid email to reset the password.
• Ensure users get confirmation messages after resetting their passwords.
• Validate the visibility of all buttons and input fields on the Login page.
• Verify the page displays content correctly and functions properly when accessed through different browsers and their versions.
• Ensure uniform styling across browsers by validating CSS compatibility.

Performance test cases for Login page

Performance testing is a pivotal procedure for guaranteeing the smooth operation of the login page. The most common performance test cases for Login page include:

• Gauge the time under normal and peak load conditions the login page needs to respond to user inputs.
• Assess the number of successful logins within a specified time frame.
• Check how the page handles certain amounts of simultaneous logins.
• Check the system’s stability (memory leaks, performance degradation, etc.) during continuous usage over an extended period.
• Simulate various scenarios of the network conditions to assess the page’s latency.
• Track system resource utilization during login operations.

CAPTCHA and cookies test cases for Login page

For the first Login page functionality, the test cases are:

• Verify the presence of CAPTCHA on the page.
• Confirm CAPTCHA appears after a definite number of failed login attempts.
• Check the ability of the CAPTCHA image refreshment.
• Ensure a reasonable timeout for the CAPTCHA to avoid its expiration.
• Check the login prevention for invalid CAPTCHA.
• Validate CAPTCHA alternative options (text or audio).

Test cases for cookies include:

• Verify the setting of a cookie after successful login.
• Check the cookie’s validity across multiple browsers until its expiry.
• Ensure the cookie deletes after logout or session expiry.
• Verify the cookie’s secure encryption.
• Validate that expired/invalid cookies forbid access to authenticated pages and redirect the user to Login page.

Gmail Login page test cases

Since the Google account is the principal access point for many users, it is vital to ensure a smooth entry into an application via the Gmail login page. The tests undertaken here are similar to other test cases described above.

• Verify login with a valid/invalid Gmail ID and password.
• Check “Forgot email” and “Forgot password” links.
• Validate the operation of the “Next” button when entering the email.
• Ensure masking of the password.
• Ensure account lockout after multiple failed attempts.
• Confirm “Remember me” functionality.
• Validate login failure after clearing browser cookies.
• Verify the support of multiple languages on the Gmail login page.
• Evaluate the Gmail login page during peak usage.
• Ensure the security of session management on the Gmail login page.

SQL injection attacks are the most serious security threat to IT solutions. How can you protect your login page from them?

Testing SQL Injection on a Login Page

SQL attacks boil down to entering untrusted data containing SQL code into username and/or password fields. What is the procedure that can help you repel such attacks?

1. Identify username and password input fields.
2. Test them by entering commonplace injection payloads (admin’ #, ‘ OR ‘a’=’a, ‘ OR ‘1’=’1′ –, ‘ AND 1=1 –).
3. Try to insert more advanced UNION-based and time-based blind SQL injections like ‘ UNION SELECT null, username, password FROM users –.
4. Check whether a single or double quote in either field triggers an error.
5. Verify whether database error messages are shown after payloads are submitted.
6. Check whether a SQL injection provides unauthorized access.
7. Verify the system account’s lockout after multiple failed logins.
8. Confirm the system rejects malicious or invalid inputs.

When writing and implementing test cases for Login page, it is vital to follow useful recommendations by experienced QA experts.

The Best Practices for Creating and Implementing Test Cases for Login Page

We offer practical tips that will help you maximize the value of test cases in this domain.

Test cases should be straightforward and descriptive

Test cases should be understandable to the personnel who will carry them out. Simple language, consistent vocabulary, and logical steps are crucial for the test case’s success. Plus, all expectations you have concerning the test case implementation and outcomes should be clearly described in the Preconditions section.

Both positive and negative scenarios should be included

You should verify not only what must happen but also take measures against what mustn’t. By adopting both perspectives, you will boost the system’s reliability manifold.

Security-related test cases should be a priority

The login page is the primary target for cybercriminals, as it grants access to the website’s or app’s content. That is why SQL injection, weak password, and brute-force attempt threats should be included in test cases in the first place. Equally vital are session expiration, token storage, and error message sanitization checks.

Device diversity is mission-critical

A broad range of gadgets, screen sizes, browsers (and their versions), and operating systems is the reality of the current user base. Your Login page test cases should take this variegated technical landscape into account and ensure the page works well for everyone and everything.

Automation reigns supreme

Given the huge number of Login page aspects to be checked and verified, their manual testing is extremely time- and effort-consuming. Consequently, test automation in this niche is non-negotiable. What platforms can become a good crutch in such efforts?

Go-to Tools for Creating Test Cases for Login Page

Each of the tools we recommend has its unique strengths.

Testomat.io

Testomat.io is a fantastic tool for creating and managing test cases, especially for critical pages like login forms. With Testomat, you can quickly set up organized test suites, add detailed test cases for scenarios like valid/invalid credentials, and track results in real time. It streamlines the testing process, making it easier to ensure your login functionality works flawlessly across different conditions.

Appium

This open-source framework is geared toward mobile app (both iOS and Android) testing automation. However, it can also be used for writing test cases for hybrid and web apps. Its major forte is test case creation without modifying the apps’ code.

BrowserStack Test Management

This subscription-based unified platform excels at manual and automated test case creation that can be essentially streamlined and facilitated thanks to intuitive dashboards, quick test case import from other tools, integration with test management solutions (namely Jira), and the leveraging of AI for test case building.

How to Create and Manage Login Page Test Cases Using Testomat.io

Testomat.io is a comprehensive software test automation tool that enables conducting exhaustive checks of all types. To create and manage test for login page with Testomat.io follow this guide:

• To get started, create a dedicated suite for “Login Functionality” or “Authentication.” Then, add test cases for various login scenarios, such as valid credentials, invalid username or password, empty fields, and more.
• For valid credentials, check if the user successfully logs in and is redirected to the home page. For invalid credentials, ensure an error message appears. Test empty fields by verifying that validation messages prompt the user to fill in the necessary fields. If there’s a “Remember Me” option, test it by verifying that the user is automatically logged in or their credentials are pre-filled after reopening the browser.

Lastly, test the “Forgot Password” link to confirm it redirects users to the password reset page. Testomat.io streamlines managing and tracking these scenarios, making your testing process more efficient.

The post How to Write Test Cases for Login Page: A Complete Manual appeared first on testomat.io.

The Playwright Model Context Protocol fits right in with modern AI automation testing and helps development and QA teams write, manage, and execute automated tests more efficiently while improving test coverage and stability.

In our Playwright MCP tutorial, you will discover more details about the Playwright MCP server, reveal how it works, and learn how to set up Playwright MCP and benefit from integration with the test case management system.

What is Model Context Protocol?

Model Context Protocol (MCP) is an open protocol that standardizes how applications provide context to large language models (LLMs), developed by Anthropic. MCP is like a USB-C port for AI applications. Just as USB-C provides a way to connect your devices to various peripherals and accessories, MCP provides a formal two-way connection of how AI models integrate different data sources, services and external tools. Do it without requiring custom integrations for each one.

MCP enables you to build agents and complex workflows on top of LLMs, connecting your models with the world, while addressing challenges such as security, scalability, and efficiency in AI-powered workflows.

MCP Architecture: How It Works

MCP follows a client-server architecture where an MCP host — an AI application which establishes connections to one or more MCP servers. It integrates AI models with external data sources and tools, including Google Drive, databases, APIs, and more. They, in turn, make their data accessible via Model Context Protocol servers. Each MCP client AI application maintains a dedicated connection with its corresponding MCP server. Every request to the server can provide context to LLMs in real-time, allowing them to maintain context even across multiple systems.

Components of MCP

Let’s define an architecture in detail where:

• Hosts – applications the user interacts with – Claude Desktop, an IDE like Cursor, and custom agents.
• Clients – components that are responsible for requesting and consuming external context from compliant servers. BeeAI, Microsoft Copilot Studio, Claude.ai, Windsurf Editor, and Postman are some of the popular examples of Model Context Protocol clients.
• Servers – these external programs can make their tools, resources, and prompts available to an AI model through a standard API (Application Programming Interface) and convert user requests to server actions.
• Local data sources – the computer’s files, databases, and services to which Model Context Protocol servers have secure access.
• Remote services – external systems which can be accessed over the internet (e.g., through APIs) and connected to.

Most developers will likely find the data layer protocol section to be the most useful. It discusses how MCP servers can provide context to an AI application, regardless of where it runs. MCP servers can execute locally or remotely.

How MCP Client Interacts With MCP Server

1. The MCP client, typically embedded in AI applications, creates a request for specific data or actions.
2. The MCP client sends requests to the Model Context Protocol server when the AI model needs to access exposed data, tools, resources, or prompts.
3. The MCP server gets these requests and sends them to the right external program or data source. Then, it handles the processing to make sure that the right data is retrieved.
4. The MCP server gets the results from the external program once it’s finished. It then safely sends the response back to the Model Context Protocol client for consumption by the AI app.

What is MCP Playwright for Automation Testing?

The Playwright MCP meaning refers to the Playwright Multi-Context Protocol. When Model Context Protocol is combined with the Playwright cross-browser testing tool, it provides browser automation capabilities and utilizes Playwright’s locators to let LLMs or AI agents interact with web pages through structured accessibility snapshots instead of screenshots. 

To put it simply, Playwright, which is known as one of the popular JS testing frameworks, acts as an MCP Client and connects to MCP Servers that expose various tools, services, or data. This setup helps QA teams and developers develop smart test scenarios, which are able to react to dynamic and live data, orchestrate more complex manual and automation workflows, and simulate real-world interactions with the system under test while keeping comprehensive and realistic automation.

  Here is an example Playwright MCP example in action:

In e-commerce, the Model-Context-Protocol server could provide a searchProducts(query) function. When Playwright sends a prompt to check how the product search bar on a website works, the MCP server would return relevant product details as if from a live database.

In this situation, Playwright’s test automation script sends a search request, which is the prompt, to the MCP server, and the Model-Context-Protocol server then runs its searchProducts function, retrieves product information, and sends this data back to Playwright, simulating the search results that a user would see in real time. 

Key Features of MCP Playwright

Playwright MCP comes packed with a variety of powerful features that make it a must-have for today’s automation testing. Get to know these features, and you can make the most out of it:

• Modular communication. The Model-Context-Protocol modular architecture of Playwright lets you use a set of tools, such as test runners, data generators, and smart validators.
• Tool interoperability. When connecting Playwright to more than one Model-Context-Protocol server, which offer specialized tools (for example, visual tools, accessibility checkers, or API fuzzers), it lets you create complicated Playwright-based test scenarios without making your code too big.
• Remote execution. Running tests on remote Model-Context-Protocol servers at the same time speeds things up and makes them more scalable.
• Dynamic tool discovery. At runtime, Playwright’s MCP can ask a Model Context Protocol server what tools and services are available so that users can make test suites that can change and adapt.
• Structured communication. Playwright’s Model Context Protocol and servers communicate using a standardized format (typically JSON), so that data and commands are exchanged without fail.

Playwright MCP AI Workflow

A typical workflow can be divided into the following phases, each with specific objectives:

1. Setup and Initialization. Firstly, you need the server to be installed and configured so that it can receive commands and translate them into browser actions. Only by establishing the necessary connection can you prepare the environment for the AI agent or LLM to interact with web browsers.
2. Capability Discovery. At this step, an AI client (e.g., an LLM or an AI agent) queries the Model-Context-Protocol server at runtime to discover what tools and services are available. Whether it is navigating pages, clicking, typing, or taking snapshots, AI needs to understand the full range of actions it can perform on a web page.
3. Command Generation. Guided by a pre-defined testing scenario, the AI model generates specific commands for the Model-Context-Protocol server in JSON. Then, it translates test requirements into concrete instructions for browser automation, explaining what the browser needs to do.
4. Browser Execution. At this step, the MCP server receives the commands provided by the AI and uses Playwright to execute them in a real web browser (Chromium, Firefox, WebKit). It interacts with the web page by performing actions like navigating to URLs, interacting with UI elements, and capturing page states.
5. Contextual Feedback and Iteration. Once a command has been executed, the Model-Context-Protocol server provides rich contextual feedback to the AI (in the form of accessibility tree snapshots of the page). After that, AI analyzes this feedback to refine its next steps, generate further commands, or validate results to reach the desired goal.

Pros and Cons of Playwright MCP

While Playwright MCP presents several benefits, there are also potential drawbacks to consider. Understanding both can help you make informed decisions about using it.

Pros	Cons
It allows AI models to identify and interact with web elements based on their context and accessibility to reduce test flakiness caused by minor UI changes and improve the self-healing capabilities of tests.	You need to have powerful infrastructure to run an AI client and a Playwright Model Context Protocol server, especially with a visible browser or many connections.
Playwright’s MCP AI agents can uncover edge cases and unexpected behaviors that might be missed by static tests.	AI’s inability to accurately interpret the web page context and generate appropriate actions leads to wrong or failed tests.
Since the AI can “see” and understand the page, teams spend less time manually updating element locators when the UI changes.	Managing complex, multi-step tasks or several AI agents through Playwright MCP can be tricky and takes careful design and debugging to make sure all actions are coordinated throughout a long user journey.
AI clients use detailed MCP information (page accessibility or network requests) to build relevant and smarter test flows.	Dev and QA teams must be well-versed in understanding Playwright, test automation, and how to work with AI models and the Model-Context-Protocol protocol.
It uses modules for AI datatype conversions to process complex transformations between formats efficiently and convert spatial data for web visualization, easing integration into test environments.	If AI models can access live browsers directly through MCP servers without any security measures in place, there is a risk of data theft.
It provides a standardized protocol for communication to be integrated with various AI models and platforms to create a more flexible approach to AI automation testing.	Limited support for legacy systems may hinder integration with older web applications, requiring additional adaptation efforts.

Why Teams Need to Use Playwright with MCP

• Smart test generation. Teams can create test cases automatically from the latest data of the application, which is based on Playwright’s MCP ability to utilize Large Language Models (LLMs) and develop more tests to increase test coverage.
• Remote debugging. Software engineers can attach to the same Playwright instance for debugging to identify and resolve issues in real time, without the need to replicate the testing environment.
• Shared testing environments. QA engineers can execute tests on a shared Playwright instance without the need to set up separate environments for each team member to accelerate the process.
• Live monitoring. Dev teams can monitor ongoing tests in real time, get rapid feedback, and resolve bugs quickly during testing sessions.
• Load testing & performance analysis. Teams can measure certain performance metrics, such as page loading speed during high traffic, server responsiveness under load, memory and CPU usage during smoke testing, and optimise accordingly.
• Distributed and parallel testing. Instead of running separate Playwright instances for each test suite, teams can launch multiple browser instances in order to improve overall testing efficiency and reduce test execution time.
• Adaptive testing based on live data. Teams can get more accurate test results and fine-tune tests using A/B testing and real-time user data.
• Self-maintaining test suites. Teams can forget about ongoing script maintenance due to Playwright’s Model-Context-Protocol ability to adapt the test suite to changes in the application and automatically adjust test flows.
• Integration and scalability. Teams can automate test creation and maintenance, speed up test cycles, thanks to Playwright’s MCP integration capabilities with CI/CD pipelines (e.g., GitHub Actions, Jenkins) and tools like Claude Desktop or Cursor IDE.

How to Set Up MCP Server for Playwright

Setting up the Playwright MCP Server requires a few dependencies and configurations to ensure smooth operation. To get started, you need to make sure that certain prerequisites are met:

Prerequisites for MCP Server

• Node.js. As Playwright and Model-Context-Protocol Server rely on Node.js to execute automation scripts, you need to install the long-term support Node.js version 18 or later for stability and verify npm.
• A Compatible Browser Driver. You need to make sure that the appropriate browser engines are installed, because Model-Context-Protocol Server supports Chromium, Firefox, and WebKit. 
• Install the VS Code Insider build  It is important, as the Playwright MCP server requires the GitHub Copilot AI agent and certain other extensions to operate; these full functionalities are available only in the Insider build. The stable VS Code release has not yet rolled out support for them (It is my case, as a Mac user).
• Network Configuration. You need to configure firewall settings and port access to prevent connection issues, because you need to make sure your network lets many clients talk to the MCP.

Explore a couple of expert video guides to assist you through the installation process:

• Playwright MCP Server: Installing and getting started in VS Code
• Manual Testing with Playwright MCP – No Code, Just Prompts!

Installing Playwright and MCP Server

Playwright. You need to install it via npm or yarn to interact with web browsers.

init playwright@latest

Once installed, you need to verify the installation. It can be done by running:

npx playwright --version

Since Model-Context-Protocol Server is an extension of Playwright, it comes built-in with Playwright’s package. However, you need to enable the Playwright MCP Server functionality. So next, proceed with the installation of Playwright MCP Server. You can do it in a few ways:

→ Follow the Playwright GitHub Repo link and trigger the Playwright Server installation:

→ Similarly, on an official Microsoft Visual Studio Extension Page, trigger the Playwright Server:

Run the following command to install the package as a dev dependency:

npm install --save-dev @playwright/mcp@latest

Check the Playwright MCP installation in your IDE Additionally, in the settings, you can see whether the MCP Playwright functionality you need is checked or not everywhere.

Running MCP Server

Once Playwright is installed, you can start the MCP Server using the Playwright CLI. You can configure the launch file and this command separately in the package.json file and run the following command to start the server:

npx @playwright/mcp@latest

This command initializes a Playwright instance that multiple clients can connect to.

It is important to verify that the Model-Context-Protocol Server is running. After launching the server, check the logs to confirm that it’s running successfully. The logs should display connection details, including the WebSocket URL that clients will use to connect.

Connecting Clients to MCP Server

Once the MCP Server is running, multiple clients (such as test scripts, automation or monitoring services) can connect to the shared Playwright session. You can use a basic connection script for it. MCP Server might perform actions on the shared session. You can connect various clients to the MCP Server, leveraging its shared Playwright session for efficient automation or testing workflows.

Practical Understanding

• Shared Session: All clients interact with the same browser instance, so actions (e.g., navigating or clicking) affect all connected clients unless isolated contexts are created.
• Use Cases: This is useful for distributed testing (e.g., running tests across machines), real-time monitoring, or AI-driven automation (e.g., with GitHub Copilot).
• Troubleshooting: If connections fail, verify the server is running, the endpoint is correct, and there are no firewall blocks on the port.

Running tests

Once your server is configured, you can run smart test prompts. You can use your scenario in a .txt file and let MCP read the prompt file, interpret the request, generate relevant Playwright test code, and insert it directly into your project or type the prompt by yourself in the Copilot Agent window.

I asked the AI Agent to generate a Plan for the standard ToDo Playwright Demo Application  and what happened. Codepilot generated and structured 70 test cases. After that, I asked to execute this Test Plan, and the Agent provided me with a command and a proposal to run it.

Ultimately, I got this result by executing my AI-generated test plan, with the Playwright MCP server managing the entire process autonomously based on my prompts. It is a pure Vibe Testing!

Challenges and Solutions in MCP Server Playwright

Below, we are going to explore the biggest challenges you’ll face when working with the server and provide practical solutions to overcome these common issues. These are:

Issue	Possible Cause	How to Fix
MCP Server Not Starting	Playwright not installed or outdated Port already in use	You need to make sure that Playwright is properly installed and up to date. Also, you can check its version and install updates. You need to check if the default port is being used by another application and either stop the conflicting process or change the port to launch the server.
Clients Can’t Connect	MCP server not running Firewall or network blocking WebSocket	You need to verify the Model-Context-Protocol Server Status to make sure that it is running and accepting connections. If you’re running the server on a network with firewalls or restrictive security settings, you need to check the settings to make sure they don’t block the WebSocket connection between the client and the server.
Debugging is Complex	AI logic issue MCP misinterpretation App under test issue	Look at the detailed logs from both the AI client and the MCP server, which include snapshots of the page structure and network activity for each step. Apply Playwright’s built-in debugging tools, like the Trace Viewer, together with the logs created by the AI.

Playwright MCP Best Practices 

To get the most out of Playwright MCP Server, here are some best practices to take into account:

• When you’re running many clients at once, you should think about connection pooling to cut down on extra work by reusing old connections rather than to keep making new ones.
• The Model-Context-Protocol Server can handle many clients, but too many connections at once can overload it, which could cause slowdowns or failures. Knowing that, you should track resource use, like memory and CPU, to stay within your system’s limits, or set appropriate limits if necessary.
• You need to check for possible errors, like connection timeouts, pages that fail to load, or network problems, and fix them to prevent crashes or inconsistent results.
• To stop different tests or clients from clashing, you need to run each set in its own isolated space and use separate browser contexts or tabs to keep tests from interfering with one another.
• As the server can use a lot of your system’s memory and CPU, you should watch these resources while tests run to keep the server smooth. For big testing efforts, it is essential to consider upgrading your hardware or splitting the work across several machines.

Playwright MCP Integration with Test Management 

When integrating Playwright MCP with an AI-powered Test Management System (TMS) like Testomat.io, you can improve test planning, execution, and review, and make your testing efforts smarter and more automated.

• With Testomat.io, you can group and link the tests to the requirements. If tests fail, you can create an issue and fix them. Also, you are able to see the percentage of automated test coverage.
• Testomat.io allows for comprehensive and well-detailed Playwright’s test reports. Artifacts like screenshots, videos, and logs can be automatically uploaded to an S3 bucket and linked to test cases in the Testomat.io dashboard.
• Testomat.io offers direct integration with Playwright’s Trace Viewer, which can be utilized and linked in the run artifacts to examine snapshots and actions.
• When integrating Playwright MCP, you can view the history of automated Playwright’s test runs. However, it is important to mention that you need to set up the correct system configurations to make full use of this option.  

Bottom Line 

The Playwright MCP Server is a strong add-on for Playwright, which makes complex testing easier, as multiple users or scripts can work in the same session, boosting teamwork and saving resources. If you’re debugging remotely, running tests in parallel, or carrying out load testing, MCP Server helps make your automated testing process smoother. 

Contact us if you aim to add Playwright MCP Server to your testing so that your teams can manage tests well, watch progress, and create detailed reports. In addition to that, you can integrate it with a comprehensive testomat.io test case management system, which will guarantee effortless coordination among teams and make your overall testing process more efficient.

The post Playwright MCP: Modern Test Automation from Zero to Hero appeared first on testomat.io.

Automated code review sits at the intersection of those two pressures. Testers now aren’t just validating features, they’re writing automation, reviewing code, and maintaining test suites under constant pressure to move fast. Whether you’re an SDET, AQA, or QA engineer juggling reviews, flaky tests, and legacy cleanups, the challenge is the same: how do you scale quality without burning out?

That’s where automated code review steps in. It doesn’t replace your judgment, it enhances it. By catching repetitive issues, enforcing standards, and removing review noise, it frees you to focus on what matters: writing resilient code and improving test strategy.

What Automated Code Review Really Does

An automated code review tool scans your source code using static code analysis. It checks for potential issues like:

• Security flaws
• Logic bugs
• Duplicate logic
• Poor naming conventions
• Noncompliance with best practices
• Violations of code style guides
• Excessive complexity
• Inefficient patterns

The tool then delivers immediate feedback inside your IDE, on the pull request, or in your CI pipeline depending on how you’ve integrated it.

Automated code review should run early and often — ideally on every commit or pull request. It’s especially useful in fast-paced teams, large codebases, or when enforcing consistent standards. The tools vary: formatters (like Prettier, Black), linters (ESLint, Pylint), AI-powered review bots (like CodeGuru or DeepCode), and analytics dashboards (like SonarQube, CodeClimate). These tools don’t get tired, forget checks, or skip reviews. That consistency compounds over time — leading to cleaner code, faster onboarding, and better collaboration.

Manual vs. Automated Code Review

Code review is essential for maintaining high code quality, but manual and automated approaches differ significantly.

Manual code review has limits. It’s subjective, time-consuming, and highly variable across reviewers. What one engineer flags, another misses. Some focus on code style, others on logic. Many ignore security vulnerabilities entirely, simply due to lack of time or expertise.

This leads to inconsistent code, missed defects, and bloated review processes. It also creates fatigue for both developers and reviewers especially when every pull request involves sifting through boilerplate and formatting issues instead of focusing on actual functionality. The reality: without support, manual reviews break down at scale.

Where Automated Code Review Fits in the Development Process

Automated code review works best when embedded throughout your software development process, not bolted on at the end.

1. Static Code Review. Catch issues as you write code. Tools surface mistakes in real time, while context is fresh and changes are easy.
2. Stage of Compelling (GitHub, GitLab, Bitbucket). Trigger scans automatically during review requests. Flag violations before merging into main, reducing cycle time and improving team trust.
3. Deployment Stage (Jenkins, Azure, CircleCI). Use quality gates to block builds that don’t meet defined thresholds — like code coverage, complexity, or security risk. In your dashboard. Track trends, monitor repositories, and highlight vulnerabilities. Dashboards give engineering leads visibility into team-wide habits and technical debt.

This end-to-end presence ensures new code meets expectations before it becomes tech debt.

Benefits of Automated Code Review

The value of automated code review is measurable, not theoretical. It shows up in your delivery metrics, onboarding speed, security posture, and team morale.

1. Cleaner Code, Faster

By offloading repetitive tasks like checking indentation, naming, or unused variables reviewers can focus on logic, design, and architectural decisions. The result? Fewer comments per PR, faster turnaround, and better conversations.

2. Fewer Production Defects

Catch problems when they’re still cheap to fix before they make it into production. Static code analysis surfaces potential issues that manual reviews may overlook, especially in large or unfamiliar codebases. Automated code reviews can use static analysis tools or custom rules to:

• Detect use of Thread.sleep() or timing-based waits.
• Flag tests that rely on non-deterministic behavior (e.g., random input, current system time).
• Catch poor synchronization or race conditions in test code.
• Warn against shared state between tests (e.g., using static variables improperly).

3. Consistent Standards

With automation, every line of new code gets the same scrutiny, regardless of who writes it. No more “it depends on who reviewed it.” You enforce coding standards and best practices as part of the pipeline.

4. Stronger Security

The best tools scan for vulnerabilities like SQL injection, cross-site scripting, and insecure API use. They also catch dangerous patterns like hardcoded credentials or risky file access. This shifts security left, where it belongs.

5. Better Onboarding

New team members don’t have to learn your standards by trial and error. The code review tool enforces them automatically, speeding up onboarding and reducing friction between juniors and seniors.

6. Developer Confidence

Clear, consistent feedback builds confidence. Programmers know what’s expected. They spend less time guessing and more time solving real problems.

Where Automated Code Review Fits in the Development Process

Automated code review integrates directly into your CI/CD pipeline — typically right after a commit is pushed or a pull request is opened. It acts as an early filter before human review, catching common issues, enforcing style, and flagging risks.

Key touchpoints:

• Pre-commit: Formatters & linters clean up code instantly
• Pre-push / CI: AI review bots and coverage checks kick in
• PR stage: Dashboards summarize issues, risks, and quality trends
• Post-merge: Analytics track long-term code health across the repo

It works quietly in the background, guiding developers and testers without slowing them down. By the time code reaches human review, the basics are already covered — so people can focus on logic, architecture, and value.

The Trade-Offs of Automated Code Review You Need to Know

Automated review isn’t perfect. But its flaws are solvable and far outweighed by its advantages.

Problem	Why it’s a Problem	How to Fix It
False Results	Bad configuration overwhelms devs with irrelevant alerts.	Customize rule sets to your needs. Tune thresholds. Suppress noisy checks. Focus reviews on new code.
Overdependence	Automation catches syntax and known bugs — not intent or business logic.	Keep human reviewers in the loop. Automation assists, but judgment still requires a person.
Adoption	Tools that slow pull requests or create noise get ignored.	Prioritize ease of use. Integrate tightly into workflows. The dev team adopt what helps them.

Best Practices for Automated Code Review

Automated code review, when done right, reinforces engineering values: clarity, safety, consistency, and speed. When done wrong, it breeds friction, false confidence, and disengagement in development teams.

These best practices are here to help build an automated review process that earns trust, scales with your team, and quietly enforces quality without disrupting momentum.

1. Start with Precision, Not Coverage

The biggest mistake teams make is turning on too much too fast. Every alert costs attention. A single false positive can train developers to ignore all feedback, even the valid kind. So before you aim for 100% rule coverage, aim for signal over noise. Start with a focused rule set:

• Common style or lint violations your team already agrees on
• Fatal or undefined code behavior that must be controlled first
• Security vulnerabilities

Then layer in more checks gradually, based on real-world feedback. Start with the guardrails teams want, not the ones you think they need. Choose a responsible person for code review. It might be a guru, an Architect of a product who described the architecture of how our product should be implemented. Or a group of experienced, well-educated developers. Establish a process, when they should do it? During Code Review Meeting, or in pair programming.

2. Customize Everything You Can

No off-the-shelf configuration fits your team perfectly. Automated review tools come with rules designed for everyone, which means they work best for no one in particular. Customize:

• Rulesets to match your coding standards, risk tolerance, and language use
• Severity levels (e.g. error vs. warning)
• Ignored paths or files (e.g. auto-generated code, legacy blobs)
• Thresholds (e.g. cyclomatic complexity, line length, duplication ratio)

The more the tooling reflects your codebase and your values, the more it will be trusted. If developers feel like they’re arguing with a machine, you’ve already lost.

3. Don’t Review the Past, Focus on What’s Changing

Flagging issues in legacy code is often pointless. You’ll either:

• Force devs to “fix” old code just to pass CI
• Or encourage them to ignore the tool entirely

Instead, narrow automated review to new and modified code only. This keeps feedback contextual and encourages continuous improvement without opening the door to massive refactoring or alert fatigue.

4. Integrate Feedback Where Development Lives

Automated review should meet developers in their flow, not pull them out of it. That means:

• Running in pull requests (e.g. GitHub/GitLab/Bitbucket comments)
• Surfacing feedback in CI pipelines, not a separate dashboard
• Avoiding annoying email reports or obscure web UIs

5. Be Deliberate About What Blocks Merges

Not all issues are created equal. If your automated system fails builds for minor style inconsistencies or low-risk warnings, developers will start gaming the system or switching it off. Use blocking only for:

• Critical security issues
• Build-breaking bugs
• License violations or known malicious dependencies

Everything else should be advisory: surfaced, but non-blocking. Let humans decide when it’s safe to proceed.

6. Treat Automation as an Assistant, Not an Authority

Automated tools are fast, consistent, and tireless, but they lack context. They can’t understand your product, your priorities, or your reasoning. That’s why code review still needs humans:

• To assess trade-offs
• To weigh design decisions
• To ask questions tools never will

7. Explain the Why Behind Every Rule

Tools often tell you what’s wrong, but not why it matters. When developers don’t understand the reasoning behind a check, they’ll treat it like red tape. That’s where documentation and context come in. Connect every rule to:

• A real-world risk (e.g. “This style prevents accidental type coercion”)
• A team standard
• A known bug pattern from your history

Better yet, invite feedback. QAs are more likely to respect rules they’ve had a say in shaping.

Tips to Choose the Right Tool: What Actually Matters

Plenty of tools claim to “automate review,” but real value comes from depth, adaptability, and ease of use.

Tools for Automated Code Review

• Lint + Prettier: Essential for different programming languages and projects; handles code style cleanly and predictably.
• Codacy: Lightweight, flexible, solid JavaScript support, easy GitHub integration.
• DeepSource: Clean UI, smart autofixes, focused on Python and Go, ReviewDog, Husky.
• Testomat.io: A test management system that helps teams manage both automated and manual tests. It can integrate with popular testing frameworks and CI/CD pipelines, and become an essential component of automated code review

These tools work well across modern version control systems, offer rich configuration, and support most mainstream programming languages.

Automation + Human Review = Scalable Quality

The goal of automated code review isn’t to eliminate humans. It’s to elevate them. By automating the mechanical checks, you give your team time and space to focus on higher-order thinking: design, performance, scalability, and real collaboration. Done right, it becomes part of your software development process, not an obstacle to it.

Your delivery process enforces quality automatically. Your pull requests become cleaner. Your reviewers become more strategic. And your development teams ship faster, with fewer bugs and tighter security. That’s a tested process.

Automated code review doesn’t fix everything. But it fixes enough to change how you build. Start small. Choose a tool that fits your stack. Configure it to your standards. Run it on real code changes. Measure impact. Refine. The teams who do this don’t just move faster, they improve continuously. And today that’s the real competitive edge.

The post Automated Code Review: How Smart Teams Scale Code Quality appeared first on testomat.io.

Based on a recent report, 76% of managers and employees noted that the lack of effective collaboration and clear communication largely contributes to workplace failure. This means that BDD is crucial for various organizations in terms of its capability to guarantee that every member of the team is on the same page and has a clear understanding of the desired software behavior. Let’s find out how the BDD framework can transform the way teams build and test today’s modern software products

What is BDD Framework?

Behavior-driven development (BDD) is a software development methodology, which has a focus on collaborative work between techies and non-techies – developers, testers, and stakeholders throughout the project’s lifecycle. With simple and natural language, teams design apps around a behavior a user expects to utilize. They write descriptions in  Given When Then  format using the user stories before any code is written to be the basis for automated test scenarios.

This BDD approach assists developers and business stakeholders in establishing a clear and common product understanding. The idea is in structuring business requirements and turning them into acceptance tests. Using tests written in plain English, all stakeholders understand and agree on the software’s expected behavior, and make sure that they develop the right software product. In BDD, teams use Gherkin language to write the script in simple words like  Given,  When , and Then . With those words, they describe the behavior of the software.

For example, Gherkin BDD framework scenario:

Feature: Product Search

  Scenario: Display search results when a user searches for a product
    Given a user is on the website
    When they perform a product search
    Then they should see search results

This test script is then turned into automated tests that check if the software behaves as expected and how it is described.

Key principles of BDD Test Framework

• Collaboration. The scenarios are written in a way that all team members – developers, testers, and key stakeholders are in the know how the system should behave regardless of their technical expertise.
• Focus on Behavior. The focus is on the users who are interacting with the product instead of how the software should be built technically.
• Common Language. Simple shared language is used across the business and technical teams so that anyone can understand business requirements and technical implementation.
• Living Documentation. BDD scenarios function as a living documentation. Since these scenarios are automated tests, they provide an up-to-date record of how the system behaves.
• Test Automation. Automating the scenarios allows teams to validate the application behavior once code changes are made. This helps catch regressions early and ensures the system behaves as expected over time.

BDD Framework Life Cycle

BDD life cycle typically includes a series of steps, which make certain that stakeholder communication and the direction of business goals or objectives are unified. Below you can find the key stages:

1. Discover. At this stage, teams collaborate with stakeholders to gain a comprehensive understanding of the project’s scope, objectives, and requirements and establish a roadmap for the project’s execution.
2. Write Scenarios in Gherkin. Teams write scenarios in Given-When-Then format to describe the product’s behavior from the users’ perspectives. These scenarios make it easier for the development teams to understand the requirements and for the QA teams to test them properly.
3. Automate Scenarios. Once scenarios are written, teams convert these plain language scenarios into automated tests using BDD test automation frameworks and tools. These tools parse the Gherkin syntax and map it to test code that interacts with the application.
4. Test. These automated tests are executed frequently to make sure that the system behavior matches the desired behavior after new code is added or existing code is modified.
5. Refactor. Teams improve existing code while maintaining behavior without changing the product’s functionality.
6. Refine and Iterate. Teams update and refine the scenarios to reflect new requirements or changes in the system’s behavior. This creates a feedback loop where the behavior is constantly validated and documented.

Why Use Playwright Java bdd automation framework?

Behavior Driven Development (BDD) with Playwright Java allows you to write tests in a more natural language, which simplifies the process of s understanding and maintaining code quality. Playwright is known as a powerful automation library which enables reliable end-to-end testing across key browser platforms (Chrome/Edge, Firefox, Safari).

Aiming to create robust, understandable, and maintainable automated tests which align with the intended functionality of your application, you can combine BDD principles with Playwright’s automation capabilities in a Java environment.

This approach will work for teams that include non-developers, such as product managers or QA engineers, who need to understand the test cases.

Playwright Cucumber Java Framework: Steps to Follow

So, let’s get started to automate! The typical technology stack for modern Java BDD projects is Java + Playwright + Cucumber together; it is a popular and well-supported choice. Here’s what each part does

• Cucumber – handles BDD-style .feature files with Gherkin syntax Given When Then
• Playwright for Java – performs the actual browser automation (clicks, navigation, input, etc.)

Java test automation framework stack includes:

• Maven – the build automation and dependency management tool for Java, is a project heartbeat.
• JUnit (usually JUnit5) – test runner; executes the Cucumber tests.

How to set up our BDD Framework?

Initially, ensure that the Java programming language development environment is installed. It is JDK 17 or higher, and Maven 3.9.3+, of course.

java -version
mvn -v

Node.js (Playwright dependency)

node -v
npm -v

If something is not installed, follow the official links to get started: Java, Playwright, Maven

So, my IDE is VS Code, and I have to install the official Microsoft Extension for Java:

By clicking the button Install you download the set of plugins, allowing you to code in Java with the Visual Studio Code editor freely now.

#1 Step: Create & configure your BDD framework project

There are two options to create a Maven project in VSCode: using the IDE UI by choosing Maven in the New Project wizard or, as in my case, through the CMD command:

mvn archetype:generate -DgroupId=com.example.demo \
-DartifactId=Demo-Java-BDD-framework \
-DarchetypeArtifactId=maven-archetype-quickstart \
-DinteractiveMode=false

Little explanation for parameters of this command:

• -DgroupId: package name base (like com.yourcompany)
• -DartifactId: Folder/project name
• -DarchetypeArtifactId: Type of project scaffold (quickstart)
• -DinteractiveMode=false: Prevents Maven from asking prompts

Once the project is created, in the editor, you will see an auto-generated basic pom.xml and project structure:

Pay attention to the Maven build notification in the bottom right-hand corner. You should agree every time after savings in the BDD framework project, anyway, to do it manually with the command:

mvn clean install

#2 Step: Configure Dependencies

The following action is adding dependencies via Maven in the pom.xml file:

• playwright
• cucumber-java
• cucumber-junit

Check the Playwright dependencies of the new version you can on the Playwright Java page

To avoid errors, you can alternatively install Playwright Java at one time:

mvn exec:java -e -Dexec.mainClass=com.microsoft.playwright.CLI -Dexec.args="install"

Similarly, you can find the required dependencies on the official Cucumber documentation page at the following link and JUnit(usually JUnit5) Dependency Page information. These configurations enable automation of step definitions and browser interactions.

Eventually, this is a minimal BDD framework example of dependencies for Playwright, Cucumber and JUnit:

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
    <modelVersion>4.0.0</modelVersion>

    <groupId>com.saucedemo</groupId>
    <artifactId>playwright-tests</artifactId>
    <version>1.0-SNAPSHOT</version>

    <properties>
        <maven.compiler.source>23</maven.compiler.source>
        <maven.compiler.target>23</maven.compiler.target>
        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
    </properties>

    <dependencies>
        <dependency>
            <groupId>com.microsoft.playwright</groupId>
            <artifactId>playwright</artifactId>
            <version>1.52.0</version>
        </dependency>

          <dependency>
            <groupId>io.cucumber</groupId>
            <artifactId>cucumber-java</artifactId>
            <version>7.23.0</version>
        </dependency>

       <dependency>
            <groupId>io.cucumber</groupId>
            <artifactId>cucumber-junit</artifactId>
            <version>7.23.0</version>
            <scope>test</scope>
        </dependency>
    </dependencies>

  </project>

After saving pom.xml, compile the Maven build again

#3 Step: Create a Cucumber Runner

Create a TestRunner.java class file. The TestRunner.java class is like the engine that wires everything together. It tells Cucumber how to find and run .feature files.

Example TestRunner Java:

package runner;

import org.junit.runner.RunWith;
import io.cucumber.junit.Cucumber;
import io.cucumber.junit.CucumberOptions;

@RunWith(Cucumber.class)
@CucumberOptions(
    features = "src/test/resources/features",
    glue = "steps",
    plugin = {"pretty", "html:target/cucumber-report.html"},
    monochrome = true
)
public class TestRunner {
}

The BDD framework structure of the project, as you can see in this picture, keeps logic separate and testable:

src
└── test
    ├── java
    │   ├── runners
    │   │   └── TestRunner.java
    │   └── steps
    │       └── LoginSteps.java
    └── resources
        └── features
            └── Login.feature

#4 Step: Write feature files with scenarios in Gherkin

Create  .feature files, look at the top of the code path where it is placed

Feature: Login to Sauce Demo

  Scenario: Successful login with valid credentials
    Given I open the login page
    When I enter username "standard_user" and password "secret_sauce"
    And I click the login button
    Then I should see the products page

#5:Step Map steps in Java using Cucumber step definitions

package steps;

import com.microsoft.playwright.*;
import io.cucumber.java.After;
import io.cucumber.java.Before;
import io.cucumber.java.en.*;

import static org.junit.Assert.assertTrue;

public class LoginSteps {
    Playwright playwright; // Variable playwright type of Playwright object
    Browser browser; // Represents a specific browser instance (e.g.Chromium)
    Page page; // Represents a single tab or page within the browser.

    @Before //Hook - runs before each scenario
    public void setUp() {
        playwright = Playwright.create(); //Initializes Playwright engine
        browser = playwright.chromium().launch(new BrowserType.LaunchOptions().setHeadless(false)); //Launches a visible Chromium Browser
        page = browser.newPage(); //opens a new Browser Tab
    }

    @Given("I open the login page")
    public void openLoginPage() {
        page.navigate("https://www.saucedemo.com/"); //Navigates to Log In page
    }

    @When("I enter username {string} and password {string}")
    public void enterCredentials(String username, String password) {
        page.fill("#user-name", username); //Fills in username
        page.fill("#password", password); //Fills password
    }

    @When("I click the login button")
    public void clickLogin() {
        page.click("#login-button"); //Clicks login button
    }

    @Then("I should see the products page")
    public void verifyProductsPage() {
        assertTrue(page.isVisible(".inventory_list")); //Checks of inventory list is visible
    }

    @After
    public void tearDown() {
        browser.close(); //closes browser page
        playwright.close(); // shuts down playwright engine
    }
}

#6:Step Run tests via JUnit5 runner

mvn clean test

Output

Opens browser using Playwright, navigates to login page, completes the login, verifies the dashboard is displayed and generates a basic Cucumber HTML report. Thus,

– Where can we find this BDD framework’ Cucumber HTML report?

Enter into the folder target, scroll down and launch Cucumber HTML report. It is automatically generated when Cucumber tests run with the proper configuration, namely:

@CucumberOptions(
    plugin = {"pretty", "html:target/cucumber-report.html"}
)

The Cucumber HTML Report is a simple and quite user-friendly visual representation test results of your BDD (Behavior-Driven Development) framework. It shows: Feature and Scenario breakdown, their steps in detail and result, Pass/Fail status, Error messages and stack traces, execution time. In total, it is not very informative, but it is not too bad either.

What is Playwright Test Report?

Generally, a Playwright test reports works as an extensive summary compiled after running a set of automated tests using the Playwright testing framework and indicates which scenarios passed, failed, or skipped.

With detailed reports, developers and test engineers can quickly identify the root cause of test failures and debug issues in the application code or the test automation itself. They can analyze reports to highlight areas of the application’s behavior that are not yet adequately covered by automated tests and create more scenarios.

The Testomatio Playwright Test Report Key Components

If a simple, basic Playwright or Cucumber HTML report is not enough, our solution is the perfect fit. The test management system testomat.io offers powerful Reporting and Analytics across different testing types.

In this test reporting, you can find the following information:

• Manual testing, as well as automation testing, in one place.
• Customizable test plans, selective test case execution. Easy to share it with stakeholders.
• Information on test status –  which tests have been passed, failed, or skipped.
• Descriptions of any errors, mentioning the type of error and the location.
• How long the test runs in order to identify slow tests and areas which cause performance delays.
• Information about test coverage.
• Screenshots or video recordings of the test execution to better understand the test results.
• Full test run history and clear progress tracking.
• Detailed logs that can help developers debug issues and offer visibility into browser actions, network requests, and responses.
• Moreover, actionable analytics with a wide range of metrics and insights to support decision-making.

Start by adding the Java JUnit XML plugin to the pom.xml file:

  <build>
    <plugins>
      <plugin>
        <groupId>org.apache.maven.plugins</groupId>
        <artifactId>maven-surefire-plugin</artifactId>
        <version>3.2.5</version> <!-- or the latest -->
      </plugin>
    </plugins>
  </build>

Sign up or log in to the TMS, then create your test project by following the system’s guidance, and then import BDD tests. Only follow the smart tips the test management UI offers.

This is the result of syncronization of manual and auto tests in the common repository.

In addition, testomat.io offers a unique feature that allows automatic converting of classical manual test cases into BDD (Behavior-Driven Development) format and importing detected steps into the Reusable Steps Database. Especially, this capability is useful for teams transitioning from traditional manual QA workflows to modern, executable BDD-style automation.

It seems this report offers a more polished presentation than the standard Cucumber report, doesn’t it?

Advantages of BDD Playwright Java framework

• Step Definition Mapping. Teams can use Given/When/Then annotations with accurate regular expressions to link Gherkin steps to Java methods.
• Playwright API Interaction. Teams can apply Playwright’s Page, Locator, and browser management APIs within step definitions in order to automate browser actions and assertions.
• Test Reporting. Teams can correctly specify the paths to Feature Files (features) and Step Definition packages (glue), which contain your automation code to set up how test results are reported and provide meaningful feedback once tests are executed.
• Parameter Passing in Steps. Teams can use capture groups in regular expressions within step annotations to pass data from Gherkin scenarios to Java methods,  because it allows them to write more reusable step definitions that can handle various data inputs from the Feature Files, and cut down code duplication.
• Assertions. With assertion methods within step definitions, teams can verify that the actual application’s behavior matches the expected outcomes defined in the Gherkin scenarios, which makes the tests reliable and verifies the software works as designed
• Selector Strategies.  With Playwright’s selector types (CSS, XPath, text-based) and reliable web elements identification, teams can automate code to accurately target and interact with specific UI elements, even after changes in the application’s structure or styling.
• Handling Asynchronous Operations.  Understanding that Playwright’s API is asynchronous and ensuring proper handling, teams can prevent their automation code from prematurely proceeding before UI elements are fully loaded or actions are completed, which contributes to more reliable and less flaky tests that accurately reflect user interactions.
• Integration with CI\CD. Teams can configure the build process to execute tests and generate reports in a continuous integration environment.

Interesting to read:

• Cucumber BDD framework for API testing with Playwright example
• Behavior-Driven Development (BDD): Meaning, Benefits & Best Practices
• Integration between BDD in Jira and Test Management System

Disadvantages of BDD test framework with Playwright Java

• Steeper Learning Curve & Complex Project Setup. If teams are new to both BDD principles and Playwright Java, it requires significant effort from them to set up the necessary dependencies.
• Complex Project Setup. Setting up the necessary dependencies (Cucumber, Playwright, Test Runner, reporting libraries) in a Java project can be more involved than setting up simpler testing frameworks.
• Too UI-centric Scenarios. Teams might fall into the trap of writing excessively detailed Gherkin scenarios that become difficult to maintain and understand. Scenarios should focus on business value, not low-level UI interactions.
• Not a Replacement for All Testing. While BDD with Playwright Java focuses on end-to-end or integration software testing from a user’s perspective, it doesn’t serve the purpose of unit tests or API tests.
• Slower Performance. Running end-to-end tests driven by Cucumber and Playwright can be slower than unit or integration tests. While Playwright is generally fast, the overhead of interpreting Gherkin and orchestrating browser actions can add to execution time, especially for large test suites.
• Maintenance Challenges. Playwright tests are susceptible to changes in the application’s user interface, meaning any minor UI modifications can break a significant number of scenarios and need frequent updates to reflect changes in UI elements, workflows, or data.
• Synchronization Issues. Web applications can be asynchronous, and handling synchronization (waiting for elements to load, animations to complete) in Playwright Step Definitions requires careful implementation to avoid flaky tests.
• Cooperation Problems. If business stakeholders are not involved in writing and reviewing feature files, the scenarios might not accurately reflect business needs.

Bottom Line: Ready To Develop The Right Product The Right Way with BDD Playwright Java?

When it comes to incorporating the Behavior-Driven Development (BDD) testing framework, organizations need to remember that it is not just a methodology; it’s a mindset. Furthermore, its adoption becomes a crucial strategy for organizations which require revolutionizing how they approach the software development process.

With BDD practice in place, you can improve communication, catch bugs early, enhance documentation, and increase test coverage. Contact our specialists if you aim to navigate software development complexities and need a working approach like BDD to develop features, which are well-understood by both technical and business stakeholders. Only by utilizing the correct BDD tools and frameworks can you get BDD’s highest potential to achieve success in your projects.

The post Playwright Java BDD Framework Tutorial appeared first on testomat.io.

In the article below, you can find information about the importance of test automation reports, reveal various types of reports, and learn how to choose the most suitable ones. Also, you can discover what tips to follow to succeed in Playwright reporting, as we consider it the most popular testing framework today.

What is Playwright?

Developed by Microsoft, Playwright is an open-source framework which is used for browser automation and testing web applications. Thanks to its ability to test Chromium, Firefox, and WebKit with a single API, teams can apply it as an all-in-one solution when conducting real-time functional, API and performance testing. Also, teams can carry out end-to-end testing by simulating user interactions such as clicking, filling out forms, and navigation.

Explore more here:

Playwright API Testing: Detailed guide with examples

Being compatible with Windows, Linux, and macOS, the Playwright tool can be integrated with major CI\CD servers such as Jenkins, CircleCI, Azure Pipeline, TravisCI, GitHub Actions, etc.

In addition to that, Playwright has broad language compatibility – supports TypeScript, JavaScript, Python, .NET, and Java – to provide QAs with more options for writing tests. In total, there is a list of key Playwright’s features:

• Cross-browser support – Chrome, Firefox, WebKit.
• Automatic waiting for elements to be ready.
• Parallel execution of tests to deliver high performance.
• Mobile device emulation and geolocation simulation.
• Easy integration with CI\CD tools.

Find more information about Playwright’s capabilities for automation testing:

Playwright Test Automation: Key Benefits and Features

What is a Test Report in Playwright?

A Playwright test report is a detailed document, which is generated after running a set of automated tests using the Playwright testing framework. It displays test results to reveal which tests were passed, failed, and skipped, and helps to uncover how well the application functions or performs.

In the Playwright test report, you can find the following components:

• Status of Tests. This component shows information about passed/failed/flaky/skipped tests.
• Error Details. This component outlines the types of errors (for example, assertion failed, timeout, network errors) and their positions within the application.
• Execution Time. Here you can discover how much time it took to run each test to uncover slow tests and performance issues.
• Screenshots. For a failed test, Playwright will automatically take a screenshot at the point of failure and provide crucial visual context.
• Videos. Playwright can record a video of the entire test execution for failed or all tests, providing dynamic information and showing what has led to a full-scale failure.
• Logs & Debug Information. Detailed logs that can help developers debug issues by providing insights into browser actions, network requests, and responses.
• Test Coverage. This component is valuable because it provides visibility into the number of tests within the coverage scope.

Indeed, Playwright reports have been designed to be interactive – with options of expanding/collapsing sections, filtering tests, and navigating through detailed failure information such as stack traces, screenshots, and videos with ease; and giving QA and dev teams an important understanding of the test’s performance in context.

Why teams need Test Automation Reports

• They see visual representations of the results of tests and can prioritize bug fixes and enhancements depending on how they affect the user experience.
• Teams are in the know about the full picture of how all the tests have been executed: they see the number of passed, failed, or skipped tests to understand how good and stable the application is.
• Thanks to reporting options, teams can get clear details of what went wrong to find and fix the main problem quickly.
• Teams can see how much of the app is being tested and which parts still need testing.
• Teams should not check the results of all tests manually to identify common problem areas in the app.
• With regular and detailed test reports, teams can monitor how well the app is doing in different tests to decide how to make their test automation better.

Different Types of Test Reporters in Playwright

When you run Playwright tests without specifying a reporter, it uses the list reporter by default. For more control, it is good practice. Specify your preferred reporter you can in the file playwright.config By default, the HTML reporter is applied.

Additionally, the easiest way to build reporters is to pass the --reporter flag with the command line. Example Playwright HTML reperter:

npx playwright test --reporter=line

Find your test result reports you can in the root folder result-reports or other if you set it.

So, let’s review the Playwright reporting types and reporter methods you can utilize to meet your testing needs.

Built-In Playwright Reporters

List Reporter

Playwright’s List Reporter provides a compact and text-based summary of the tests run. For every test that encounters a problem, it delivers the error message right near it and a call stack – this helps in figuring out what has gone wrong. While it doesn’t offer interactive features like the HTML report, its simplicity makes it an excellent choice for rapid debugging during development.

Furthermore, the List Report offers valuable information on test execution status, but without the need for a browser or complex UI. This reporter is useful for CI\CD pipelines where a simple, sequential output is preferred for logging and immediate feedback.

Line Reporter

Being a highly compact, line reporter uses a single line to display test execution results and dynamically update it as tests complete.

Line reporter is useful for large test suites, where it shows the progress but does not spam the output by listing all the tests.

It is important to mention: Line Reporter only outputs detailed information, such as error messages and stack traces, specifically when a test fails to make it very useful for developers who need quick feedback during local development or in CI environments where log details should be controlled. Overall, it prioritizes a clean console while still delivering immediate alerts for any issues.

Dot Reporter

When you run your tests in the console, Playwright’s Dot Reporter provides a highly visual representation. It uses a single dot (.) for every test that passes, so you can instantly see how things stand at any time as tests are run. If a test fails, it usually emits an ‘F’ (‘Failure’) or similar character as a warning.

Dot reporter is a good fit if you need to quickly measure overall test suite results without detailed output, making it just right to use on large projects or in the CI\CD dashboards. Its main advantage is that it offers a real-time and intuitive visual progress for your test suite.

HTML Reporter

HTML Reporter is an invaluable tool, which is used by teams to visualize test results in an intuitive and interactive web interface. After a test run, it generates a comprehensive HTML file which can be opened directly in a web browser. In our cases index.html file:

After we open the HTML file, we can see such a report in visualization Passed, Skipped or Failed tests:

The playwright HTML report gives a detailed overview of all tests, clearly displays which areas of the application were tested, and highlights the status of each test and its coverage.

For any failures, it offers detailed accounts of failures, notes error types and locations, supplemented by screenshots, videos, and powerful trace files.

What is Playwright Trace Viewer?

Playwright can record a trace of your test execution—essentially a detailed log that includes:

• Screenshots and DOM snapshots
• Network requests/responses
• Console logs
• Actions performed (clicks, inputs, navigations, etc.)

The Trace Viewer then lets you open these trace files in a visual UI for step-by-step playback. In Trace Viewer you can easily understand what exactly went wrong —maybe a timing issue, a missing element, or a slow response.

JUnit Reporter

Playwright’s JUnit Report is built to output test results in the standardized JUnit XML format that is crucial for Continuous Integration/Continuous Delivery (CI\CD) systems.

JUnit reporter produces a JUnit-style xml report.

Most likely you want to write the report to an xml file. You can see it on our screenshot in the down left corner.  When running with --reporter=junit use the environment variable:

PLAYWRIGHT_JUNIT_OUTPUT_NAME=results.xml npx playwright test --reporter=junit

In configuration file, pass options directly:

import { defineConfig } from '@playwright/test';

export default defineConfig({
  reporter: [['json', { outputFile: 'results.json' }]],
});

The generated XML file includes all the information about the test suite and cases – names, durations, and results. For failed tests, it provides essential details like error messages and stack traces, enabling automated parsing by CI tools. Its biggest advantage is that it can be used in any CI pipeline, with build servers readily able to understand the results of tests and processes controlling deployments. Although it does not provide the rich interaction of the HTML Reporter, its machine-readable format is essential to make automatic quality gates and continuous feedback work. You can download the XML JUnit Report file and upload it to various analytics tools to view data in a more refined presentation.

Multiple Reports in the Configuration File

With Playwright, you’re not restricted to a single report format, so you can meet a variety of requirements. Thanks to this adaptability in reporting, you can assign multiple reporters at once to the configuration file and define them on the console terminal. In the configuration file write:

  reporter: [
    ['html'],
    ['json', {  outputFile: 'test-results.json' }],
    ['junit', { outputFile: 'results.xml' }]
  ],

For instance, you can create a HTML report and a JSON report to receive a JSON file along with the results once you specify it in the command line or configuration file.

Custom Report

With Playwright Custom Reporter, you can tailor test result output based on the project’s unique needs. You can develop your custom reporter using JavaScript/TypeScript to transform raw test data into any format or integrate Playwright tests into existing workflows or proprietary systems that don’t support standard report formats. A custom reporter allows you to filter, aggregate, or visualize data and give a view of the results of tests, which can be reviewed by all relevant stakeholders.

For using the Custom reporter, you need to study more about the Reporter API and update the Playwright configuration file by writing its data there:

import { defineConfig } from '@playwright/test';

export default defineConfig({
  reporter: [['./my-awesome-reporter.ts', { customOption: 'some value' }]],
});

Third-Party Reporters in Playwright

Playwright allows you to integrate third-party reporters to extend its built-in reporting capabilities more extended. Thanks to these external tools Allure, Monocart, Tesults, ReportPortal, Currents, and Serenity/JS, teams can improve the reporting process by adding the following features – detailed HTML reports, real-time monitoring, and interactive dashboards; they also help teams in viewing test results and visualising them in different formats and simplify the monitoring of test performance, failures, and trends.

Max Schmitt, Open Source enthusiast, Playwright full-stack web developer, gathered all such third-party solutions for Playwright in a single GitHub repo,  Awesome Playwright.

In this repo, we are also represented

Playwright’s integration with testomat.io enables teams to see a live status before the test run has finished execution. And, a full report link will also be created to share among all parties involved as necessary.

If something fails, the execution trace, test case, and attachments can be analysed to find out what went wrong.

These reports are good for analyzing whether build compile, automated test execution, or deployment steps passed or failed.

Detect Playwright flakiness you can in a 2 way as you can see with the Analytics Dashboard widget and the AI Testing Agent. Flakiness detection helps ensure Playwright tests in the framework are dependable enough to be run automatically and frequently.

With generated reports for CI\CD pipelines, teams can create quality and deployment readiness reports automatically from their continuous integration and delivery processes. It can be achieved through the integration with the testomat.io tool.

How to Choose the Right Type of Playwright Reporter

Before selecting the type of reports, it is essential to define the needs of your team, your project scale, and the level of detail you require, and then adapt Playwright reporting to those needs. Here are a few considerations to make when deciding which type of Playwright reporting you need:

• Purpose of the Report. Your report should be driven by the main testing goals and determine the need for either quick developer feedback or comprehensive stakeholder updates.
• Size of the Test Suite. If you are testing small, a short console reporter (List or Dot Reporter) could be enough for fast feedback. But when the test suite reaches hundreds or thousands of tests, deeper reports like the HTML Reporter or specialized dashboards are invaluable to effectively explore and interpret the results.
• Environment. The testing environment heavily influences reporter choice: for local development, an interactive HTML report is ideal for immediate debugging, while for CI\CD pipelines are a good fit thirty-part Playwright reporting solution for automated parsing and quality gate integration.
• Level of Detail. The depth of understanding the results of tests is important. For detailed debugging and root cause analysis, the HTML Reporter (and it’s Traces, Screenshots, and Videos) provides every detail of any failure, up to the kind of failure, and the place in the application where it failed. If the level of detail is minimal, you can select the Line or Dot Reporter to get at-a-glance feedback.
• Data Storage Needs. If you require the historical analysis, there are reporters that generate HTML and JUnit XML files for further review. For long-term trend analysis or integration with test management systems, you can select a reporter which will sends data to an external database or service, often through a Custom Reporter. But our test management software testomat.io also support this option.
• Customization Options. If one of the regular reporters simply isn’t generating the data exactly like you need, the way the data’s aggregated, or if the data needs to be submitted to an external system, the Custom Reporter is a good option for matching the specific reporting workflows.
• Test Management Systems (TMS) Integration.  Some reports (JUnit XML, for instance) can be readily integrated with a variety of TMS to collect data in one place. So, if you need real-time monitoring of test runs, failures, and trends, you need to consider whether the report is required to directly push results to a TMS for better visibility.
• Team Cooperation. When selecting, you need to make sure the report format can be shared among team members to get a better understanding and make decisions. comprehension of the situation. If the team uses certain tools (such as Jira or Slack) to communicate with each other, then test management software might be suitable to facilitate your test result display.

Benefits of Reporting in Playwright

• Thanks to Playwright reporting, teams can get comprehensive details about test runs and quickly pinpoint the root cause of issues.
• Teams can assess the suite of results in real-time, speed up the feedback process, and maintain a continuous development flow.
• With shareable reports, teams can quickly discuss test results with business stakeholders, even those without technical backgrounds, to accelerate understanding across development, QA, and product teams.
• Teams can prevent faulty code from being deployed and ensure continuous quality thanks to automated quality gates in CI\CD.
• Teams benefit from customizable Playwright reporting options to tailor their reports to unique requirements.
• Teams can generate report files (like HTML or JUnit XML), archive results, and analyze performance, failure rates, and trends over time.

Challenges in Playwright Reporting

There are some challenges in Playwright Reporting that teams should be aware of:

• While Playwright offers custom reporters, creating interactive reports beyond the built-in options can demand significant development effort.
• Teams face difficulties in identifying key issues in the test reports in terms of including too much information in the suites of tests.
• The use of multiple environments, including various browsers and devices, can contribute to generating unpredictable results.
• Flaky tests are prone to producing false positives or negatives, which might result in inaccuracy in the reports.
• Slow page loads may cause an increase in reported execution times and impact accuracy.
• Complicated user flows and dynamic content can overwhelm reports with redundant information.

Tips for Effective Playwright Reporting

Here are some tips to follow to enhance the Playwright test reporting:

• Before executing the tests, it is essential to clearly define what you’re testing and focus on metrics which will help you determine what success looks like.
• You need to create a reporting format that is easy to interpret and helps teams resolve issues quickly. Likewise, the content can be presented in HTML format or offered as a downloadable PDF.
• For better understanding, you need to add screenshots/videos to your test reports to provide visual context and make sure they are shareable.
• You need to use CI tools to automatically trigger the report creation and distribution after each test run.

Want to Reap the Benefits from Playwright Reporting?

With good test reporting, you can turn testing data into actionable insights. Using Playwright’s reporting tools allows teams to get useful information about their results, uncover problems early, and make testing better. Thanks to diverse types of reporters in Playwright and integration capabilities, teams can integrate multiple reporters and even create custom ones to meet their different needs in testing.

If you are interested in simplifying Playwright reporting and integrating it with testomat.io for better management, do not hesitate to drop us a line and learn more about the services we provide.

The post Playwright Reporting Generation: All You Need to Know appeared first on testomat.io.

In the article, you can discover more information about the Pytest BDD framework, learn how to write BDD tests with Pytest, and reveal some considerations to help you make the most of the Pytest BDD test framework.

Why teams need the Pytest BDD framework

If your team works on Python projects, pytest-BDD will give them a sizable boost in project clarity.

• Tech teams and non-technical business executives can take part in writing test scenarios with Gherkin syntax to describe the intended behavior of software in a readable format to make sure it meets business requirements.
• Teams can verify user stories and system behavior by directly linking them to feature requirements.
• Teams can make the test automation process more scalable with pytest’s features like fixtures and plugins.
• Teams can create a solid Steps base for test cases and reuse code in other tests by turning scenarios into automated tests.
• Teams can easily update BDD scenarios as the product changes.
• Teams can get detailed test reports with relevant information about the testing efforts.

Fixtures & Tags: Why use them?

With pytest-bdd, teams can use the power of the entire Pytest ecosystem, such as fixtures and tags.

Fixtures

Marked with the @pytest.fixture decorator, fixtures are known as special functions that provide a way to set up and tear down resources required for your tests. They are very flexible and have multiple use cases – applied to individual tests, entire test classes, or even across a whole test session to optimize resource usage. There are various reasons for using Pytest fixtures:

→ Fixtures are implemented in a modular manner and are easy to use.
→ Fixtures have a scope (function, class, module, session) and lifetime that help to define how often they are created and destroyed, which is crucial for efficient and reliable testing.
→ Fixtures with function scope improve the test code’s readability and consistency to simplify the code maintenance process.
→ Pytest fixtures allows testing complex scenarios, sometimes carrying out the simple.
→ Fixtures use dependency injection (configuration settings, database connections, external services) to improve test readability and maintainability by encapsulating setup and teardown logic.

While fixtures are great for extracting data or objects that you use across multiple tests, you may not use them for tests that require slight variations in the data.

Tags

Tags are a powerful feature that helps selectively run certain tests based on their labels. They also allow teams to assign tags to scenarios in feature files and use pytest to execute tests, especially when dealing with large test suites. Tags can be used to indicate test priority, skip certain tests under specific conditions, or group tests by categories like performance, integration, or acceptance. Let’s consider the reasons for using tags:

→ You need to run suites of tests that are relevant to your current needs, like testing a particular feature.
→ You need to group tests based on their functionality, priority, or other relevant criteria to easily understand the test suite structure and find specific tests in the future.
→ You need to execute tests that match multiple tags by using logical operators (AND, OR, NOT) to precisely target the tests you want to run.
→ You need to automate the execution of specific test subsets and get customized reports based on test tags.

While tags help categorize the tests based on any criteria, their overuse can lead to a cluttered test suite and make it hard for developers to understand or maintain the code.

In fact, Pytest has limitations, but it comes with many plugins that extend its capabilities, among them the Python BDD plugin, which we are interested in at this point in the article. This plugin provides all the advantages of Python in BDD, which is why many automators love it

Getting Started with Pytest BDD

Prerequisites: Setting up the environment

If you are ready to utilize pytest-BDD, you need to make sure that all the required tools and libraries are installed. Below you can find out the steps to follow to set up the environment and start writing BDD tests:

1. 1. Install Python. You need to download the latest version from Python’s official website to get Python installed on your system. Then you need to verify the installation by running the command:

      python --version

   2. I used the optional alias python to python3 (macOS/Linux only) because I saw messages: command not found, as python3 was installed instead of python.

      alias python=python3
      

   3. I installed the package manager pip.
   4. Indeed, some test automation engineers prefer to use the Poetry library over Virtualenv. Poetry is more modern and enables management of dependencies in the global project directory without manually activating environments.
   5. Set up a Virtual Environment. At this step, you can create a virtual environment for your project to isolate it from other environments, give you full control of your project, and make it easily reproducible. Firstly, you need to install the virtualenv package if you haven’t already with pip. Once installed, you can specify the Python version and the desired name for the environment. It is a good practice to replace <version> with your Python version and <virtual-environment-name> with the environment name you want to give.

      pip install virtualenv

   6. Install pytest and pytest-BDD. At this step, you can use pip to install both the pytest framework and the Pytest-BDD plugin

      pip install pytest pytest-bdd

   7. Install Additional Dependencies. If you need additional libraries like Selenium or Playwright, you can install them as well. We need them to operate on a browser. For instance Playwright

      playwright install
      

   8. Activate virtualenv based on your OS

      source venv/bin/activate
      

   9. Create Feature Files and Steps File. The last step before writing the BDD tests is creating a structured project directory where you will keep your feature files and test scripts. Typical project structure looks like:

      pytest_bdd_selenium/
      ├── features/
      │   └── login.feature
      ├── steps/
      │   └── test_login_steps.py
      ├── tests/
      │   └── test_login.py
      ├── conftest.py
      ├── requirements.txt
      └── pytest.ini
      

How to write BDD Tests with Pytest

To write a BDD Test with Pytest, as mentioned above, you need to create a feature file and define step functions that match the scenarios in the feature file.

#1: Writing Feature File

To write feature files, you need to understand the Gherkin syntax used to describe the behavior of the application in plain English. The “given/when/then” vocabulary is pretty clear to all team members – analysts, developers, testers, and other specialists without technical background. Generally, the feature files work as living documentation of the system’s expected behavior. More information about Gherkin-based feature files can be found here.

Here is a basic example of a successful login functionality on this site https://practicetestautomation.com/practice-test-login/ 

Feature: Login functionality

  Scenario: Successful login with valid credentials
    Given the user is on the login page
    When the user enters valid username and password
    Then the user should see the secure area

#2: Creating Step Definitions

Step Definitions map the Gherkin steps in your feature files to Python functions. Pytest-bdd matches the steps in feature files with corresponding step definitions. Here is an example code for user login:

from pytest_bdd import scenarios, given, when, then

scenarios('../features/login.feature')

LOGIN_URL = "https://practicetestautomation.com/practice-test-login/"
USERNAME = "student"
PASSWORD = "Password123"

@given("the user is on the login page")
def open_login_page(browser_context):
    browser_context.goto(LOGIN_URL)

@when("the user enters valid username and password")
def login_user(browser_context):
    browser_context.fill("#username", USERNAME)
    browser_context.fill("#password", PASSWORD)
    browser_context.click("#submit")

@then("the user should see the secure area")
def check_login(browser_context):
    header = browser_context.locator("h1")
    assert "Logged In Successfully" in header.text_content()

* File test_login.py might be empty if all scenarios are loaded from a step file.

#3: Create Conftest file

Now, Playwright uses built-in fixtures like Page, and in many cases, we do not need it — Playwright provides everything.

You only need it if you want to:

• Add custom fixtures (e.g., for login tokens, DB, API)
• Change browser settings (e.g., headless, slow motion)
• Set up project-wide hooks
• Configure Playwright launch options

Our basic application is a login, so we have to create conftest.py

import pytest
from playwright.sync_api import sync_playwright

@pytest.fixture
def browser_context():
    with sync_playwright() as p:
        browser = p.chromium.launch(headless=False)  # set True for headless
        context = browser.new_context()
        page = context.new_page()
        yield page
        browser.close()

Using a Page, you can update your steps/test_login_steps.py file

from pytest_bdd import scenarios, given, when, then
from playwright.sync_api import Page

scenarios('../features/login.feature')

LOGIN_URL = "https://practicetestautomation.com/practice-test-login/"
USERNAME = "student"
PASSWORD = "Password123"

@given("the user is on the login page")
def open_login_page(page: Page):
    page.goto(LOGIN_URL)

@when("the user enters valid username and password")
def login_user(page: Page):
    page.fill("#username", USERNAME)
    page.fill("#password", PASSWORD)
    page.click("#submit")

@then("the user should see the secure area")
def check_login(page: Page):
    assert "Logged In Successfully" in page.text_content("h1")

#3: Executing PyTest BDD

Once the feature file and step definitions have been created, you can start test execution. It can be done with the pytest command:

pytest -v

#5: Analizing Results

After PyTest BDD tests execution, you can analize, measure and review your testing efforts to identify weaknesses and formulate solutions that improve the process in the future.

If you integrate pytest BDD with a test case management system such as testomat.io, you can generate test reports, analyze them, and get the picture of how your tested software performs. 

You can debug your Playwright tests right inside the test management system for faster troubleshooting and smoother test development.

Advantages of Pytest BDD

• Pytest BDD works flawlessly with Pytest and all major Pytest plugins.
• With the fixtures feature, you can manage context between steps.
• With conftest.py, you can share step definitions and hooks.
• You can execute filtered tests alongside other Pytest tests.
• When dealing with functions that accept multiple input parameters, you can use tabular data to run the same test function with different sets of input data and make tests maintainable.

Disadvantages of Pytest BDD

• Step definition modules must have explicit declarations for feature files (via @scenario or the “scenarios” function).
• Scenario outline steps must be parsed differently
• Sharing steps between feature files can be a bit of a challenge.

Rules to follow when using Pytest BDD for Test Automation

Below you can find some important considerations when using Pytest-bdd:

• ‍You need to utilize Gherkin syntax with GWT statements.
• You need to use steps as Python functions so that pytest-bdd can match them in attribute files with their corresponding step definitions to be parameterized or defined as regular Python functions.
• You need to utilize the pytest-bdd and pytest fixture together to set up and break down the environment for testing.
• Each scenario works as an individual test case. You need to run the BDD test using the standard pytest command.
• You can use pytest-bdd hooks to generate code before or after events in the BDD test lifecycle.
• You can use tags to run specific groups of tests, prioritize them, or group them by functionality.

Bottom Line: Ready to use Pytest BDD for Python project?

With pytest-BDD, your teams get a powerful framework to implement BDD in Python projects. When writing tests in a clear and Gehrkin-readable format, teams with different backgrounds can better collaborate, understand business requirements, and build what the business really needs. Contact us if you need more information about improving your Pytest BDD workflow and its integration with the testomat.io test case management system and increasing test coverage.

The post Behavior-Driven Development: Python with Pytest BDD appeared first on testomat.io.

Productive development and accurate QA require an efficient test automation strategy, and today we will tell you how to create it. This article explores the role of automation and the key elements you should consider in strategy development. With our checklist, you will be able to make sure no step is missed and the testing will be conducted properly!

What Does Automation Mean for Software Development?

The test automation approach means that specialized tools are used to automatically check the key parameters and functions of the newly created program solution. It helps to achieve higher product quality and ensure reliable performance for clients and applications’ end-users. With proper test management tools, the Quality Assurance department can arrange a smoother testing process, enable higher efficiency, and reduce the risk of errors.

Manual testing can provide real user feedback and indicate which adjustments the development team can consider to make the product more user-friendly. However, it is more time-consuming and requires specific expertise and a knowledge base from testing team employees. The testers’ ability to use their intuition and react to changes in real-time mode makes them an important part of the testing process. Automation does not replace the team; it can empower employees with enhanced accuracy.

What Kinds of Tests Can Be Automated?

You can divide all testing into two main groups: functional and non-functional tests. Functional ones include tests that show if the solution performs its functions as required. For instance, the system can automatically check if users can share notes. Non-functional testing covers everything beyond functionality, like security and performance.

So, What Can We Test Automatically?

There is plenty types of testing, but let’s focus on the main ones:

Accessibility

This type of tests helps developers create solutions for everyone, including people with different physical capabilities. The automation will not completely replace manual audits; however, it can offer fast feedback and enhance accessibility standards checking.

Usability

Usability testing ensures the applications’ simplicity and ease of use. Usually, they are performed manually, but automation can boost testing by collecting usage data and automating scenarios to check where users face problems.

Security

Automated security testing provides early detection of vulnerabilities. Such tests can identify if any configuration can work inefficiently and lead to data loss or a leak. They highlight weak points and enable more efficient fixes.

Performance

The QA team measures response times and can evaluate load stability with the performance tests. An automated system can simulate close-to-stress conditions, checking how the system works under pressure.

UI interactions

UI testing tools can imitate user interactions across different devices and browsers to see how well the design will function under various conditions. It enables faster validation of design behaviors and ensures cross-platform compatibility.

Critical paths and functionality

The so-called smoke tests check core application functionality after each build. Their main mission is to verify that all critical paths, like logins, data submission, and others, work as expected.

Regression

Automation is a lifesaver for regression tests, as they take lot of time and effort when performed manually. These tests ensure that new changes will not break anything in existing functionality.

What Testing Practices Can We Apply?

Static testing from the QA views means examination of initial requirements and documentation, finding mismatches there to fix potential defects before starting the development phase. From the developers’ perspective, it employs tools such as formatters and type checkers, which can proofread the code, catching errors and typos.

Unit tests enabled checking of isolated code pieces to detect the issues and help developers fix them faster.

API testing controls that all the requests are handled properly. With an API check, you can ensure all responses meet your expectations and align with project requirements.

The integration test goal is to verify that all components function. It is especially important for applications based on independent modules, like in microservice architecture.

End-to-end testing leverages the GUI and flow paths to check multiple elements simultaneously. The automated system simulates close to end-user interactions to check how the application will react.

Find out more about these testing types:

Test Pyramid Explained: A Strategy for Modern Software Testing for Agile Teams

What benefits does software development get from test automation

Alt: Benefits of test automation strategy creation

In our experience, four benefits can impact the decision to apply automation. Let’s examine them all and their effect on development workflows.

#1: Smarter Time and Resource Allocation

According to most research, software test automation can reduce 20% of manual efforts related to repetitive tasks. For instance, AI-powered solutions like testomat.io can generate test cases and execute them with inhuman speed and accuracy, saving time for QA teams for other tasks. Organizations state that after five years of continuous automation use, they saved up to 50% of costs.

#2: Accuracy

Automated testing minimizes the chances of human errors, enhancing the overall product quality. Human testers may struggle to maintain the same contraction level during the testing cycle, but it is not a problem for an automated solution. It can cover a wider range of test cases and variable scenarios in round-the-clock mode without quality and efficiency loss.

#3: Quicker Feedback

Automated solutions perform the testing faster, and developers get their feedback. The testing cycle still needs human involvement, but the right tools can highlight the parts where it is required first. It enables more effective workflows and boosts bug fixing. 

#4: Wider Test Coverage

Automated testing allows QA to check various scenarios and environmental conditions, enabling a more comprehensive approach to software tests. It demonstrates how the software program will operate and what features need adjustment. As a result, the development company can create a high-quality, user-friendly solution with a minimum chance of bugs.

What is a Test Automation Strategy?

An automation strategy is a plan that describes how to implement automation solutions into existing testing workflows and reduce the number of failures during tests. Traditionally, it includes the areas the business will automate and the chosen methods and test automation tools. A strategy also considers the goal and defines the testing environment that matches it best. It informs about the software’s capabilities and functionality and helps to check if all required features are present.

Why Do Businesses Need an Automation Strategy?

An automation strategy helps to choose the correct tools and technology stack to reduce the required time and minimize problems. It can forecast and analyze related risks and develop remedies and alternatives to address potential issues. A plan can also serve as a tool to ensure that developers achieve everything expected, comparing what was planned and what was done.

The quality assurance process is not a place for improvisation, as a poor strategy can cause downtime and affect the brand’s reputation with a low-quality final product. A well-prepared strategy can drive you to the top of the competition, ensuring the software’s proper performance and a lower chance of technology failure. 

Key Elements of the Efficient Automation Strategy

Automation Goals

It is important to set a clear scope and objectives for future automation. For instance, to make it work, you need to decide what exactly you want to upgrade with an automation solution and what goal you want to achieve.

Technology Stack

The tech stack depends on the available budget and the specific project’s requirements. The strategy should highlight which tools work best to fulfill the set goal. For example, it is vital to consider platform compatibility and the simplicity of use.

Test Environment

We recommend setting up a testing environment that matches a real production setup to see how a solution will function under close-to-real conditions and how it will work for future users. The strategy should help to adjust software, hardware, and networks to create a reliable and stable area for testing.

Test Data Requirements

An effective test automation strategy plan should identify the data required for existing and future automated test scenarios. It is important to ensure data security and privacy while checking how the solution will react to realistic input.

Integration

The connection with a CI\CD (Continuous Integration\Continuous Delivery) pipeline enables automated test execution. Any change or adjustment triggers a series of tests to check how the update affects the solution. As a result, the developers get detailed feedback stating the current code quality and functionality. This approach helps to detect and identify issues at the early development stages.

Test Case Prioritization

Priority setting helps organize the workflow, making the system run critical test scenarios and features first. For instance, the Testomat management tool is an advanced solution for testing flow organization and control, ensuring full test coverage.

The Automation Testing Strategy: How to Build It in 9 Steps

Based on our experience, we have created a 9-step guide to help you plan an efficient strategy. Feel free to follow it, but remember that each project requires an individual approach, and even the best strategy needs adjustments to match your purposes fully. 

1. Test Automation Goal and Scope

Now your mission is to set a clear goal. A measurable target will guide the process to specific results. It doesn’t have to be a huge one; it can be simple, like priority test case automation. Then, set the scope to ensure that teams will not spend time inefficiently. For instance, they need to understand what work is supposed to be performed manually and what will be automated.

2. Requirements

Communication and discussions with stakeholders will help to set automation priorities and select goals and KPIs. Based on them, you will be able to form testing requirements. At this stage of strategy creation, you can choose which testing types to run.

3. Risk Identification

Risk analysis enables effective test prioritization by ordering based on the risks they can pose to the business. It will help address potential issues and define their probability. Here, you need to establish criteria to measure the impact of the risk on the project goal. Reviewing different outcome scenarios will help to evaluate how it can affect the business and measure its severity. It is better to document findings and update the risk register to control them in the future and prevent critical issues.

4. Automation Test Case Selection

It is time to decide which workflows and functions require an automation upgrade. Business value points should be at the top of the priority list, but it is also important to analyze how stable the current workflows are. You can analyze set goals, requirements, and risks to select parts for automation.

5. Test Data and Environment Choice

The testing environment should be stable and close to real product conditions. If you need to, you may clean up the testing artifacts after the testing cycle finishes.

When it comes to test data, remember that regional regulations like GDPR have very strict limitations on data use. Privacy and information security should always be considered. This is why QA teams quite often use synthetic data for testing.

6. Framework and Technology Stack Selection

Frameworks and technology choice depend on the project’s nature and the development team’s expertise. By choosing technologies that are familiar to the in-house team, you will be able to simplify testing. We also recommend considering ease of use and compatibility with existing systems. If you are testing a mobile application, you also need to pay attention to supported platforms.

7. Progress Tracking

Progress tracking is vital for proper workflow arrangement, as it helps to control at which stage your team is at the moment and what is left to be done in the future.

Best practices for progress tracking

• Management tool. You can stick to three testing statuses: planned, automated, and outdated, or add more if required for your project.

• Backlogs in ticket management systems are also quite useful for the work allocation and organization of the process.

Anyway, we recommend the first one, as it is specifically designed for this purpose. Unlike general-purpose ticket systems, it provides features like test case reuse, result history, traceability to requirements, and structured reporting, which are essential for maintaining quality in software testing.

8. Reporting

It is important to arrange a failed test analysis and reporting process to make the feedback useful for corrections. For instance, decide which team members should get a report after a specific failure to fix it.

9. Further Maintenance

Test automation works only if you perform continuous maintenance. It is not a one-time procedure that you do and forget; it’s a process of improvement. Reacting to and updating the outdated scripts will help to keep the automation running properly. You can also optimize testing efficiency by designing and implementing reusable tests to improve resource allocation and AI testing agents.

Checklist for Automation Testing Strategy

Checklists are handy for more advanced automation process control. Let’s review the main points businesses need to check in any project before automation implementation:

→ Checking the current automation effort status is complete.
→ Goals and objectives are clear.
→ The scope of the test automation project is defined.
→ The team and stakeholders set realistic expectations for automation.
→ The budget covers all required updates.
→ The team is capable of using new automation tools.
→ Tech profiles and apps for automation are chosen.
→ Experts conducted advanced risk analysis.
→ You have discussed and chosen the technology stack, test data, and environment.
→ The implementation team possesses the relevant knowledge, experience, and skills.
→ Progress tracking and reporting methods are chosen.
→ The project has a set timeline.

What Are the Risks of an Ineffective Automation Strategy?

A well-crafted automation strategy can define what should be done and how to achieve it most effectively, but what will happen if it is built wrong? The obvious answer is that it will cause more problems than benefits.

The Complete Picture Is Lost

Without a clear goal and objective understanding, it will be difficult to measure the results and see if the desired outcomes were achieved. The absence of a certain target may lead to impulsive decisions and chaotic updates in technology and irrelevant new features. Strategy allows focusing on the bigger picture and sticking to set priorities instead of jumping from one task to another.

Inefficient Resource Use

Unrealistic expectations that do not match the budget may cause inefficient resource allocation. For instance, trying to save money on the automation script, the business may choose a version that is not adaptable to changes. As a result, instead of reducing the manual effort, it will need additional manual updates and extra costs for rework and further maintenance.

Incorrect Technology Choice

A test strategy helps a team choose the technology stack that will match objectives and required functionality. A missed step or wrong choice may lead to a mismatch with your goals or the incapability of employees to use the provided tools.

Best Practices: Efficient Automation Strategy Development

If a poor strategy can lead to such risks, how can you avoid them? No worries, we have your back, and here is a short guide on how to make your automation strategy work

Communication Is the Key

Being on the same page is vital for productive strategy planning, so we recommend engaging stakeholders. Discussing future automation upgrades with developers, testers, and managers can align the team members with project goals. This approach will let all the departments involved in the development process express their needs and share their insights and feedback. It will make planning more productive and oriented to your business needs and the project’s specifics.

Version Control

Version control systems like GitHub can simplify test script management. They can track changes and return the script to a previous version if anything goes wrong in the current one. A centralized platform can make the process organization easier and improve ongoing maintenance.

Data-driven Testing Approach

Separating test data from test scripts enables script reuse across multiple data sets. This approach improves test coverage and enhances efficiency by reducing the number of duplicates. Separation can simplify further maintenance, excluding the difficulties caused by data changes. As a result, you get a lean and scalable testing workflow.

Continuous Testing

Testing strategies need to evolve continuously to match changing software development requirements. The best you can do to keep them up-to-date is to review and refine your approach regularly. Continuous testing helps maintain focus on the project goal and, at the same time, incorporates insights and experience gained from previous testing scenarios.

Run a Trial Test

You don’t have to jump to the large-scale test at once; you may start with a small-scale trial to test your automation strategy and establish a proof of concept. With such a pilot run, you can check automation advantages and capabilities before implementation. A test run will highlight potential challenges and enable more accurate estimation of the required effort.

Final Words

A testing automation strategy is a comprehensive plan that defines how to apply the automation solutions most efficiently and enable significant issue reduction. It helps businesses prepare for new tools and organize upgraded workflows to match the new goals and objectives. Testing strategy grasps the complete picture of the project and simplifies tech selection. A well-structured automation strategy guides the process, ensuring the desired outcome and controlling the done and planned automation steps.

Test management system testomat.io simplifies building a test strategy by unifying manual and automated testing in one place, making it easy to plan, organize, and scale your testing efforts. It offers real-time visibility, flexible test case organization and execution, seamless integration with CI\CD pipelines— helping teams align testing with release goals efficiently.

AI-driven features detect flaky tests, detached tests, suggest where coverage is missing, which essentially improves your test automation.

The post How to Create a Test Automation Strategy: A to Z Guide appeared first on testomat.io.

Let’s consider how we can maximize the most out of this approach,

The integration of a unified approach to BDD, the Test Management System, and Jira — three pillars establishing a clear structure for the testing process, that, in turn, facilitates communication among all project participants.

This suite of tools offers numerous benefits at its core:

→ First, it improves reporting visibility, which minimizes the risk of errors and ensures the delivery of high-quality software.
→ Second, by reducing testing time, it basically is the team’s efficiency; this set of tools leads to accelerating product development.

Further, we delve into detail on how they combine and work

Jira Role in BDD Implementation

Jira is a widely used project management tool for working with requirements and tracking defects. It plays a crucial role in our BDD-oriented approach by serving as a central hub for managing requirements, and it is a well-known tool for team participants that does not require additional training.

Before you pull a user story from Jira backlog into development, it is essential to have a conversation to clarify and confirm the acceptance criteria that your requirements should meet. Some teams do this during Scrum sprint planning, poker sessions, discovery workshops or backlog refinement meetings. Other teams have a specific Three Amigos Meeting. Whatever you call this conversation, its goal looks like an Example Mapping.

The conversation involves the following main iterative stages:

1. The team selects a small upcoming system update from the Jira backlog.
2. They discuss specific examples of new functionality.
3. These examples are pre-documented in a Given-When-Then form, a consistent basis for automated verification.

The result is clarification that serves as a common reference point for the team, enhancing understanding and ensuring alignment on feature requirements. Ensure fast iterations and the ability to make changes as needed, teams can return to earlier stages whenever additional information is needed.

Whereas Jira out-of-the-box does not support documenting user stories in a structured Gherkin syntax, we need to use extra Jira plugins like Xray, Zephyr, or Testomat.io to expand Jira’s capabilities of what we explore further below.

Test Management System and Its Role in BDD Process

Thus, thanks to the integration of Jira and Test Management Plugins, the team implements the pre-described behavior to Given-When-Then user stories. However, the right chosen test management tool can provide much more.

The Test Management System (TMS) is an important tool for organizing and optimizing the software testing process. It allows for:

• convenient planning
• executing and tracking test cases
• analyzing testing results

The test management system fosters more effective interaction among all participants in the process:

• developers (Devs)
• testers (QAs)
• Business stakeholders
• Product Owner
  …. e.g.

In the context of Behavior-Driven Development, a test management system simplifies the verification of BDD scenarios. At the same time, it ensures:

• clear and convenient test representation, Given-When-Then feature file
• ensuring seamless transparency and traceability between features and defects
• real-time documenting and updating with Living Documentation
• control over all verification stages
• streamlining BDD workflow
• maintaining BDD test scenarios

Moreover, Jira BDD and Test Management combination enables faster identification of errors during the requirements formulation stage, when fixing them is least costly — thus ensuring high product quality.

BDD Test Automation Frameworks

Automation scenarios using the Gherkin format help add value to your automation and respond quickly to feedback. Once user stories are described in Gherkin language, they are subject to verification by test automation frameworks. The Given-When-Then statements should be valid during checks with code-based statements defined in the automation framework.

Among the popular Automation frameworks which involve BDD tools and Jira in testing workflow are:

• Cucumber – usually executes test scripts written in JavaScript, TypeScript, Java or Ruby.
• Behave – helps automate BDD Python tests.
• JBehave – a Java-based BDD framework that supports automating of Java step definitions.
• Serenity – extends the capabilities of tools like Cucumber and JBehave. It is Java-based test automation frameworks, also Kotlin and Groovy.

Integration automation with BDD framework into your test workflow provides incredible efficiency for the test process. Namely, this allows:

Make testing not so consuming for repetitive tasks
Reduce overtime risks spent on testing in product releases
Ensure a transparent development process for the whole team
Improve overall software quality.

Integrating BDD, Test Management, and Jira

Now it is time to look at how the combination of BDD, test management tools, and Jira work together and how their connections promote collaboration, transparency, and automation at all stages of the development and testing.

Shortly:

Tests written with BDD are typically formulated in natural language, making them accessible to all participants in the development process.
BDD reduces the distance between development and testing, ensuring requirement compliance.
How Jira ensures that the requirements always align with the tests, quick bug fixes.
TMS allows for better control over the entire development process and timely delivery of a high-quality product.

Understanding of Jira Plugins

Many QA teams integrate Jira into their workflow through third-party services. Jira plugin – ensures integration of BDD tests with tasks in Jira, improving collaboration between testers, developers, and business analysts.

There are approaches to integration testing in Jira:

• Direct connection through Jira plugins, numerous of which you can find on the Jira Marketplace.
• Use of test management systems that support integration with automation tools.

The most common solutions are TestRail, Testomat.io, XRay, Zephyr, and qTest.

Testomatio for Jira

Advanced Jira Plugin is designed to meet one of the key needs of modern development: ensuring collaboration between technical and non-technical specialists in a project. Its advantage is that it is not limited by Jira functionality like many competing solutions. Its Jira integration is bidirectional. Any changes made in the Jira project, including changes to test plans, are immediately reflected in testomat.io, and vice versa – you can work with tests directly in the bug tracking system. Thanks to this, developers and other business stakeholders can fully participate in testing while working in a familiar tool.

The use of solutions like testomat.io test management plugin allows for combining manual and automated testing, including BDD within a single environment, providing convenient test management directly in Jira.

BDD tests can be added to the test management system in 4⃣ ways:

• Creation manual BDD tests – in the Gherkin editor, this is appropriate when the test scenario contains new steps. Intellegent autoformatting and autofilling are provided.
• AI BDD scenario generation – AI analyses your BDD project, existing BDD step, its requirements (Jira tickets) and suggests new ones based on them. If they’re okay, need to approve them.
• Migration and automatic conversion tests to BDD – you can import manual test cases from other TMS, XLSX, or CSV files from other test management systems and automatically convert test cases into BDD scenarios. The system automatically recognizes steps and adds steps in Gherkin syntax to the database for future use. It’s convenient for teams that previously worked without BDD.
• Import automated BDD tests from source code – after creating a new project, you can use the Import Project from Source Code feature, selecting the appropriate parameters (framework, project language, OS). All test scenarios from BDD frameworks will be automatically transferred to the TMS. Testomat.io easily integrates with the popular BDD framework Cucumber. You can import ready-made test scripts from this environment, edit them in TMS, run them, and track test results through detailed reports.

To optimize the process of creating and managing BDD tests, testomat.io offers a range of useful features:

• Reusing steps with the Step Database feature allows you to store all imported or manually created steps, helping save time when creating test cases and keep your project more unified.
• Automatic step updates during synchronization – the step database is updated every time new scenarios are added. If this does not happen automatically, the “Update Steps” function can be used.
• Functional file editing mode – allows you to modify Gherkin scripts: format them, add new or existing steps, and quickly find and replace words in all test steps.
• Tags – is an effective mechanism for managing test sets, allowing them to be grouped by various criteria, including test type or operating system.
• Search and filtering of tests – the ability to find necessary test cases by tags, test status (manual/automated, disabled, etc.), responsible specialist, or other parameters.
• Labels and custom fields – a tool for adapting the TMS to a specific project. It allows assigning labels to test cases, test sets, test plans, etc.
• Conversion of manual tests to automated – after creating manual BDD tests, they can be automatically converted to an automated format. Just write the code and import it into the system.
• Living docs – BDD test scenarios automatically become the technical documentation of the project, available to all participants and updated in real time.
• Advanced analytics – allows analyzing testing metrics such as unstable tests, slow tests, automation coverage, tag statistics, etc.

Create Jira tasks from TMS and track test results and defect fixes without the need to switch between tools.

Integration with Jira allows for executing test cases without technical skills. The bidirectional interaction between TMS and the project management system ensures automatic reflection of test results in Jira and quick creation of defect reports. Look at the picture below, what kinds of test execution you can choose on Jira Plugin Dashboard:

Benefits Automating BDD Testing with Test Management Systems & Jira

Automating BDD testing using test management systems (TMS) and integration with Jira significantly improves software testing efficiency. It ensures continuous testing, reduces testing time, and minimizes errors related to human factors, which is a key element in modern software development processes and allows teams to focus on improving product quality.

Integrating BDD scenarios with Jira allows each scenario to be automatically linked to the corresponding task or user story in the project management system. This helps clearly track which requirements are being tested and which testing stages have been completed.

When integrating BDD tools, such as Cucumber with Jira, testing can be automatically triggered with each change in Jira tasks. For example, when a developer finishes a task or creates a new branch in the repository, the corresponding BDD test scenarios are automatically executed, providing quick feedback and identifying potential defects early in the development process.

One of the main advantages of integrating BDD with TMS and Jira is the automatic generation of test result reports. After executing automated tests, all results can be viewed directly in Jira or in the TMS, enabling teams to quickly identify issues, defects, or deviations from expected outcomes. This reduces the time spent analyzing test results and speeds up decision-making.

Detailed reports help in the faster detection and correction of errors.

Thanks to the integration of BDD with continuous integration and delivery (CI\CD) systems, test automation becomes part of the daily workflows. Every change in code can automatically trigger tests, ensuring continuous testing and minimizing the risks of defects during development. Jira can display the status of tests and allow for real-time tracking of any errors.

Integrating automated tests with Jira keeps test status information up-to-date for all project participants, including developers, testers, and managers. This improves communication and fosters closer collaboration between teams. Everyone can easily review the current state of testing, enabling quick issue identification and process adjustments.

Finally, automating testing with BDD, TMS, and Jira not only accelerates testing but also provides greater reliability and accuracy of results

Xray

Xray test management in Jira – manages all tests as standard Jira tasks, allowing you to customize screens, workflows, and add custom fields for test scenarios.

Xray allows you to plan, write, and execute tests, as well as create detailed test reports. This system uses Jira issues to process tests that change at each stage of testing.

Xray organises tests, allowing you to manage the entire execution process. This ensures tracking of coverage, error detection, and basic release management. Detailed tracking reports allow you to identify which test failed and at which stage the issue occurred. This helps understand what needs to be fixed and efficiently collaborate with developers to resolve the issues. Implemented integration with frameworks like Cucumber and JUnit, so you can coordinate automation testing for your codebase.

What do you get with Xray?

• BDD support – work with behavior-driven testing without any limitations.
• Jira experience – testing is fully integrated into the familiar environment.
• Bug detection – check web, mobile, and desktop applications with new effective methods directly from your desktop, using seamless integration with Xray and Jira.

Xray is one of the oldest test management tools on the market, and some customers admit that its UI and core are somewhat obsolete. The tool works very slowly when dealing with large projects. The downside is that the implementation of large-scale and complex test projects is difficult without failures. It creates test cases with every team-run execution. Additionally, Xray’s advanced functionality is only available in the premium version.

Zephyr

Zephyr can be part of a BDD pipeline, but it doesn’t provide first-class BDD support natively, like before we talked about Xray or Testomat.io

1. Manually written BDD scenarios in Zephyr are not well-optimized for structured Gherkin input or Living Documentation.
2. Automated test cases written in Gherkin can be maintained in version control (e.g., Git), executed with automation tools, and the results pushed back to Zephyr via APIs only.

Zephyr offers users a choice of three options based on team size

• Zephyr Squad is a tool for running manual and automated tests in Jira, the most popular test management Jira test management solution.
• Zephyr Scale is a platform for growing teams working on multiple projects simultaneously, offering features for test result analysis, test run planning, and integration with BDD and CI tools.
• Zephyr Enterprise is an enterprise-level solution that allows testing across the entire organization, integrating the work of multiple teams in a single project, regardless of the development methodology used.

Overall, Zephyr for JIRA is known for its ease of use. However, the downside is that it works slowly and requires payment for each user. As a result, it becomes significantly more expensive than Testomat.io. Moreover, compared to it, Zephyr offers more limited functionality.

Benefits BDD, Test Management & Jira Integration

The integration of BDD with a test management system and Jira significantly improves the testing efficiency. Now let’s briefly recap the main points we have talked about above much

With a unified BDD, Test Management, and Jira integration, you can:

• Extend Jira capabilities, which works only with requirements.
• Formulate user stories and scenarios to define the acceptance criteria for specific software features.
• Create a common language (team communication) for developers and stakeholders (business analysts) to discuss user needs and software solutions.
• Ensure clear and structured test scenarios using natural language (Gherkin).
• Perform traceability of requirements. Link BDD tests with requirements and track progress as test coverage of the project progresses.

Conclusion

The integration of a unified BDD approach, testing systems, and Jira enables efficient tracking of tests and requirements, enhances collaboration between technical and non-technical project participants, and allows for quick response to changes and error correction during SDLC (Software Development Life Cycle). This combination ensures optimization of reporting, reduction of product time to market, and improvement of software quality.

The advantage of TMS is that it is a centralized system that can provide more, in addition to working with requirements, testomat.io also includes importing automated tests and its synchronisation with manual tests. In other words, the Testomat.io Jira plugin brings new capabilities to Jira that it could not offer on its own.

If you have any questions about implementing Jira BDD best practices together with our test management solution, drop us a line without hesitation!

The post Integration between BDD in Jira and Test Management System appeared first on testomat.io.

In this article, we will reveal what AI automation testing is, its key components and common use cases, overview benefits and limitations, and share actionable tips to help you get started with AI-powered test automation. Let’s get started

What is AI in Test Automation?

When we talk about AI in testing automation, we mean a type of software testing where artificial intelligence (AI) is applied to improve and make the testing process more streamlined and simple. Originally, AI automation testing is faster when there is a need to retrieve data, run tests, optimize test coverage, and identify bugs and other anomalies.

Key Components of AI For Automation Testing

• Machine Learning (ML). ML-based algorithms are main in AI automation testing in terms of their ability to learn from historical data, identify patterns, and forecast potential defects or anomalies.
• Natural Language Processing (NLP). In the context of testing, NLP equips AI automation testing tools with the ability to understand and interpret human language. While testing teams can use plain language for writing test cases, the AI automation testing tool can then turn them into scripts for further execution.
• Data Analytics. With advanced data analytics incorporated into AI testing tools, teams can assess large volumes of test data and extract meaningful insights. Artificial intelligence can also be used for test results analysis to detect recurring issues or performance faults.
• Computer Vision. AI-driven image recognition helps detect visual anomalies in highly descriptive UIs and maintain consistent visual layouts.
• Robotic Process Automation (RPA). When RPA integrates with AI, it enables the automation of repetitive, rule-based tasks – data entry, report generation, and environment setup, which can be performed within the testing lifecycle to let testing teams concentrate on more strategic activities or processes.
• Self-Healing Scripts. With these scripts, AI-based tools can automatically update scripts when either the UI or code changes, minimizing the maintenance efforts of the software development team.

Use Cases of AI in Test Automation: How to use AI in automation testing

Artificial intelligence has had a major impact on automation testing that we can’t ignore. The uses of artificial intelligence in software testing for automation cover more use cases:

API Testing

With AI tool for automation testing, the process of API testing is more simple in terms of faster test cases generation, responses validation, and continuous monitoring of API performance. When used, it provides thorough API test coverage and helps detect issues before they impact production.

Visual Testing

AI-driven visual testing tools are used to detect UI inconsistencies across different environments. They can analyze screen elements, validate layout transitions, image misalignments, and incorrect colors to make sure that user interfaces are pixel-perfect based on the number of visual changes incorporated.

Performance Testing

When applying AI for performance testing, it enables analyzing performance data and predicting potential bottlenecks in the application that is being tested. Thanks to this approach to performance testing, developers can address performance issues in early development process.

Analytics for Test Automation Data

Tests generate vast amounts of data, which must be analyzed to derive meaning. The addition of AI to this process significantly improves its efficiency. AI-powered algorithms may discover and classify faults. More powerful AI systems can detect false negatives and genuine positives in test scenarios.

Predictive Analytics for Defect Detection

With predictive analytics, testers apply historical data from previous test results, code quality statistics, and defect patterns to create ML-based models. These models will help them uncover potential defects and predict future bugs by analyzing current test results in real time and identifying patterns and anomalies. Thus, they can optimize their testing strategies and accordingly allocate resources.

Generative AI automation testing

Generative AI generates information from diverse sources to create an array of test cases that cover a wide spectrum of scenarios. It provides a comprehensive testing process across a wide range of data inputs and helps in detecting potential bugs and anomalies.

AI-Assisted Bug Detection

AI can identify patterns that indicate potential bugs or issues even before traditional tests have been run. This predictive capability can help testers focus on areas that are more prone to defects.

Codeless Testing

Testing without code allows the testing teams to create automation test scripts without using programming languages. With visual interfaces, drag-and-drop functionalities, and sometimes natural language processing, they are able not only to design but also keep control of test cases through more intuitive and user-friendly ways.

Natural Language Processing for Test Design

With NLP, tools can extract requirements from user stories, use cases and functional specifications and automatically generate test cases in a structured format. They can also update test cases as requirements evolve to reduce manual testing effort and ensure better test coverage.

Self-Healing Test Automation

AI-driven algorithms in self-healing test automation identify, analyze, and dynamically update test scripts whenever application changes happen in the UI. It saves time and effort for QAs to maintain test scripts and continue execution of test cases, even when changes are made to the app under test.

Simulation & Virtual Testing Environments

AI-driven test automation can be used to create virtual environments where software can be tested under different conditions and scenarios. By simulating real-world situations, teams can test the software’s robustness and resilience – either network disruptions or hardware failures. Here we can include cross-browser testing.

Mobile AI Driven Automation Testing

AI-based tools can analyze UI and user interactions in mobile applications and check layout inconsistencies and performance issues to speed up mobile testing. Simulate testing on various devices.

Security Testing

AI test automation tool is built to scan code for security loopholes and find weak points in both APIs and web applications. It helps detect vulnerabilities and prevent potential cyberattacks and data breaches before deployment.

Benefits of AI software testing automation

Now, traditional testing methods struggle to keep up because organizations strive to deliver software solutions faster. With AI-driven test automation, they significantly streamline the testing process. Let’s discover more benefits below:

• Teams eliminate the need for manual data creation by automatically generating test cases and maintaining scripts while enhancing the efficiency of the process.
• Teams analyze historical and current data, predict areas which are likely to fail, and proactively address them before potential issues arise.
• Teams can focus on high-risk areas of the application and, through intelligent test execution, run tests based on various factors, such as code changes, historical results, and user behavior analytics.
• Teams integrate AI into CI\CD pipelines to carry out continuous testing.
• Teams identify edge cases and provide continuous testing coverage.
• Teams detect issues faster, leading to long-term cost savings.
• Teams quickly resolve UI issues to improve the overall user interface, delivering a more aesthetic user experience.

Limitations of AI in automation testing

Despite its benefits, AI automation testing has some drawbacks to consider:

• When it comes to implementing AI-powered testing tools, it demands an initial investment of money, as commonly AI-powered testing platforms require a subscription.
• AI systems require training and expertise from team members in order to manage AI test automation more effectively.
• AI can’t perform exploratory and usability testing, which require the intuition of humans.
• While AI can reduce test maintenance, it still requires oversight and periodic updates.
• There is a need to have enough quality data for AI’s effectiveness.
• In terms of “low-code” platforms, test creation is getting more intuitive and accessible to non-engineers.

AI and Automation Testing: Tips To Follow

When you think of implementing AI in test automation to streamline processes and improve testing efficiency, it is crucial to follow some key strategies to get the best results from AI integration.

Here are some tips to help you implement AI in your test automation process:

• From the very start of using AI in your testing process, you need to define what you want to achieve with it – improve the test coverage, reduce the test execution time, speed up test case generation, etc.
• You need to train your QA teams so that they can use AI tools effectively.
• You need to understand how the AI tools work and how to use them. So, start by automating a few key areas to try it and only then scale up.
• You need a lot of relevant data to train AI to produce the best results. If you use a small or biased dataset, you face overfitting and get unreliable results.
• You need to balance the testing capabilities of AI tools with human problem-solving skills to get the most out of the AI test automation process.

Check out some of our other posts:

• AI Testing Tools: An Effective Way to Optimize Your QA Processes
• Playwright AI Revolution in Test Automation
• AI Mobile Testing – How To Use It Effectively
• Role AI testing, in particular, ChatGPT in Software testing

CodeceptJS AI Self-Healing Capabilities in Your Testing

Codeceptjs testing framework is one of our team’s developments, embraced by teams worldwide. It supports the AI Test Self-healing functionality for Automated tests, which boosts UI test reliability by smartly fixing broken selectors. When UI updates alter classes, IDs, or DOM structures, static locators often fail. Codeceptjs analyzes past selectors and pattern matching to locate elements, keeping tests on track. Look of how it works:

The key benefits of Codeceptjs AI Healing are the slashing of maintenance costs and flaky test failures in dynamic web apps.

Powered AI Automation Test Management

Test Management System plays a crucial role in the testing process, especially when it is fueled by AI-powered automation testing. This combination is a key in modern testing ecosystems. Not only provides sync manual and automated tests in a single test platform for efficient organization, execution, and analysis. It drives efficiency, quality at speed, and smarter QA decisions in automation.

You can see the stack trace and exception to know where the error is located.

AI-Driven Reporting & Analytics apply AI to historical test trends, a TMS can surface intelligent insights, such as predicting flaky tests, identifying performance bottlenecks, or highlighting coverage gaps. This empowers teams to make faster, data-backed QA decisions.

If you press the button Explain Failure, you can see the recommendation on how to fix the framework broken by the error in the code.

Bottom Line: Future of AI in Test Automation

AI technology changes test automation thanks to intelligent test case generation and test cases management, bug/issue detection and report generation. In many ways, AI speeds up software testing and automates repetitive tasks, but there is no one-size-fits-all solution in testing. QA testers are still irreplaceable for their cognitive skills, creativity, and problem-solving abilities. In many ways, AI can not identify unexpected user behavior or minor inconsistencies in interfaces.

Even with more sophisticated intelligent ai based automation testing solutions, the future of automation testing succeeds through a collaborative approach where testers can use their emotional intelligence while AI assists them. However, if AI-powered tools could create self-healing tests which automatically adjust to UI changes, it would be really innovative. We are convinced that using the strengths of both AI and human experts leads to achieving the highest quality software possible. If you are interested in AI-based automation testing or have any questions, feel free to reach out to us here.

The post AI Automation Testing: Detailed Overview appeared first on testomat.io.

— Are you surprised

— The reason? Flaky tests are!

This article explores how to identify, eliminate, and prevent dangerous flaky tests.

Unstable tests can become a serious obstacle that complicates the development process. They create uncertainty and require significant time and resources to resolve when they are detected.

→ What do flaky tests entail?
→ What triggers them?
→ How can they be fixed or prevented? — it is the most important for us.

You will find answers to all these questions below

What Is a Flaky Test?

A flaky test means an automated test that produces inconsistent results. We can talk about a spontaneous test failure and a successful pass during the next test execution. This behavior is not related to any code changes. Naturally, this type of software testing does not contribute to overall Quality Assurance. In other words, it prevents teams from effectively reaching their objectives.

Key characteristics of flaky tests include:

• Inconsistency of results. Flaky tests produce unreliable outcomes, as their results fluctuate regardless of code changes.
• Unreliable test status. Assessing a product with flaky tests is inherently unreliable, as the results cannot be trusted. The Pass and Fail statuses fluctuate randomly with each retry, making them unpredictable and difficult to interpret.
• Dependence on external factors. These tests are extremely vulnerable to external dependencies, including environment variables, system configurations, third-party libraries, databases, external APIs, and more.

We have now defined flaky tests and outlined their key characteristics. However, the common causes of their occurrence have not been revealed yet.  Let’s delve into this topic

What Causes Test Flakiness?

Understanding the root causes of flaky tests will allow you to develop an effective strategy for their prevention. It will also help you mitigate the consequences if they do occur.

What can serve as precondition for future flaky tests?

→ Parallel test execution. Running tests concurrently can enhance the efficiency of QA processes. However, in some cases, this approach may backfire, leading to test instability. This happens when multiple tests compete for the same resources. In other words, race conditions are present.

→ Unstable test environment. A project may face unreliable infrastructure or fluctuating system states. Insufficient control over the environment or lack of isolation can also contribute to instability in testing.

→ Non-determinism. This refers to generating different results from the same set of input data. In testing, this can occur when tests depend on uncontrollable variables, such as system time or random numbers.

→ Errors in test case writing. These can result from misunderstandings within the team, incorrect assumptions, or other factors. As a result, the test logic may be compromised, leading to unreliable results, such as false positives or false negatives.

→ Partial verification of function behavior. When creating test cases, it is important to write as many assertions as possible. They should cover all aspects of the function’s behavior, touching on edge cases and accounting for all potential side effects. If this is not done (if the assertions are insufficient), the tests risk becoming unstable.

→ Influence of external factors. Some dependencies can negatively impact test stability. To illustrate, here are a few examples:

• Tests that rely on external services or API can become unstable.
• Problems with data consistency or synchronization can arise if testing is related to a database or external storage.
• System issues. Issues like high server load or memory overload can undermine stability.
• Device dependency. Instability may also arise from problems related to hardware availability.

Most of these preconditions for test instability can be eliminated, thus preventing future issues. However, if this is not achieved, it is important to be able to recognize flaky tests in time.

Learn more about the causes of flaky tests in this video: What Are Flaky Tests And Where Do They Come From? | LambdaTest

How to Identify Flaky Tests?

In this section, we will discuss how to determine that your test suite is not reliable enough due to the presence of flaky tests. It is crucial to do this, as ignoring the issue can reduce trust in the CI\CD pipeline overall and slow down development.

Here we are describing them in detail:

#1. Re-running Tests

Examine the test results when executed multiple times. If conflicting results arise during this process, it’s a clear indication of flaky test.

#2. Alternating Between Sequential and Parallel Test Execution

Test both sequential and parallel executions, then compare the outcomes. If a test fails only during parallel execution, this could point to race conditions or test order dependencies.

#3. Analyzing Test Logs

Reviewing the test execution history and error messages can reveal patterns in failing tests. For example, tests that produce different errors across runs may signal non-determinism or insufficient assertions.

#4. Testing in Different Test Environments

Run tests in various environments with different configurations or resources. If the results vary, it’s a sign that the tests may not be stable.

#5. Focusing On External Dependencies

Pay special attention to tests that depend on external factors. These may include API, databases, file systems, etc. These tests are more prone to unstable behavior. Potential failures may be triggered by issues with the external system.

#6. Using Specialized Tools

The CI\CD pipeline is an ideal place to spot flaky tests, as it tracks the success and failure history of individual tests over time. Many CI\CD tools also offer additional plugins designed to monitor instability.

Modern test management systems like Testomat.io can also assist in detecting and diagnosing flaky tests. We’ll dive into the platform’s capabilities for this later.

#7. Manual Checks 

If test flakiness is still not obvious, you can try to detect potential flaky test cases manually. To do this, check the test codebase, evaluate the likelihood of race conditions, and analyze the test logic. In other words, assess the presence of any instability causes we mentioned earlier.

These reliable strategies will help you identify flaky tests. Why is this crucial for project success? We break it down.

The Importance of Flaky Test Detection

Test instability is an issue that many teams face. The results of a recent study showed that 20% of developers detect them in their projects monthly, 24% weekly, and 15% daily. Interestingly, 23% of respondents view flaky tests as a very serious issue.

Here are several reasons behind this perspective, highlighting why it is crucial to identify and address instability promptly:

1. Slowing down the development process and increasing project costs. Unreliable test results prevent teams from progressing to the next development stage. They require manual checks, repeated executions, or extra steps to pinpoint and fix errors, consuming valuable time and resources.
2. Decreasing the effectiveness of test automation. Flaky test outcomes provide little useful information, leading to a loss of trust in the entire test suite. Over time, teams may begin to disregard test results, undermining the purpose of continuous integration systems.
3. Inconsistent feedback. Instability in tests results in inconsistent feedback on the quality of the application code. Developers fail to get an accurate picture of the situation, which delays problem identification and resolution.
4. Decreased team performance. Frequent failures can negatively impact team morale, leading to diminished productivity, communication, and motivation. This ultimately affects the quality of the final product.
5. Challenges in identifying true errors. Flaky tests in the test suite, may cause developers to mistakenly attribute all failures to these inconsistencies, overlooking real issues in the codebase. As a result, these problems remain undetected, accumulate, and create major challenges in diagnosing and resolving them.

Flaky tests disrupt the software development process in many ways. From increasing the duration of project work to worsening the overall atmosphere within the team. This is why it is important to identify and eliminate them as they arise.

How to Measure and Manage Flaky Tests?

The initial step in effectively managing flaky tests is to evaluate their frequency and the impact they have. This can be done through different methods:

• Analyzing test run history. Review the test execution history in your CI\CD pipeline or version control system. This will help identify the number of tests that periodically change their pass/fail status regardless of code modifications.
• Evaluating failure frequency. Track how often tests fail under varying conditions, such as in specific testing environments.
• Using test automation metrics. To gauge the extent of the issue, calculate the flakiness rate. This metric represents the percentage of test runs that produce unstable results. It is calculated using the following formula:

• Applying statistical methods. For example, you can use the Standard Deviation/Variance measurement method. If there is no instability in a specific test suite, the standard deviation will be zero.
• Using specialized tools. Some modern platforms enable teams to optimize their testing efforts by analyzing test result trends and helping to identify and manage flaky tests.

Test Management AI-Powered Solution for Flaky Tests Detection

Test Management System testomat.io is a powerful TMS that offers its users advanced capabilities for working with automated tests. One of these features is advanced test analytics, offered through a Test Analytics Dashboard with user-friendly widgets.

One of the key widgets in the system is Flaky Tests. It allows testers to easily track tests with inconsistent results and make decisions about fixing them.

Let’s take a closer look at the algorithms used to detect flaky tests in Testomat.io. On what basis can a test be added to this list?

To identify instability, the system calculates the average execution status of a specific test. The following parameters are used for the calculation:

• Minimum success threshold. The minimum acceptable percentage of a “pass” status, which can be considered an indicator of instability.
• Maximum success threshold. The maximum acceptable percentage of a “pass” status, which can be considered an indicator of instability.

Let’s consider how the system’s algorithms work with a practical example.

Set success thresholds:

• Minimum – 60%.
• Maximum – 80%.

Suppose a test was run 18 times. Out of these, 12 runs were successful. So, its success probability = 66%. We see that the obtained result falls within the specified range. Therefore, the test will be considered unstable.

Note: To calculate the passing score, the data from the last 100 test runs are considered.

After displaying the table with flaky tests, users can perform the following actions:

• Sort with one click. To do this, click on one of the required columns – Suite, Test, Statuses, or Executed at.
• Filter by execution date, priority, tags, labels, and test environment.
• Change the order of columns for easier data analysis.

So, we have learned how to detect flaky tests, assess their impact on development quality, and manage them with specialized tools. Let’s move on to methods for addressing the problem.

How to Maintain Flaky Tests?

Effective maintenance of flaky tests involves several stages. Together, they allow you to fix existing instability, understand its cause, and prevent it in the future.

Root Cause Analysis

Identify the cause of instability. The most common causes include resource unavailability, external dependencies, errors in test code, or race conditions.

Fixing Flaky Tests

After pinpointing the cause of instability, take corrective measures to eliminate it. These may involve:

• Ensuring test idempotency. Tests should be designed to run independently, without relying on previous executions to maintain consistency.
• Implementing synchronization mechanisms. This is necessary so that tests fail when race conditions or system delays occur.
• Simulating external dependencies. If the cause of instability is a test’s dependency on external services, it is advisable to use stubs. They will simulate the dependency.
• Stabilizing the test environment. It is crucial to ensure maximum stability and predictability of the environment. One option is to use containerization.
• Improving the quality of flaky test cases. Control test logic and cover as many system or function behavior scenarios as possible.

Isolation and Prioritization of Problematic Tests

This step involves categorizing flaky tests by severity. If a test frequently fails, the issue should be addressed as a priority.

The most unstable tests should be isolated or temporarily removed from the overall test suite. Alternatively, use a relevant tag to mark such test cases. This way, you can minimize the impact of flaky tests on overall test results.

Continuous Monitoring of Tests and Team Training

Even after eliminating instability, continue to monitor your test sets continuously. This will allow you to:

• ensure the effectiveness of the fixes made;
• prevent flaky tests from reappearing;
• maintain a feedback loop.

It is also important to provide ongoing training for testers throughout the project. This will help them write reliable, stable tests, including:

• correctly handling external dependencies;
• considering all possible function behavior scenarios;
• following test isolation methods;
• avoiding race conditions.

Effective maintenance of flaky tests includes identifying their causes, working on their elimination, and subsequently monitoring the quality of test cases. Combined with continuous improvement of test automation skills, your team will achieve good results in solving this problem.

Summary: Best Practices for Minimizing Flaky Tests

Flaky tests are a serious problem faced daily by many development teams. To bring a quality software product release closer, it is recommended to focus on reducing their number. How can this be done?

• Ensure test isolation. Tests should not depend on the state of previous tests. It is also important to test in isolated environments. Containers or virtual environments are suitable.
• Avoid tests that rely on time. Do not rely on waiting for a fixed amount of time. Instead, use timeouts or explicit waits.
• Simulate external dependencies. If a test depends on external services, use mocks and stubs. Instead of databases and API, you can use mocking libraries.
• Use reliable test data. It should be predictable. Avoid depending on dynamic data, as any changes to it can cause instability.
• Ensure reliable synchronization. Parallel test execution should be carefully managed. Use locking mechanisms like semaphores or queues to ensure tests run consistently and prevent race conditions.

Implementing these strategies will help minimize the chances of instability in software testing for your project. As a result, your team will save time and resources that would otherwise be spent on fixing it.

The post Overcome Flaky Tests: Straggle Flakiness in Your Test Framework appeared first on testomat.io.