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.

It is a powerful framework offering a variety of features, such as annotations, that enable running test suites in an accurate, organized, and efficient manner. Let’s find out more information about TestNG’s annotations, their types, lifecycle and hierarchy, reveal their advantages, and disadvantages in the article below:

What is the TestNG Framework?

Developed on the same lines as JUnit and NUnit, TestNG is an open-source test automation framework for Java, which is suitable for unit testing, integration testing, and end-to-end testing. The ‘NG’ combination of letters means Next Generation.

However, in 2025, JUnit 5 is generally regarded as the more modern and future-ready tool compared to TestNG, especially for starting new projects. JUnit 5 continues active development with regular updates and improvements.

TestNG framework was created to make automated testing simpler and more effective thanks to diverse features and capabilities, which include grouping, assertions, simultaneous test execution, parameterized testing, test dependencies, annotations, and reporting.

Applying its useful functionality, especially annotations, during QA testing enables testers to easily organize, schedule, and execute tests. Thanks to its stability, it remains in demand for complex enterprise testing scenarios.

To start, let’s first clarify some essential terminology which relates to TestNG’s annotations.

→ Suite. A suite consists of one or more tests.
→ Test. A test consists of one or more classes.
→ Class. A class consists of one or more methods.

What are TestNG Annotations?

In TestNG, you can use annotations to identify tests, set priorities, and configure other aspects of how the tests should be run. Aimed to serve different purposes, annotations are lines of source code, which have been included in the program or business logic to control the flow of methods in the test script.  They are preceded by a @ symbol, which allows performing some Java logic before and after a certain point.

TestNG Annotations Hierarchy and Lifecycle

In this framework, there is a lifecycle of annotations that helps teams organize and execute test methods in a logical order. These lifecycle annotations are mainly the before and after annotations that are used to execute a certain set of code before and after the execution of actual tests.

These lifecycle methods are used to basically set up test infrastructure before the start of test execution and then to cleanup any of these things after the test execution completes. In the picture below, you can see that the method annotated with @BeforeSuite will be executed first, whereas the method annotated with @AfterSuite will be executed last. Below, you can see the lifecycle and hierarchy of TestNG annotations

Different Types of TestNG Annotations

Here you can find the TestNG annotations list along with TestNG annotations with examples:

1. @Test TestNG is told to execute methods as standalone and separate test cases. Using this method, extra characteristics can be detailed, and tests can be turned on/off.
2. @BeforeSuite This method will execute before the entire suite of tests. For example, it can be useful for one-time setup tasks, such as initializing a database connection or setting up a global test environment.
3. @AfterSuite This method will execute after the entire suite of tests. It makes it ideal for global cleanup operations, like closing database connections, tearing down test environments, or generating final reports.
4. @BeforeTest This method will run before the execution of all the @test annotated methods inside a TestNG Suite. It is suitable for setting up configurations, which are specific to a particular test run. For example, initializing a browser instance.
5. @AfterTest This method will run after the execution of all the @test annotated methods inside a TestNG Suite. It is a good fit for specific test cleanup tasks – closing the group’s browser instance or clearing test run data.
6. @BeforeClass This method will execute before each test class. It is a good fit for setup tasks common to all tests in that class. For example, loading configuration files or initializing a reusable class object.
7. @AfterClass This method will run after each test class. It is suitable for class-level cleanup tasks, such as releasing resources or shutting down objects created for the entire class.
8. @BeforeMethod This method will execute before each test method. It is suitable for initializing a fresh browser session, logging in a user, or resetting test data before each @Test method runs.
9. AfterMethod This method will execute after each test method. It can be used for logging out a user, closing a browser session, or cleaning up test data.
10. @BeforeGroups This method will execute right before the first test method of a specific group or set of groups, which can be smoke, regression, begin. It is perfect when you need to perform setup tasks, which are common for a collection of related tests.
11. @AfterGroups This method will execute once, after all test methods belonging to specific groups have finished running. It can be applied as an ideal option for performing shared cleanup tasks for a collection of related tests.

Test annotations in TestNG have multiple attributes, which can be used for the test method and help define tests and provide clarity in terms of TestNG annotations order of execution used in the TestNG class. These attributes are:

• alwaysRun: always executes, even if its dependencies or preceding methods fail.
• dataProvider: specifies the name of a method that provides data for the test method.
• dependsOnGroups: specifies group names whose methods must run and succeed before this test method (or class) executes.
• dependsOnMethods: makes certain the tests execute only if its specified dependent method successfully runs, otherwise it will be skipped.
• description: describes the test method briefly.
• enabled: the test method or class’s tests are skipped if false. The default is true.
• expectedExceptions: indicates that a test method is required to throw the specified exception.
• groups: groups test methods focusing on a single functionality.
• invocationCount: defines the number of times a test method should be executed.
• priority: defines the order of execution of test cases.
• successPercentage: sets the failure percentage for tests that run multiple times.
• timeOut: defines the time a particular test/tests should take to execute.

Why Teams Use Annotations in TestNG

• Thanks to TestNG annotations execution order, teams are in the know about a clear lifecycle of tests, having specific steps, which have been clearly defined, for everything that happens before, during, and after each test or group of tests is executed. 
• Teams can categorize tests into logical groups in order to run only smoke or regression tests. 
• With parameterization, teams can execute the same testing logic multiple times with different sets of data to improve test code reusability and eliminate the need to write separate test methods for each data variation.
• Strongly typed annotations allow teams to get immediate feedback on incorrect configurations and fix problems with their setup before the tests even have a chance to run.
• When marking methods with specific annotations, every team member has a clear understanding of what the purpose is and can maintain and upgrade tests over time.
• When teams use annotations, there is no need to extend any Test class like JUnit.

How To Work With TestNG Annotations: Key Steps

Before using TestNG’s annotations, you need to take into account the following prerequisites:

• You need to use an IDE like Eclipse or IntelliJ for easier development of tests. Anyway, our example TestNG project is implemented here with Visual Studio Code.
• JDK version should be compatible with TestNG and configured.
• You need to create or launch the Java project where you’ll be developing and running tests.
• You need to include a Maven/Gradle equivalent dependency or TestNG’s JAR file in your project’s build path to make the annotations available.
• You need to create and configure testng.xml to fully utilize features like suites, tests, groups, and parallel execution that interact with various annotations.
• If necessary, you can get ready to include TestNG tests in your CI\CD workflow.

# 1 Step: Set up environment

Before starting a test framework, check the Java version which runs TestNG tests via Maven to ensure project stability and compatibility for proper execution and builds.

java -version
mvn -v

You may review them in the documentation by following the next links Java, 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.

#2 Step: Create & configure your TestNG 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-TestNG-framework \
-DarchetypeArtifactId=maven-archetype-quickstart \
-DinteractiveMode=false

A brief parameter explanation:

• -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

#3 Step: Setting up Configuration

First, the test automation engineer is adding dependencies via Maven in the pom.xml file:

<dependencies>
  <!-- TestNG -->
  <dependency>
    <groupId>org.testng</groupId>
    <artifactId>testng</artifactId>
    <version>7.9.0</version>
    <scope>test</scope>
  </dependency>

  <!-- Selenium -->
  <dependency>
    <groupId>org.seleniumhq.selenium</groupId>
    <artifactId>selenium-java</artifactId>
    <version>4.20.0</version>
  </dependency>
</dependencies>

#Step 4: Organizing TestNG framework structure

At this step, teams need to organize test suites based on their testing needs, so it is suitable to define the tree structure of our project now.

Demo-TestNG-Login-Project/
├── pom.xml
├── testng.xml
└── src
    └── test
        └── java
            └── com
                └── example
                    └── tests
                        ├── BaseTest.java
                        └── LoginTest.java

The testng.xml file in TestNG serves as an entry point for executing TestNG tests in a controlled and flexible manner. Instead of running all tests in classes, configure it to include or exclude specific groups.

Sample testng.xml Suite File:

<!DOCTYPE suite SYSTEM "https://testng.org/testng-1.0.dtd" >

<suite name="Login Suite">
  <test name="Login Tests">
    <classes>
      <class name="com.example.demo.LoginTest" />
    </classes>
  </test>
</suite>

Explanation:

• <suite>: Defines the whole suite of tests. You can give it a name.
• <test>: It is a logical container for a group of test classes.
• <classes>: Contains all the test classes to be executed.
• <class>: Specifies the fully qualified name of the test class

#3 Step: Writing Tests

At this step, teams can utilize the @Test annotation in TestNG to write tests for Java test classes with annotated methods.

File LoginTest.java showcases test automation logic, the example verify login in the system:

package com.example.demo;

import org.openqa.selenium.By;
import org.openqa.selenium.support.ui.WebDriverWait;
import org.openqa.selenium.support.ui.ExpectedConditions;
import org.testng.Assert;
import org.testng.annotations.Test;

import java.time.Duration;
import java.util.List;

public class LoginTest extends BaseTest {

    @Test
    public void loginWithValidCredentials() {
        driver.get("https://www.saucedemo.com/");

        // Wait for the username field to be visible
        WebDriverWait wait = new WebDriverWait(driver, Duration.ofSeconds(10));
        wait.until(ExpectedConditions.visibilityOfElementLocated(By.id("user-name")))
                .sendKeys("standard_user");
        driver.findElement(By.id("password")).sendKeys("secret_sauce");
        driver.findElement(By.id("login-button")).click();


        // Wait for product titles to be visible and verify
        wait.until(ExpectedConditions.visibilityOfElementLocated(By.cssSelector("[data-test='title']")));
        List<String> productTitles = driver.findElements(By.cssSelector("[data-test='title']"))
                .stream().map(element -> element.getText()).toList();
        Assert.assertFalse(productTitles.isEmpty(), "No product titles with data-test='title' are visible on the page.");

    }

    @Test
    public void loginWithInvalidCredentials() {
        driver.get("https://www.saucedemo.com/");

        // Wait for the username field to be visible
        WebDriverWait wait = new WebDriverWait(driver, Duration.ofSeconds(10));
        wait.until(ExpectedConditions.visibilityOfElementLocated(By.id("user-name")))
                .sendKeys("invalide_user"); // Using a likely invalid user for testing
        driver.findElement(By.id("password")).sendKeys("secret_sauce");
        driver.findElement(By.id("login-button")).click();

        // Wait for product titles to be visible and verify
        wait.until(ExpectedConditions.visibilityOfElementLocated(By.cssSelector("[data-test='title']")));
        List<String> productTitles = driver.findElements(By.cssSelector("[data-test='title']"))
                .stream().map(element -> element.getText()).toList();
        Assert.assertFalse(productTitles.isEmpty(), "No product titles with data-test='title' are visible on the page.");
    }
}

The BaseTest.java class serves as a foundational setup and teardown class for your Selenium TestNG tests. Particularly, it inherits common browser behavior from the LoginTest file.

package com.example.demo;

import org.openqa.selenium.WebDriver;
import org.openqa.selenium.chrome.ChromeDriver;
import org.testng.annotations.*;

public class BaseTest {
    protected WebDriver driver;

    @BeforeClass
    public void setUp() {
        driver = new ChromeDriver();
        driver.manage().window().maximize();
    }

    @AfterClass
    public void tearDown() {
        if (driver != null) {
            driver.quit();
        }
    }
}

#Step 5: Running TestNG framework tests

At this step, teams can execute the script directly from the IDE or build tool. It can be done by initiating the runner from the command line.

mvn test

# Step 6: Reporting

After being executed, the TestNG tests — would be okay to know their results  The test management system testomat.io generates comprehensive reports (HTML and XML) which provide details on results, including passes, failures, and skipped tests, and include execution time or runtime – how long the tests took to run. Based on that report, teams are in the know about bugs and ready to fix them. Find a Java report GitHub Link with the project details here Take only a few steps to get integrated reporting quickly:

1. Add dependency to pom.xml  with a classifier to align your test framework:

   <dependency>
       <groupId>io.testomat</groupId>
       <artifactId>java-reporter-distribution</artifactId>
       <version>0.6.1</version>
       <classifier>junit</classifier>
   </dependency>

2. Get your API key from Testomat.io (starts with tstmt_)
3. Set your API key as environment variable:

   export testomatio.api.key=tstmt_your_key_here

4. Run your tests – that’s it!

We can see the error details to find out the reason the test failed.

Smart AI-generated Summary Report that appears after each test run — giving you an instant overview without digging into details to save testers, developers, and managers time. Also, provide some value insights and suggestions.

# Step 7: Integrating CI\CD

At this step, teams can configure their CI\CD pipeline to automate TestNG execution on code commits. The pipeline uses these generated TestNG reports to see if the build succeeded. Then it provides immediate feedback on code quality and blocks faulty code from deployment.

Integration into a CI\CD pipeline with testomat.io adds another powerful layer of test orchestration and traceability. @Tags and Labels allow smart running, subsets (e.g., smoke, regression, feature-specific tests) via the CI. When TestNG tests fail in CI, test management software sends notifications to Slack, Jira, email and Microsoft Teams.

Advantages and Disadvantages of TestNG

Focusing specifically on annotations, TestNG is more flexible than the JUnit framework, this is a comparison of pros and cons:

Advantages of TestNG	Disadvantages of TestNG
Annotations are easy to understand.	It takes time to set up this framework.
Easy to group test cases and set timeouts.	Without the need to prioritize test cases on the project, it is not a good fit.
Parallel testing and cross-browser testing are possible with TestNG.	Compared to JUnit, its limited adoption resulted in a smaller pool of experienced specialists.
It supports parameterized and dependency tests.	Requires additional effort to manage complex test dependencies.
Generating HTML Reports by default.	Need some effort to customize for specific project needs.
Organizing tests into suites, groups, and dependents through a hierarchical test structure.	Hierarchical structure can become hard to maintain with large test sets.
TestNG’s listener interface enables the addition of customized setup, takedown, reporting, and cleanup procedures.	Listener implementation can introduce performance overhead.

TestNG Annotations Best Practices

Here are some tips to follow to effectively use TestNG Annotations:

• You should know the specific TestNG annotations sequence for running tests – @BeforeSuite –> @BeforeTest –> @BeforeClass –> @BeforeMethod –> @Test –> @AfterMethod –> @AfterClass –> @AfterTest
• You should correctly choose @Before and @After annotations, which define how often your setup/cleanup processes are going to be performed.
• You should give your @Test methods relevant group names (for example, sanity, regression, integration) to quickly run select tests without altering your code.
• You should keep your @Before and @After methods free of complex app logic to maintain the speed and reliability of tests.

Interesting to read:

• Cucumber BDD framework for API testing with Playwright example
• Playwright Java BDD Framework Tutorial
• Integration between BDD in Jira and Test Management System
• Introduction to Serenity BDD Framework – Tutorial

Bottom Line: What about using TestNG annotations?

With TestNG, teams can make automated tests more organized, readable, and maintainable. Its annotations allow them to organize and control the flow of test cases. When it comes to scaling and executing cross-browser testing across varied web environments, using TestNG annotation in Selenium is a perfect option.

– If you have any questions about annotations? Do not hesitate to contact our specialists.

The post TestNG Annotations Tutorial appeared first on testomat.io.

What is Gherkin?

Gherkin is a simple, human-readable plain language, composed in such a way that anyone can understand the written statements, even those with a limited scope of programming knowledge. Gherkin is used in Behaviour-Driven Development (BDD). In other words, Gherkin is the heartbeat of BDD.

It helps development teams write clear scenarios that describe how software should behave from the user’s perspective, actions are equal – Steps.  This allows both technical and non-technical people to work together and stay on the same page, making collaboration easier and ensuring documentation stays accurate.

Cucumber is the most widely used BDD framework. Some popular ones are Behat, Behave, JBehave, CodeceptJS and Codeception.

Why Grerkin Matters in Behavior-Driven Development (BDD)

• Gherkin encourages test-first thinking. Gherkin encourages writing scenarious early, guiding teams to define expected behavior before writing code. It prevents bugs, not just catches them.
• Shared Understanding Across Teams. Rather than relying on lengthy technical manuals or ambiguous user stories, Gherkin provides a formalized way to describe system behavior through conversational language. This simplicity enables teams to align expectations early on, especially who is involved in the development process — not just engineers, but also product owners, business analysts, and QA specialists. It occurs during the Three Amigos sessions, where developers, testers, and business stakeholders collaborate to define what the Definition of Done(DoD) looks like.
• Living Documentation. Gherkin plays a vital role in Behavior-Driven Development by transforming complex requirements into simple, structured documentation.
• Enhancing collaboration. Gherkin, by acting as a living specification, reduces misunderstandings, improves test coverage, and keeps requirements tightly coupled with automated validation. It bridges the gap between business intent and technical implementation, making BDD not just possible but practical.

In short:

Gherkin makes BDD practical — aligning business goals with technical implementation through clear, collaborative, and testable language.

Gherkin in Agile & BDD Workflows

Gherkin focuses on teamwork, taking small steps, and getting regular feedback. This method fits well with Agile practices.

In Agile teams, Gherkin helps connect business and tech teams. It helps everyone understand user stories and acceptance criteria together. This way, Agile teams can deliver value bit by bit and adjust to new needs quickly. Gherkin serves well in Agile and BDD workflows:

• User stories → drive features
• Scenarios in Gherkin → describe behavior of these features
• Automation tools like Cucumber, SpecFlow, or Behave → link Gherkin to real tests

This creates a shared understanding between PO, Dev, and QA. Let’s break it down more:

For example, BDD with Gherkin could also be implemented like this in the Agile Cycle:

As you can see from our visual, the main differences between BDD Agile Workflow and traditional imperative testing are:

→  More traditional Agile testing workflow is more focused on execution rather than behavior.
→  BDD uses Gherkin, a declarative DSL that emphasizes specific behaviors.
→  BDD Agile promotes a shift-left approach. With Gherkin-based acceptance criteria defined upfront, teams embed quality into development before it starts.

Basic Structure of a Gherkin Scenarios

A Gherkin .feature file is structured to describe software behavior using scenarios. It begins with a Feature keyword, followed by a description of the feature. Each scenario within the feature outlines specific examples of how the feature should behave, using keywords like  Given, When,  Then to define the context, actions, and expected outcomes. Here is a breakdown of the structure:

For example, the User Login feature describes how users access the system through the login page. If they enter the correct username and password, they’re taken to the home page. If the login details are incorrect, the system shows an error message to let them know something went wrong.

Feature: User Login
As a user, I want to be able to log in to the system.

  Scenario: Successful Login
    Given the user is on the login page
    When the user enters valid credentials
    Then the user should be redirected to the home page

Features and Scenarios Explained

At the center of Gherkin are Features and Scenarios. A Gherkin feature points out a specific ability of the software. It comes with related test cases and explains how a feature should work in different situations.

• Scenarios serve as test cases.
• Each feature has different scenarios.
• These scenarios imitate how real users behave.
• They explain certain actions and the results you should expect.
• They offer a simple guide on how a system should react to various inputs or situations.

To avoid repeating tests for similar tasks with different data, Gherkin uses Scenario Outlines These are like templates. They allow testers to run the same scenario many times with different data. This way, testers can check everything well while keeping the code simple and effective.

Step Definitions: Given, When, Then

Gherkin syntax uses a simple format called Given-When-Then. This format helps to describe the steps for each test case. It makes it easy to understand the setup, the actions taken, and the expected results in a scenario.

• Given shows where the system starts. It describes what the system looks like before anything happens. This step makes sure the system is prepared for what follows.
• When  tells us about the action that makes the system respond. It includes what the user does or what takes place in the system that changes how it works.
• Then shows what should happen after the action in the When step. It explains how the system should behave after that action, so we can check if it works as intended.

* Take a closer look at this extended code snippet — note how we marked Given, When,  Then as Facts, Past, Present, or Future statements for a better understanding of context.

# Login Functionality

Background:
Given the following user registration schedule:
  | Username | Password | Status   |
  | user1    | Pass123  | Active   |
  | user2    | Test456  | Inactive |
And user1 is a Frequent Flyer member     # <-Fact    

Scenario: Successful login with valid credentials
Given user1 has purchased the following credentials:          # <-Past 
  | Username | Password |
  | user1    | Pass123  |
When the user submits the login form                   # <-Present
Then the user should be redirected to the dashboard     # <-Future

What is an Effective Gherkin Test?

Creating good Gherkin tests isn’t just about understanding the syntax. You also need to follow best practices. These practices make the tests clear, simple to update, and dependable.

It is important to write tests that are short and clear. These tests should show how real users interact with the system. Use simple words and avoid technical terms. Focus on one part of the system for each test. This way, your Gherkin tests will be better and easier to handle.

Advantages of Using Gherkin

Gherkin is a powerful communication tool that brings developers, testers, and business stakeholders onto the same page. By describing behavior in plain language, Gherkin helps teams define, automate, and validate application functionality with less friction and more clarity. Below are the key advantages of using Gherkin in modern Agile and BDD workflows.

Better Communication Across the Team

Since Gherkin uses plain English, everyone, whether they are technical or not, can understand what the software is supposed to do. This helps developers, testers, and business stakeholders stay on the same page and reduces the chances of misunderstandings. It also keeps the focus on the user experience, which leads to more useful related features.

Documentation That Stays Current

Gherkin scenarios are tied directly to automated tests, which means they reflect the software’s real behavior, not just how it was supposed to work. You are not stuck with outdated documents, and your team always has a reliable reference point. These scenarios are version-controlled and stored with the code, so everyone can access and update them easily.

Faster Development and Better Testing

Because Gherkin scenarios can be turned into automated tests, they help speed up testing and give quick feedback during development. Writing tests before building features also helps catch issues early. Since Gherkin fits well with Agile practices, it supports frequent changes and constant improvement.

Long-Term Efficiency and Better Test Coverage

Gherkin scenarios are easy to update as requirements change, which helps lower the time and cost of maintaining tests. They also encourage teams to think through different use cases and edge cases, improving overall test coverage. The structured format allows you to reuse steps across different tests, reducing repetition and making your test suite easier to manage.

BDD Test Case Writing Pitfalls to Avoid: How To Solve Them?

Gherkin makes it easier for you to write tests. However, there are a few common mistakes to remember. These mistakes can make your test cases less effective

By avoiding these mistakes and using these Gherkin strategies, you can build better and more reliable Gherkin tests. This will improve your testing as well as the quality of the software.

How Is Gherkin Linked to Automated Test Code

The main connection of the language with automated test code is through its syntax. It uses a plain text format, operating such keywords as  Given, When, and  Then which are linked to the corresponding automated code that executes all the required steps. Thanks to it, the language stays abstract and readable, which allows non-technical users to understand the scenarios and technical users to maintain the test code.

Popular Testing Frameworks with Gherkin Support

Gherkin is paired with testing frameworks that interpret and run them — the most well-known being Cucumber, which turns real system behavior into automated BDD tests.

Together, Gherkin and these BDD frameworks simplify test automation, improve collaboration, and create living documentation that evolves with your product. Below is a comparison of popular frameworks that support Gherkin syntax:

Framework	Language(s)	Description
	Java, JavaScript, Ruby, etc.	The most widely used BDD tool; executes Gherkin scenarios directly.
	Python	Lightweight BDD framework for Python projects; uses Gherkin syntax.
	.NET (C#)	Native BDD framework for .NET; integrates tightly with Visual Studio. Unfortunately, now it is not supportedrted already.
	Multiple (Java, C#, JS)	Developed by ThoughtWorks; supports markdown-style Gherkin + plugins.
	JavaScript	End-to-end test framework with Gherkin plugin; integrates with WebDriver.
	JavaScript/TypeScript	Combines Jest’s test runner with Cucumber support for BDD testing.

Requirements for the Test Management System:
What Do True Testers Need?

Every test automation with a language has its own set of requirements in order for the analysis to succeed. Everything starts with defining the Agile roles in BDD. Every project must include a team that consists of:

• QAs
• Dev team
• BA (business analysts)
• PM (project managers) or PO (product owners).

Then, there are technical requirements that must be met by the system for integrating Gherkin naturally. The basic criteria include:

• Gherkin-Friendly Editor. The system should let users write, edit, and manage Gherkin feature files with syntax highlighting and support for key elements like Given, When, Then, tags, backgrounds, and scenario outlines.
• Seamless BDD Tool Integration. It should work smoothly with popular BDD tools such as Cucumber or Behave, making it easy to plug into existing testing workflows.
• Automation & CI\CD Support. The platform should connect with CI\CD tools (like Jenkins or GitLab), allow automated test execution, and display test results directly in the system.
• Test Management & Result Tracking. The system should let you track which scenarios are passing, which ones failed, and how they map to defects or bugs, offering a full picture of test coverage.
• Team Collaboration Tools. It should support multiple users working on the same features, with options for comments, approvals, and version history to review what changed and why.
• Reporting & Dashboards. The platform should offer easy-to-read dashboards that show test progress, coverage, and trends, with filters for tags, features, or test status.
• Gherkin also helps with living documentation. This means that the tests will update when the software updates. This is important for development that happens in steps. Because of this, Gherkin is a great tool for teams that want to be flexible and create high-quality software frequently.

Once these requirements are met, the team can proceed with setting up the testing environment and running the very first check using Gherkin.

Test management system testomat.io meets the needs of modern teams in Behavior-Driven Development (BDD) and makes the testing process more practical and powerful by seamlessly integrating Gherkin-style test cases into your workflow. Testomatio’s BDD-friendly UI supports an advanced Gherkin Editor.

Steps Database allows the reuse of steps and shared scenarios, making collaboration easier across distributed teams. Smart, generative AI analyses existing BDD steps across your project and suggests new ones based on them.

Starting with us, you can easily turn your manual test cases outside with a script into BDD scenarious in a minute.

Drop us a line today to learn how we can help you enhance your BDD testing processes that meet the highest standards, contact@testomat.io

How Do Gherkin Scenarios Work with Continuous Integration (CI) & Continuous Delivery (CD)?

Gherkin scenarios integrate smoothly with Continuous Integration (CI) and Continuous Delivery (CD) pipelines, helping Agile teams deliver high-quality software faster. When used with CI\CD, Gherkin scenarios automatically run each time code is pushed, ensuring that new changes do not disrupt existing functionality. This provides early detection of issues, minimizes risks, and ensures that only stable, verified features are deployed. Here is how Gherkin enhances CI\CD practices:

• Automated Test Execution. With Gherkin scenarios written in a BDD framework like Cucumber, tests can be automatically executed as part of the CI pipeline. When developers push changes, the pipeline runs these scenarios, validating that new code aligns with predefined acceptance criteria and doesn’t introduce regressions.
• Immediate Feedback Loop. CI\CD practices emphasize frequent deployment and testing to provide immediate feedback. Gherkin’s clear, business-oriented scenarios allow both technical and non-technical team members to understand results, facilitating prompt discussions and decisions.
• Living Documentation in Real Time. Gherkin scenarios act as living documentation within a CI\CD environment. As the software evolves and scenarios pass or fail, the documentation reflects the latest behavior of the system. This keeps the whole team aligned on current functionality and prevents outdated documentation from leading to misunderstandings.
• Continuous Quality Assurance. By integrating Gherkin-based tests into the CI\CD pipeline, teams can enforce continuous quality checks. Each build goes through comprehensive Gherkin-based testing, ensuring that any issues are detected early and resolved before deployment.

Conclusion

Gherkin is very important as it helps teams work together better and be more efficient. Gherkin has a simple structure and is closely connected with Cucumber, but it is not the same. This connection allows teams to speed up their testing and improve BDD, which means Behavior-Driven development.

Writing clear Gherkin tests and using good practices is key to avoiding common mistakes. This helps make software projects successful. There are many examples in the real world that show how helpful Gherkin can be. It is flexible and is valuable in several industries. You should use Gherkin to make your testing better. Keep learning and creating!

The post What is Gherkin: Key to Behavior-Driven Development appeared first on testomat.io.

Did you know that GenAI-based tools will write 70% of software tests by 2028, based on IDC, and the AI use in software testing helps reduce test design and execution efforts by 30% according to Capgemini?

This article dives into what AI in software testing is and why to use artificial intelligence in QA testing – and offers actionable tips to level up your entire testing process and be in sync with the current AI software engineering practices.

Role AI In Software Testing

Artificial intelligence and machine learning algorithms in software testing enhance all stages of the Software Testing Life Cycle (STLC). The adoption of AI in Quality Assurance continues to rise because it boosts productivity while automating processes and enhancing test accuracy.

Topics handpicked for you

• AI Model Testing
• AI Unit Testing: A Detailed Guide
• Role AI testing, in particular, ChatGPT in Software testing

The traditional testing approach depends on manual test script development, while an AI-powered system learns from data to generate intelligent decisions.

Knowing that, AI test automation tools are a good fit for identifying critical code areas, generating test case recommendations, and automatically developing test cases. What’s more, these tools adapt to user interface modifications without requiring regular updates and maintain their ability to detect and test interface elements even when buttons move or their labels change. This is relevant when considering visual testing codeless tools.

The current software testing industry heavily depends on AI to speed up operations while improving test quality. The artificial intelligence system helps create tests and run them while analyzing results and adapting to new changes through learned knowledge.

Only by opting for AI in software testing can you enhance testing speed, provide smarter and scalable solutions, and decrease the need for test maintenance while speeding up testing operations. When integrated, teams achieve faster software releases with increased confidence through their work and the efforts they put in alongside the artificial intelligence capabilities.

What is AI Testing?

When we talk about AI testing, we mean the use of Artificial Intelligence(AI) and Machine Learning(ML) technologies in the testing process, which helps improve its speed, accuracy, and efficiency. Both are becoming essential in modern QA strategies.

In contrast to classical testing, applying AI-based approaches promotes intelligent analysis and processing of testing data and previous test cycles, fosters test case selection and test case prioritization, and offers the detection of UI inconsistencies and much more. Smart software testing solutions, like predictive analytics, pattern recognition, and self-healing scripts — improve overall software quality.

Manual Software Testing VS AI in Software Testing

It is not a secret that conducting the traditional software testing process requires significant time and efforts, which makes QA and testing teams manually design test cases, make updates after recent code changes, or inadequately simulate real user interactions.

Of course, they can create automated scripts for some components where it is possible, but they also require continuous adjustments. AI in software testing has changed the situation and made it more reliable, efficient, and effective (of course, when following the right approach and using the right AI testing tools).

Thanks to it, teams can automate many repetitive and mundane tasks without risk, more accurately identify and predict software defects, and speed up the test cycle times while improving the quality of their products. Furthermore, it helps them make adjustments before deployment and predict areas, which are likely to fail, reducing the chances of human error and overlooked issues.

Summary

Manual testing focuses on human insight and intuition, while AI in testing brings speed, adaptability, and data-driven intelligence to the QA process.

When to Use What?

→ Manual Testing: Best for exploratory testing, usability evaluation, edge cases, or when AI setup is not justified.
→ AI Testing: Ideal for repetitive tasks, large-scale regression, risk prediction, and accelerating Agile, CI\CD workflows.

Let’s dive deeper into the use cases of how AI is used in real testing workflows.

Current Landscape: How to Use AI in Software Testing?

So, you can find popular use cases to apply generative AI in software testing below:

Test generation & Accelerated testing

Testers face a long and tiresome process of creating test scenarios. Thanks to gen AI in software testing, this process has changed. Now, AI-based tools can be applied for the generation of tests.

They analyze your codebase, requirements, user acceptance criteria, and past bugs to automatically create new tests, which will cover a wide array of test scenarios and detect edge cases that human testers might miss, and accelerate the testing process.

Low-code testing | No-code testing

The combination of Low/No-code testing with artificial intelligence allows testing teams to create and execute tests quickly and reduce the need for human resources and time. Even non-technical team members can actively participate in test automation and faster feedback loops, which contribute to more stable software releases.

Test data generation

With AI test data generation, QA teams can get new data that mimics aspects of an original real-world dataset to test applications, develop features, and even train ML/AI models. It helps them achieve better test results, AI model predictability and performance.

AI can automate the generation of test data in several ways:

→ To create datasets that cover a wide range of scenarios, user behaviors and vary inputs.
→ To produce anonymized data with key features, without personal identifiable information.
→ To generate test data that closely reproduces user actions and situations.
→ To create data for rare and complex testing scenarios which are difficult to capture with real-world data alone.

Test report generation

Artificial intelligence reduces time on manual report creation. With AI-based algorithms, you can automate various aspects of report creation and quickly build test reports which help teams in the following situations:

• Investigate the failure reasons after the completion of tests.
• Visualize test results and provide the visual indicators of test performance.
• Configure simple and understandable reports for your teams
• Analyze the roots of failure and suggest possible solutions for resolution.

Bug Analysis & Predictive Defects

Based on past test data and pattern recognition, AI-based tools can predict which line of code is likely to fail. This helps testers concentrate their efforts on high-risk areas to boost the chances of detecting defects early in the automation testing process. Thanks to predictive defect analytics, test case prioritization, and bug identification are getting more efficient and quicker in the testing process.

Risk-based testing

Risk-based testing focuses on areas that pose the greatest risk to the business and the user experience. With AI-based tools, teams can reveal the “risk score” for each feature or workflow and increase test coverage for them. AI helps them prioritize testing efforts based on potential risks and balance the use of resources, concentrating on areas with the greatest potential impact. More information about risk-based testing can be found here.

Why Teams Need AI in Software Test Automation

Without fear of oversimplifying, the biggest challenge that testing teams face is automating repetitive testing tasks that require a lot of time to perform. However, AI in testing is that not only solves this key problem. In fact, it can handle other, no less pressing issues. Let’s discover why teams need AI in software test automation:

• Teams need Artificial intelligence to automate similar workflows and orchestrate the testing process.
• Teams need Artificial intelligence to highlight which test cases to execute after changes in the program code of some features to make sure that nothing will break in the app before its release.
• Teams need Artificial intelligence to know what test scripts to update after changes in the UI.
• Teams need Artificial intelligence to know what feature or functionality requires immediate attention.
• Teams need Artificial intelligence to expand test coverage by revealing edge cases and allocate testing resources efficiently.
• Teams need Artificial intelligence to reduce delays in regression testing and find possibilities to speed up testing.

However, it is important to mention that artificial intelligence in testing cannot deal with situations not included in the training data or replace human judgment.

AI in Software Testing Life Cycle

Artificial intelligence can be integrated into the key stages of the testing lifecycle – planning, design, execution, and optimisation. Below, you can find more information about each stage:

#1: Test Planning

With artificial intelligence, the requirement documentation, user stories, and specifications for the testing process can be processed in seconds. Depending on the information that AI will gather from them, it can convert them into testable scenarios.

When implementing this approach, teams can remove the possibility of errors during the test creation process and reduce the manual efforts, which are required to analyze large documentations and identify inconsistencies at the earlier phases of the development cycle.

Also, AI-based algorithms can go through the historical project data and predict high-risk areas of the applications that are more prone to failure and repurpose all the testing efforts accordingly.

#2: Test Design

Using AI, teams can automatically create the tests depending on the requirements and user behaviors, and suggest areas of the application that require further testing. With accurate and varied test data, teams can also ensure that all the tests that occur in real-world scenarios are met. Artificial intelligence will also collect that data to do variability and compliance testing around GDPR, so you are compliant with user privacy and security.

#3: Test Execution

AI’s goal is to minimize the time required for test execution and improve real-time decision-making about testing strategy. Teams can integrate AI to create AI-driven tests that automatically find the UI changes and update the locators within the tests, which leads to the scalability of tests and their dependability as well. Furthermore, teams can apply AI to understand the optimal execution strategy – they are in the know which tests to run and on which platform/environment, taking into consideration previous testing results and current changes within the application infrastructure.

#4: Smart bug triaging

If bugs are not recorded, mapped, and reported properly, then the time and efforts involved in identifying the root cause and rectifying them are much higher. Thanks to AI-based natural language processing techniques, teams can intelligently address bug triage.

Artificial intelligence can automate the creation, update, and follow-up of bug reports and get a full picture of your tests’ performance. By spotting flaky tests and using historical data, it identifies the best tests for the task instead of wasting resources on unnecessary testing.

#5: Self-healing tests

Traditional automated testing requires extensive time to maintain scripts because of UI updates and functional changes. The testing scripts frequently fail and need human intervention for updates when working in environments with dynamic development. AI-based algorithms can be utilized for autonomous issue detection, precise test case generation, and software change adaptation without requiring human involvement.

#6: Test Reporting

With AI-powered reporting, testing teams can generate detailed and actionable testing dashboards with detailed information and recommendations using AI. It also speeds up the defect triaging and helps teams define the resolution strategy to get rid of bugs in less time before the software system is deployed. In the long run, it improves the visibility across multiple teams and enables them to make faster decisions to reduce the feedback loops and also the production cycle.

#7: Test Execution Optimization (Maintenance)

AI-powered systems learned from past executions and user interfaces help teams identify the flaky or low-value tests and give recommendations – whether to remove or refactor them in order to meet the requirements. Thanks to AI, teams can find failures and link them back to the code changes, infrastructure issues, or integration errors, and minimize the overall troubleshooting steps.

Test Management AI Solves Software Testing Extra Tasks

Flaky test detection

When your test suite grows, flaky tests become a common problem for many development and QA teams. If left unchecked, they lead to false positives (tests that pass but shouldn’t) or false negatives (tests that fail but shouldn’t). Thanks to AI-based tools, teams can identify and score flaky tests, and then define which tests to re-run or skip and which ones mean the code needs fixing.

Code coverage analysis

In testing, code coverage quantifies how much of the source code is exercised by the test suite running. Teams can also measure what percentage of code is executed during those tests and understand how effective the testing strategy is.

If code coverage is high, it indicates that a larger portion of the code has been tested under various conditions. With the integration of AI, teams can get a full coverage review of code, study the app code thoroughly, and suggest tests achieving over 95% test coverage. It prevents the likelihood of any defects escaping into production due to insufficient tests.

Regression automation

With AI-based regression testing tools, teams can adapt to changes in scripts and prioritize tests. Artificial intelligence can manage large numbers of regression tests by automatically detecting changes and identifying areas that are likely to be most affected by new updates. By analyzing defect patterns, user behavior, and historical data, Artificial intelligence helps identify risk-prone areas and provides thorough testing of critical functionalities, saving manual effort and accelerating test cycles.

Test orchestration

With orchestration in place, teams can perform several rounds of testing within an extremely limited amount of time and achieve the desired levels of quality. Thanks to Artificial Intelligence test orchestration, it optimizes the selection of tests and intelligently prioritizes the right ones for execution based on code changes and risk, rather than simply running everything.

With its help, teams can dynamically manage the execution of tests across diverse environments and validate the reports for successes/failures, including the report on smoke testing and performance testing, and configure the right capacity of resources needed.

For example, the AI Testing Assistant from testmat.io can help QA teams make decisions regarding determining the project’s release readiness or assessing its quality.

Benefits of AI in Software Testing

AI brings significant improvements to how software is tested, especially in Agile, Shift-left and fast-paced CI\CD, DevOps, TestOps modern methodologies. Below are the key benefits:

Detailed some benefits incorporating AI:

• Visual AI Verification. With AI, teams can recognize patterns and images that help to detect visual errors in apps through visual testing, which guarantees that all the visual elements work properly.
• Up-To-Date Tests. When the app grows, it changes as well. Thus, tests should also be updated or changed. Instead of spending hours updating broken scripts of tests, Artificial intelligence can automatically adjust tests to fit the latest version of your application.
• Improved Accuracy and Coverage. By scanning large amounts of data, AI finds patterns and highlights areas that require more attention. It also measures how much of the application is tested and reduces the risk of bugs before production.
• Automation of Repetitive Tasks. Artificial intelligence is helpful when it comes to the automation of repetitive tasks and lets teams focus on the things that need human attention, like exploratory testing.
• Faster Execution of Tests. Thanks to AI in software testing, tests can be executed 24/7, which leads to faster feedback and quicker development cycles.
• Reduced Human Error. When teams do manual testing, it can lead to mistakes. AI changes this situation and does the same work without losing focus, and eliminates bugs caused by missed steps or overlooked details.

Challenges of AI in software testing

Below, we are going to explore the challenges of AI in testing that development and QA teams face when implementing it:

• AI is highly dependent on data and requires quality data to be trained on for producing correct and unbiased recommendations.
• Devs and QA teams need to constantly monitor and validate the data generated by AI, because even a small error may break the existing functioning unit tests.
• Devs and QA teams face difficulties in explaining AI-driven decisions and might cope with the risk of biased AI models.
• It is important to mention that AI is not a full replacement for human testers, but a help for them in automating repetitive tasks, speeding up test execution, and improving accuracy.
• AI implementation requires significant initial setup and continuous learning and updates.
• It produces training complexity and is computationally expensive in the initial phase.

Tips for Implementing AI in Software Testing

Below, you can find some information you need to know to successfully implement AI in testing:

Define Goals

To get started with AI implementation, you shouldn’t forget about setting testing goals. All these questions should be asked and answered from the very beginning:

• Do you need to increase test coverage or test execution time?
• Do you need help deciding on software quality or release readiness?
• Do you need to boost bug triaging?

Choose the Right AI Tool

Taking into account your quality assurance objectives, you need to assess project demands and choose an AI tool that fits your needs and development environment. Don’t forget about usability, scalability, and integration capabilities of the right AI test automation frameworks during the selection process.

Prepare High-Quality Training Data

You need to remember that AI testing success depends on training data quality. For the AI to start providing accurate outcomes, it should be trained on quality datasets which go through iterative data refining steps. You need to establish data policies, standards, and metrics that define how data is to be treated at your organization. Also, you shouldn’t forget to implement data audits, which reveal poorly populated fields, data format inconsistencies, duplicated entries, inaccuracies in data, missing data, and outdated entries to make sure the training data remains high quality.

Incorporate Metrics for AI assessment

You need to establish meaningful success criteria and performance benchmarks aligned with real-world expectations for AI in software testing. With statistical methods and metrics, you can measure the reliability of AI model predictions and its results. Also, you can incorporate human judgment for evaluating AI effectiveness.

Continuous Monitoring and Improvement

For better results, you need to continuously analyze AI testing results and find areas for improvement, audit training data, and adjust artificial intelligence parameters to keep AI as efficient and flexible for software testing as possible.

Wrapping up: Are you ready for AI and software testing?

It is crucial to remember that there is no “one-size-fits-all” solution anywhere, even in testing. Before implementing AI for software testing, assessing artificial intelligence readiness in your organisation is essential. All the current testing processes, team capabilities, and specific QA challenges should be investigated.

Furthermore, you need to discover areas of weakness where AI can help, choose the right tool to address them, and then start integrating it into your process. If you need any help with AI in testing software, our team understands the AI life cycle and is equipped with the AI-based tool you need for an effective and fast AI software testing process.

The post AI in Software Testing: Benefits, Use Cases & Tools Explained appeared first on testomat.io.

To adopt AI-model testing, you need to effectively manage the massive amounts of data generated during the testing process. Furthermore, you need to train AI models using these vast datasets and enable the models to make accurate predictions and informed decisions throughout the testing lifecycle.

In practice, the problem of introducing AI-models into a real business is not limited to new data preparation, development, and training. Their quality depends on the verification of datasets, testing, and deployment in a production environment. When adopting the concept of MLOps, QA teams can increase automation, improve the AI-model quality, and increase the speed of model testing and deployment with the help of monitoring, validation, versioning, and retraining.

In the article below, we are going to find out essential information about AI-model testing and its lifecycle, reveal popular tools and frameworks, and explore key strategies and testing methods.

What Is an AI Model?

When we talk about AI-models or artificial intelligence models, we mean mathematical and computational programs which are trained on a collection of datasets to detect specific patterns.

In Simple Terms:

AI model is like a trained brain that learns from data and then uses that knowledge to solve real-world problems.

AI-models follow the rules defined in the algorithms that help them perform tasks from processing simple automated responses to making complex problem-solving. AI models are best at:

Analyzing datasets
Finding patterns
Making predictions
Generating content

What is AI Model Testing?

AI Model Testing is the procedure of testing and examining an AI-model carefully to make sure it functions in accordance with design specifications and requirements. AI model’s actual performance, accuracy, and fairness are also considered during the testing process, as well as the following:

• Whether the predictions of the AI-model are accurate?
• Whether an A-model is reliable in practical circumstances?
• Whether an AI-model makes decisions without bias and with strong security?

Google’s Gemini, OpenAI’s ChatGPT, Amazon’s Alexa, and Google Maps are the most popular examples of ML applications in which AI-powered models are used.

Why Do We Need to Test AI Models?

Below, we have provided some important scenarios why testing an AI-based model is essential:

• To make sure AI-models deliver unbiased results after changes or updates.
• To increase confidence in the model’s performance and avoid data misinterpretation and wrong recommendations.
• To reveal “why” the AI-based models make a particular decision and mitigate the potential negative results of wrong decisions.
• To confirm that the model continues to perform well in real-world conditions in terms of biases or inconsistencies within the training data.
• To deal with scenarios in which models have misaligned objectives.

*AI, as well as APIs, are at the heart of many modern Apps today.

AI Model Testing Methods

Carrying out various testing methods allows teams to make sure the model is accurate, reliable, fair, and ready for real-world use. Below, you can find more information about different testing techniques:

• During dataset validation, teams check whether the data used for training and testing the AI-based model is correct and reliable to prevent learning the wrong things.
• In functional testing, teams verify if the artificial intelligence model performs the tasks correctly and delivers expected results.
• When simultaneously deploying AI-based models with opposing goals, teams opt for integration testing to test how well different components of the ML systems work together.
• Thanks to explainability testing, teams can understand why the model is making specific predictions to make sure it isn’t relying on wrong or irrelevant patterns.
• During performance testing, teams can reveal how well the model performs overall on unseen large datasets and functions in various circumstances.
• With bias and fairness testing, teams examine bias in the machine learning models to prevent discriminatory behavior in sensitive applications.
• In security testing, teams detect gaps and vulnerabilities in their AI-models to make sure they are secure against malicious data manipulation.
• Teams examine whether the model’s performance does not change after any updates with regression testing.
• When carrying out end-to-end testing, teams ensure the AI-based system works as expected once deployed.

AI Model Testing Life Cycle

To get started, you need to identify the problem the AI-model solution will solve. Once the problem is clear, it is essential to gather detailed requirements and define specific goals for the project.

#1: Data Collection and Preparation

At this step, it is important to collect the necessary datasets to train the AI-powered models. You need to make sure that they are clean, representative, and unbiased. Also, you shouldn’t forget to adhere to global data protection laws to guarantee that data collection has been done with privacy and consent in focus. When collecting and preparing data, you should consider key components:

• Data governance policies which promote standardized data collection, guarantee data quality, and maintain compliance with regulatory requirements.
• Data integration which provides AI-models with a unified access to data.
• Data quality assurance which makes sure that high-quality data is a continuous process and involves data cleaning, deduplication, and validation.

#2: Feature Engineering

At this step, you need to transform raw data into features, which are measurable data elements used for analysis and precisely represent the underlying problem for the AI model. By choosing the most relevant pieces of data, you can achieve more accurate predictions for the model and create an effective feature set for model training.

#3: Model Training

At this step, you need to train AI-powered models to perform the defined tasks and provide the most precise predictions. By choosing an appropriate algorithm and setting parameters, you can iteratively train the model with the processed data until it can correctly forecast outcomes using fresh data that it has never seen before. The choice of model and approach is critical and depends on the problem statement, data characteristics, and desired outcomes.

#4: Model Testing

Before the testing step, it is highly recommended to invest in setting up pipelines that allow you to continuously evaluate the chosen model and determine the AI model’s capabilities against predefined performance metrics and real-world expectations. You need to not only examine accuracy but also understand the model’s implications – potential biases, ethical considerations, etc.

#5: Deployment

After the AI model testing step, you can start the deployment of the model by transitioning from a controlled development environment to one that can provide valuable insights, predictions, or automation in practical scenarios. This step involves tasks like:

• Establishing methods for real-time data extraction and processing.
• Determining the storage needs for data and model’s results.
• Configuring APIs, testing tools, and environments to support model operations.
• Setting up cloud or on-premises hardware to facilitate the model’s performance.
• Creating pipelines for ongoing training, continuous deployment, and MLOps to scale the model for more use cases.

#6: Monitoring & Retrain

At the monitoring step, you need to provide ongoing performance evaluation, regular updates, and adaptations to meet evolving requirements and challenges. If done, you can make sure that the AI model functions in real-world conditions effectively, reliably, and in ethical alignment.

The Retrieval-Augmented Generation (RAG) approach uses its project data along with generic industry knowledge. Keep in mind, data quality in model training and testing is crucial to avoid pesticide effects.

Look, as AI Model Testing Life Cycle goes

As we can see in the illustration below, the testing process involving AI is sequential and cyclical. The stage of development and implementation of the AI strategy is major.

AI Testing Strategy: How to Use AI Models for Application Testing

AI is not a magic bullet, but a powerful co-pilot. By integrating AI models into your testing strategy, you can streamline test creation, enhance coverage, predict defects, and even reduce flaky results. These transform your test strategy into a smarter, faster, and more adaptive system. Leveraging artificial intelligence in application testing automates complex tasks.

#1: Identify Test Scope

At the very start, it is essential to define the goals that should be attained with AI model testing. Whether you need to automatically create new test scenarios, detect UI changes, or adapt flaky test scripts.

#2: Select and Train AI Model

Based on your goals, you need to choose an appropriate artificial intelligence model that best meets your software project requirements.

• Generative artificial intelligence models and Large Language Models (LLMs) are used for creating diverse and intelligent test cases.
• Computer Vision Models are a good fit for visual regression testing and UI element recognition.
• Reinforcement Learning Agents are well-versed in simulating complex user journeys.
• Anomaly Detection Algorithms is a good option for identifying deviations in system behavior (for instance, in logs or performance data).
• Predictive Models can be applied if there is a need to prioritize tests or predict defect likelihood.

Once the AI-model has been selected, you need to make sure you have all the necessary data for training: past test cases, test coverage results, UI snapshots/screenshots, software requirements, design documents, and user behavior data. Also, it is important to verify that it performs well.

#3: Integrate AI into the Existing AI Model Testing Framework

Once trained and validated, you should connect the AI-model with your current test automation tools and CI\CD pipelines. You can use custom testing platforms that offer pre-built integrations or automate the data flow between your application, test infrastructure, and the AI model. At this step, you can automate the testing process for generating test cases, analyzing test results, or UI changes for visual regressions.

#4: Analyze and Refine the AI Model

At this step, it is essential to review the AI-driven testing results and validate them. You need to review the test cases suggested by AI and investigate flagged anomalies, because human expertise still remains crucial for decision-making and context. Based on human feedback, you can retrain and improve the artificial intelligence model and adjust its specific goals if the testing needs of your AI application are changed.

#5: Employ MLOps for Retraining and Versioning

If you run several models simultaneously, need a scalable infrastructure, or require frequent AI-model retraining, you can automate deployment and maintenance with MLOps. Without MLOps, even advanced models can lose their value over time due to data drift. By implementing MLOps, or DevOps for machine learning, you can:

• Automate model retraining, deployment, and monitoring processes.
• Accelerate seamless interaction between data scientists, ML engineers, QA engineers, and IT teams.
• Guarantee version control for models, data, and experiments, and provide monitoring and retraining of the models.
• Support scalability and manage multiple models and datasets across environments, even as data and complexity grow.

From data processing and analysis to scalability, tracking, and auditing, when done correctly, MLOps is the most valuable approach, which enables releases to end up a more significant impact on users and better quality of products.

Advantages of AI-based Model Testing

Here are the most important reasons why you should embrace AI model testing:

Challenges to Testing AI-based Models

In testing, QA teams usually face the following challenges:

• Being dependent on data, AI models in testing are as good as the data they are trained on and learn from. If the data is noisy, incomplete, and full of bias, the model will produce incorrect results and give wrong recommendations.
• In comparison to traditional software, AI-based models can’t deliver identical outcomes for the same parameters, especially during training, which makes testing a little bit tricky in terms of the ability to predict or replicate the results.
• When coping with edge cases, AI test models can cause unexpected failures in terms of unusual input data that they have not seen before.
• Complex AI-based models can be Black-boxed and hard to interpret how they make decisions or why they make a certain prediction.
• Testing for bias and fixing it can be difficult in terms of presenting biases in the training data or through the algorithm’s design.
• Training complex models often requires specialized hardware and significant infrastructure investment.
• It can be difficult to set up clear and precise criteria for evaluating the correctness of AI models because of the complexity and nuance of their outputs.
• When testing AI models, you need to make sure they adhere strictly to legal and ethical considerations to avoid trouble after deployment.

Software Testing Tools and AI Model Testing Frameworks

To conduct effective and efficient testing, you need to choose the appropriate tools, and you need to adhere to best practices. Thus, the testing process can be greatly increased with the appropriate AI testing tools, including the following:

Best Practices for Testing AI Models

Here are some best practices to follow to conduct effective AI Model Testing in your organization:

• You need to prepare clean and unbiased data for testing and training AI models.
• You need to automate repeated test scenarios to accelerate the testing process.
• You need to track model performance and conduct fairness and bias tests to maintain its accuracy in real-world applications.
• You need to update models frequently with fresh data and make sure AI model actions can be traced back.
• You need to implement MLOps to automate data preprocessing, model training, deployment, and to keep models updated.

Bottom Line: Struggling with AI model Testing?

Navigating the AI-model testing is a complex but rewarding journey. It requires defined goals, data quality, a well-thought-out MLOps approach, solid technical expertise with ethical considerations from the start, and strategic vision to reduce release lifecycles and iteratively improve the AI products.

Whether you test one model or more, you should focus on automation, collaboration, and continuous monitoring to make sure your models remain accurate and safe. Contact testomat.io if you have any questions, and we can guide you through the AI model testing process to help you address your unique challenges.

The post AI Model Testing Explained: Methods, Challenges & Best Practices appeared first on testomat.io.

What is AI Unit testing?

AI unit testing means using artificial intelligence to automate test case generation of unit tests and data preparation processes. It eliminates manual efforts and verifies the behavior of each unit in isolation. If a unit does not do what it should do, the software program will not work efficiently or will not work at all.

How can artificial intelligence be applied in unit testing?

Here are six ways artificial intelligence can help you carry out unit testing:

#1: Test Case Automation

With AI tools, QA teams can save time and resources by letting machine learning algorithms analyze the lines of code and quickly generate automated test cases. By analyzing both your code and the code segment context, AI can automatically select high-risk areas for testing, generate unit tests for code segments or recommend tests that will provide insights into your code’s behavior. It will reduce manual workload and speed up the testing process.

#2: Test Case Generation

By using AI,  teams can automatically generate a variety of test cases that cover a wide range of scenarios and conditions. Algorithms of generative AI for unit testing properly analyze the code to identify critical points and generate effective test cases, which will cover every possible execution scenario and enable team members to identify potential issues at early stages or before implementation.

#3: Test Case Selection

With AI-based tools, teams can quickly identify the tests which are most likely to uncover defects or choose a subset of test cases from the entire test suite to be executed in a particular testing cycle. Without running the entire suite, the aim is to select those test cases which are most likely to uncover defects.

#4: Test Case Prioritization

By using AI-backed tools, teams can see how tests are prioritized based on the code complexity,  history of bugs, and code changes. By arranging test cases in a sequence that maximizes certain criteria, they help in detecting critical defects early, improving the efficiency and effectiveness of the testing process.

#5: Test Suite Optimization

AI can identify redundant or less effective tests, helping to reduce the overall test execution time. It detects error-prone areas of code and focuses testing efforts on critical flows. Furthermore, it is effective when giving recommendations on the tests that should be performed for greater test coverage.

#6: Automated Test Maintenance

Thanks to AI, test failure logs can be analyzed to identify the root cause of failures. It can also suggest potential fixes to the code or automatically update and repair existing tests to maintain their relevance and effectiveness.

Benefits of using AI to create unit tests

AI-assisted unit test creation comes with several benefits for the QA and development teams:

• Artificial intelligence tools are effective when generating a large number of tests.
• Artificial intelligence tools provide high code coverage across the project by applying the same level of thoroughness to every piece of code.
• Artificial intelligence systems can learn from feedback and improve their unit test generation efficiency over time.
• Artificial intelligence tools identify and test edge cases that eliminate human errors of overlooking.
• Artificial intelligence tools cut down the time developers spend on writing, maintaining, and running tests.
• Artificial intelligence tools update existing test suites in response to changes in the codebase.

Challenges of unit testing with AI

While AI Unit Testing offers numerous benefits, teams may face some challenges. Let’s reveal what they are:

• When it comes to unit testing with AI, teams lack standardized testing frameworks and can not establish consistent testing procedures across projects and teams.
• Teams may face difficulties when dealing with large datasets, and they require more efficient methods to manage and process vast amounts of data during the test execution process.
• When analyzing code syntax and logic, AI lacks the deep contextual understanding and might miss the broader context and business logic that dictate correct functionality, which result in tests that do not fully cover the necessary edge cases or misinterpret the intended functionality of the code.
• It may get harder for developers to rely on test automation to catch real issues. It happens because AI can sometimes generate tests that either falsely pass or fail.

To avoid mistakes, you need to write your tests before you write the actual code so that each part of your application is tested as it’s developed. Also, you need to make sure that you use realistic synthetic data that mimics real-world scenarios before generating tests.

More importantly, you need to integrate unit testing into your CI\CD pipelines to ensure tests are automatically run with every code change, catching bugs early. It helps maintain code quality throughout development.

Popular AI Tools for Unit Testing

Here are the top tools on the market today for writing unit tests. These tools use various AI techniques to automate and optimize different aspects of code review, test generation, and quality assurance.

• CaseIt It is a specialized testing tool that automatically generates test cases for diverse testing scenarios.
• Bito Used for Behavior-Driven Development (BDD), this tool offers artificial intelligence code reviews for Git workflows, AI code generation, and plan-to-production developer agents for IDE or CLI.
• Unit-test.dev This AI tool helps teams create unit test cases, supports multiple languages (Python, JavaScript/TypeScript, Java, C#) and IDEs to produce more accurate results when used in specific parts of the code.
• Virtuoso QA Using natural language processing, it simplifies test creation and execution, provides low-code/no-code testing, self-healing test scripts, etc.
• Checksum.ai This tool applies AI for test creation and maintenance.
• Carbonate Integrated into your existing testing framework, it helps teams write tests in plain English, offers a code coverage analysis, and detects areas lacking proper unit testing.
• Google Cloud’s Duet It offers AI-based code completion and generation for developers.
• Diffblue Cover With this AI-powered tool, teams can automatically generate JUnit tests for Java applications.
• Keploy An AI tool used as a test case generator for end-to-end test cases based on real user interactions.
• Github Copilot It is used to generate unit and integration tests as well as help improve code quality.

One of the coolest tools on this list is Copilot. So let’s take a look at AI unit testing in action with an example of how Copilot works. We will show you how to start using AI Copilot by demonstrating the ins and outs of generating test automation. After that, we’ll discuss Copilot’s strengths and weaknesses. Although many tools listed here use similar NLM concepts, we will not compare them in this context.

Why Copilot?

GitHub Copilot is a reasonable choice for QA engineers and developers; it boosts their productivity, improves code quality, and helps release faster.

GitHub Copilot for AI unit testing helps reduce the tediousness of writing unit tests. Integration into an IDE is advantageous as the testing tool exposes the code to the AI Copilot Chat, making it easy to tell the IDE to generate tests for a function, method, class, etc. Even a junior coder can easily write unit tests to ensure quality development. It has wide support in VS Code, Visual Studio, IntelliJ IDEA, Vim, and other IDEs. Works with multiple programming languages.

Microsoft provides the Copilot feature or service to users at no cost, and charges a premium for its advanced features.

Utilizing Copilot for Writing Unit Tests in VS Code

Copilot offers several ways to generate tests. We are focusing on using the Copilot integration with Visual Studio Code, which is a fairly representative one. To use Copilot in VS Code, we must first install it. Important prerequisites — you must have a GitHub account if you are using Copilot.

After installation, GitHub Copilot displays the chat screen as shown below.

AI-generated Unit Tests with VSCode

In VSCode, there are two primary ways to generate tests. You can enter commands in Copilot chat or you can use the right-click menu in a code file and select to generate tests. It offers AI-based code suggestions and auto-completions. To generate tests in the Copilot chat, enter a prompt asking Copilot to generate tests for the method or function. As a suggestion, on our request, Copilot provides unit test cases.

Occasionally, Copilot responses might introduce errors because it lacks full context and a natural sense of user sense — be sure to double-check the results of its suggestions. And look at an example of Jest Unit tests Copilot provides:

This Jest Unit test Copilot example code does not include setTimeout()which is better than jest.runAllTimers() in our use case. It might cause runtime issues. However, numerous users have found that Copilot attempts to predict your application’s logic but lacks a true understanding of its underlying structure or embedded details. It operates within the confines of a specific code snippet and ultimately functions in a highly intuitive manner.

Test coverage is always lacking in one way or another, if it exists at all. Leveraging AI unit testing in development is a good way to add value and decrease the significant risk of non-qualitative code.

You might also find this topic valuable:

Automated Code Review: How Smart Teams Scale Code Quality

Asking an AI to generate test automation for your code has the added advantage of providing an extra pair of eyes on your code. To an extent, the quality of the generated test code is correlated to the quality of the code being tested. When AI Copilot struggles to generate tests or produces tests, it can be an indication that the code is not easily testable, the application code is complex or incomplete. Conversely, it offers a valuable hint about refactoring: if Copilot struggles to suggest text, it may indicate that your code is overly complex and could benefit from simplification.

GitHub Copilot Agent VS Copilot Chat

You should pay attention to GitHub Copilot Agent. GitHub Copilot Agent is not only a code suggester, it is an advanced AI-powered extension that provides multi-step assistance to teams across the entire software development lifecycle — not just code completion. Learn more with the Execute Automation YouTube video, How GitHub Copilot Agent Writes Perfect Code & Tests

Best Practices For Implementing Unit Testing AI in general

We hope that following these best practices will help you implement AI unit testing successfully:

• At the very start, you need to define the goals you aim to achieve with your AI unit testing and make testing data clean and well-prepared. Removing inconsistencies and errors from your data enhances reliability. This also improves the validity of your unit tests.
• You need to create isolated tests for individual units in isolation. to identify specific issues within each unit and make debugging easier and more effective.
• With artificial intelligence tools for writing unit tests, you can generate tests and data automatically, adapt to changes in the codebase, and continually improve the tests. However, don’t forget to keep your test cases and data up to date, regularly track test coverage, and analyze performance metrics to optimize your testing strategy.
• By updating tests regularly, you make sure that test cases remain relevant and effective in catching new bugs.

Bottom Line: Ready to use AI-based Tools For Unit Testing?

With AI-driven tools for unit testing, you can make sure that your software testing is both efficient and highly effective. You can also ensure that web applications and mobile applications are functional and reliable. By implementing effective testing strategies and utilizing the right AI tools, you can improve code quality, reduce bugs, and avoid delays and bottlenecks in the development cycle.

If you’re hesitant to apply AI directly to a production codebase, that hesitation is well-grounded. Anyway, AI is amazing, as it significantly speeds up work, allowing us to deliver quality products and add more value to our clients in a more timely manner. However, we should always be cautious, keep our eyes open, and ensure we understand what we’re doing and what the AI tools are doing for us.

Drop us a line today to learn how we can help you enhance your testing processes and deliver high-quality software that meets the highest standards.

The post AI Unit Testing: A Detailed Guide appeared first on testomat.io.

To address this challenge, you can use Selenium WebDriver’s locators to find and interact with web elements. While also basic element locators like ID, Name, Class Name, and CSS Selectors often work well, they are insufficient when elements lack unique attributes or their properties change frequently. That’s when you can use XPath to navigate a web page’s complex structure to find specific elements. In this article, we will discover what  XPath in Selenium is, explore the different types of XPath, reveal basic and advanced techniques, and learn how to write XPath in Selenium.

What is Selenium?

Being an open-source suite of tools and libraries, Selenium enables teams to make the testing of website functionality automated. With its cross-browser, cross-language, and cross-platform capabilities, they can test across different environments.

Selenium supports  Java, JavaScript, C#, PHP, Python, and Ruby programming languages, which allows teams to integrate it with existing development workflows.

Furthermore, it also offers extensive browser compatibility with major web browsers like Chrome, Firefox, Safari, Edge, and Opera to cover all major browsers, while being flexible in terms of its ability to be compatible with different automation testing frameworks like TestNG, JUnit, MSTest, Pytest, WebdriverIO,

Selenium Primary Components

• Selenium WebDriver. It is a programming interface which can be used to create test cases and test across all the major programming languages, browsers, and operating systems. Regarding the cons, it has neither built-in test reporting nor a centralized way to maintain objects or elements.
• Selenium Grid. It is a smart proxy server which allows automation testers to run tests on different machines against different browsers.
• Selenium IDE. It is an easy-to-use browser extension which records your interactions with websites and helps you generate and maintain site automation, tests.

What is XPath in Selenium?

XPath, which is known as an acronym for XML Path Language, is a query language used to uniquely identify or address parts of an XML or HTML document. Generally, you can use it to do the following:

• To query or transform XML documents
• To move elements, attributes, and text through an XML document
• To look for certain elements or attributes with matching patterns
• To uniquely identify or address parts of an XML document
• To extract information from any part of an XML document
• To test the addressed nodes within a document to determine whether they match a pattern

When to use XPath

• When elements do not have unique IDs, names, or class names
• When elements are dynamic or change quickly
• When there is a need to locate elements based on their text content or position, which is relative to other elements

Overview of Basic XPath syntax in Selenium

• // – it indicates the current node
• tagname (e.g., div, input, a) – it indicates the tag name of the current node
• @attribute (e.g., @id, @name, @class) – it indicates the attribute of the node
• value (e.g., //input[@id='username']) – it indicates the value of the chosen attribute

The Difference Between Static | Dynamic XPath in Selenium

Before we start considering XPath types, it is essential to define “static” and “dynamic” XPath in the context of web elements. It needs to be done because it will determine the choice and robustness of your XPath and will result in effective test automation:

Static XPath. It is a direct and absolute path, which is specified from the root of the webpage to point to an element’s location in the Document Object Model (DOM) hierarchy. But any change in the UI can break the path. Here is XPath in Selenium example:

/html/body/div[1]/div[2]/input

This path starts from the root and traverses down to the desired element.

Dynamic XPath. It is a relative path that uses flexible criteria to locate dynamic web elements whose attributes or positions change frequently on a webpage. In contrast to Static XPath, the elements in the dynamic  XPath are more resilient to changes in the UI. To create dynamic XPathes, you can use the following:

• contains(), text(), starts-with()dynamic element indexes
• logical operators OR & AND separately or together
• axes methods

Here is xpath examples in selenium:

//input[contains(@id, 'user')]

This expression selects any <input> element with an id attribute containing the substring ‘user’.

Sum up: Static VS Dynamic XPath

Static XPath (typically absolute XPath) provides a full path from the HTML root to an element, making it very prone to failure in terms of breaking with any minor change in the page’s HTML structure.

Dynamic XPath locates elements whose properties/positions change frequently to guarantee that test scripts are less prone to failure in the face of UI updates or dynamic content. With dynamic XPath techniques, you can create stable locators, which remain stable despite UI changes, to drastically cut down on test automation maintenance, while you may face frequent test failures by relying on static XPaths in dynamic web applications.

What is an XPath locator?

XPath locator in Selenium WebDriver is a technique used in automation testing to identify web elements and help automation tools like Selenium interact with them even in complex or dynamic DOM structures. They support both absolute and relative paths, providing adaptable element identification via relationships, attributes, or text.

Types of XPath in Selenium

You can use two ways to locate an element in XPath – Absolute XPath and Relative XPath. Let’s review them with some XPath examples in Selenium below:

Absolute XPath

It contains the location of all elements from the root node (HTML), where the path starts, and specifies every node in the hierarchy. However, the whole XPath will fail to find the element if there is any change/adjustment of any node or tag along the defined XPath expression. The syntax begins with a single slash, “/”, and looks like this:

/html/body/div[1]/div[2]/form/input[2]

We see that if any new element is added before the target element, or if the structure of the divs, form, or inputs changes, this XPath will fail and break your test automation script.

Relative XPath

As the most commonly used and recommended type, it tells XPath to search for the element anywhere in the document. Starting with a double forward slash “//”, it begins from the middle of the HTML DOM structure without the need to initiate the path from the root element (node). The syntax looks like this:

//input[@id='username'] or //button[text()='Submit']

How To Create XPath in Selenium

When writing XPath in Selenium, you can do it by applying various types of XPath locators. Let’s consider them:

• Using Basic Attributes
• Using Functions
• Using Axes

Using Basic Attributes

Using XPath Functions in Selenium

XPath’s functions are used to determine elements by their attributes, positions, and other factors.

Using XPath axes in Selenium

With Axis, you can see the relationship to the current node and locate the relative nodes concerning the tree’s current node. So, the XPath Axis uses the relation between several nodes to find those nodes in the DOM structure:

Below you can find commonly used XPath axes:

We would like to mention that you can apply chained XPath in Selenium concept, where you can utilize multiple XPaths in conjunction to locate an element that might not be uniquely identifiable by a single XPath expression. In other words, instead of writing one absolute XPath, you can separate it into multiple relative XPaths. When chaining XPaths, you can improve the accuracy and robustness of the element location strategy, thus making the automation scripts more stable.

How to Use XPath in Selenium: Practical Examples

Example 1: Locating an Element by ID

The simplest way to locate elements using XPath is by their unique identifier, which is, as a rule, the id attribute. It looks like this:

WebElement element = driver.findElement(By.xpath("//input[@id='username']"))

In this example, you can use the <input> element where the id is “username.” With the findElement method, you can return the element for further interaction, checking its presence, or entering data.

Example 2: Traversing Using Axes

In this example, we consider an advanced technique to traverse the DOM’s structure based on how elements relate to each other.

WebElement parentelement = driver.findElement(By.xpath("//span[@class='label']/parent::div"))

We can see that the parent axis is applied to find the parent <div> element of a <span> with the class “label”. When an element, which you’re aiming to locate, has no unique identifying attributes, but can be found by its relationship to parent or sibling elements, XPath’s axes can be useful to achieve this goal.

html
└── body
    └── div#form
        ├── label        (Username or Email)
        ├── input        (name="log")
        ├── label        (Password)
        ├── div          (class="wp-pwd")
        ├── input        (name="rememberme")
        ├── label        (Remember Me)   
        └── button       (Log in)

Result of copying XPath with Developers’ tools are the next:

Example 3: Copy Xpath

//*[@id="user_pass"] //Copy XPath

Example 4: Copy full XPath

/html/body/div[1]/form/div/div/input.

What Are the Advantages of XPath Locators?

• With XPath, complex searches are becoming more flexible to allow you to locate items using a wide range of parameters.
• When you work with web pages with dynamic content, XPath can easily adapt to changes in page structure.
• It can traverse the DOM in both directions, which means moving from parents to children or from children to parents and siblings, to target elements that are structurally related to a known and stable element.

What Are the Disadvantages of XPath Locators?

• Complex XPath queries may be more slowly compared to simpler locators like CSS selectors.
• When relying on specific structures or attributes, XPath expressions may fail if the page structure changes.

Best Practices for Using XPath in Selenium

Here are some of the tips to follow when using XPath in Selenium:

• You need to use relative XPath to write more adaptable and maintainable locators compared to absolute XPath, which is based on the complete path from the root node.
• You need to keep XPaths as short and specific as possible to make them easier to maintain and improve.
• You need to apply functions like contains(), starts-with(), and text() if there is a need to create XPath expressions for processing dynamic elements with changing attributes. The contains() function is suitable when attributes such as id or class have variable values.
• When direct attributes aren’t enough, you can opt for XPath axes to locate elements through their relative position to a stable and identified element.
• Before incorporating an XPath into your code, you should test it directly in the browser’s console to make certain it works correctly.

Topics interesting for you:

• Playwright VS Selenium VS Cypress: A Detailed Comparison
• Playwright vs Selenium: Will Selenium Rise Again?
• Tutorial Selenium Exceptions & mastering to handle them
• Journey: Migrating from Selenium to Playwright

Bottom Line: Ready to use XPath in Selenium?

Applying XPath in Selenium while conducting the automated testing process is useful and effective for your teams. Whether they use a simple XPath or a more complex one, choosing the right XPath is crucial for test case stability. Being a powerful tool, it provides a flexible way to build robust Selenium test scripts that can handle a variety of web page structures with dynamic content and make sure they won’t fail if any of these locators change later.

Drop us a line if you want to learn more additional information about XPath in Selenium, and the testomat.io team is glad to provide software test automation services

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Both methods highlight how important it is to include test efforts early in the software development process. However, they focus on different things and have different ways of working. This guide will help you understand TDD and BDD. You will find out which method is best for your software development needs.

Understanding the Basics of TDD and BDD

Software development should always be approached as a structured process aimed at delivering the best solution. DDD, TDD, and BDD each offer valuable perspectives that can be effectively combined within the Software Development Life Cycle (SDLC) — or used dependingly on the context and project needs.

• DDD defines what to build and why by focusing on the business logic, domain language, and problem space.
• BDD defines how the system should behave based on the client’s view, involving business and non-technical stakeholders in its formulation using a simple common language form.
• TDD helps developers build the system incrementally, making sure that every part of the program is placed correctly. Developers write unit tests for the logic behind the behavior, then implement the code to make the tests pass.

Historically, BDD builds upon the TDD methodology, grounding development in acceptance testing scenarios. However, an equally important component is the concept of DSL (Domain-Specific Language), which is derived from DDD (Domain Driven Design) – a natural language understandable to all participants in the process, enabling the combination of the task statement, tests, and documentation.

The founders of the BDD approach highlight that they consider BDD primarily as a means of improving communication between the development team and the business. Dan North explains BDD as an extension of TDD practices, shifting from using it within the developer environment as a common language for all project stakeholders, and decodes the last D as development: end-to-end software development. So, let’s clarify these two definition now

Test-Driven Development (TDD)

Test-Driven Development (TDD) is a method for creating software that includes testing throughout the process. In TDD, developers first write tests before they create the actual code. This practice ensures every part of the code gets tested. The process includes three steps: writing tests, making them pass, and improving the code. This cycle leads to better software quality, fewer bugs, and improved code design. It helps to make the code stronger. TDD tests are often automated and focus on specific features. You can compare it to setting up a safety net before trying to walk on a tightrope.

Behavior-Driven Development (BDD)

Behavior-Driven Development, or BDD, focuses on how an application should work from the user’s point of view. It encourages teamwork among developers, testers, and business stakeholders. BDD user stories are written in a simple style, like Gherkin syntax. This makes it easy for everyone involved, both technical and non-technical stakeholders, to understand. By giving clear examples, BDD helps to ensure that the software meets business goals. There are BDD frameworks, like Cucumber and SpecFlow, that aid communication and keep good documentation. This is why BDD is a popular choice in agile software development.

Diving Deep into Test-Driven Development (TDD)

Test-Driven Development (TDD) is very important in the software development process. In TDD, developers write tests first and then write the code. This practice helps keep the code correct and improve software quality. TDD tests look at each small part of the code, making it essential for developers.

By running the tests often, TDD helps developers find and fix issues early. This leads to stronger and bug-free software applications. Also supports agile software development methodology.

Core Principles of TDD

Test-driven development (TDD) has important principles:

• The main one is the test-first method. This means you begin by writing a test that describes how the code should function. Only after that do you create the code itself. This approach gives developers a clear target. It also helps ensure the code works properly.
• Focus on behaviour, not implementation. Tests describe what the system should do, not how it is done. This encourages better design and flexibility in implementation.
• Principle of simplicity. TDD encourages developers to write the simplest code to make the test pass. This means they should avoid adding changes or features that are not needed right now. The goal is just to meet the requirements of the test.
• Keep your test suite fast and isolated. Tests should be independent for quick runs and to avoid stubs in development.
• Refactor with confidence. TDD focuses on repeating a cycle. In this development practice, you write tests again and again till they does not pass. You run these tests, fix the code to make them pass, and then make the code better in design and readability. This method helps you make steady progress and find problems quickly, so they can be fixed fast.

Advantages of Implementing TDD

Implementing TDD in your software development process has several benefits. The goal advantage is higher software quality. When you test each line of code well, you have fewer bugs in production. This leads to a more stable and reliable product. It also improves customer satisfaction and reduces costly bug fixes later on.

Another great benefit is that it saves time on regression testing. A full set of automated tests lets the development team check quickly if code changes have caused new bugs or broken current features. This gives developers more time to add new features and improve the product. Here are more benefits:

1. Lower development costs. When there are fewer bugs, it takes less time and money to fix them. This saves costs in the long run.
2. Better code design. Writing tests first makes developers think carefully about how they design their code. This leads to better code that is easier to manage, change, and reuse.
3. More confidence in the codebase. A well-tested codebase makes developers feel sure when they need to change or improve things. They do not worry about causing problems.
4. Efficiency of regression testing. A full set of automated tests lets the development team quickly check if code changes have caused new bugs or broken current features.
5. Improved documentation. Tests serve as live documentation. They clearly show how different parts of the system should work.

Common Challenges and Solutions in TDD

Implementing TDD has many benefits, but it also brings some challenges. A major challenge is the learning curve. This is especially true for team members who are new to the software development process. Writing tests requires a different mindset and a strong understanding of how the code should work. Another challenge is the temptation to take shortcuts when deadlines are near. It might feel easier to skip tests or write them later, but this goes against TDD.

Here are some ways to manage the challenges of TDD:

1. Give good training and support. Spending time on training helps team members learn TDD practices. It can help them get through early challenges.
2. Start small and improve. If the team is new to TDD, they should start with small, simple modules. They can then slowly use TDD for more parts of the code.
3. Work together. Encourage pair programming and code reviews. This allows everyone to share knowledge and follow TDD rules better.
4. Ask for assistance. There is a large TDD community online. Many resources can help teams deal with challenges they face.

Exploring the World of Behavior-Driven Development (BDD)

Behavior-Driven Development (BDD) builds on TDD but shifts the focus to the behavior of the system from the user’s perspective. It is a method for business stakeholders and developers to work together, describing user needs in natural Gherkin syntax. This ensures that the code is right and meets business goals. By using real examples of what is needed, BDD helps team members communicate better. Popular BDD frameworks like Cucumber and SpecFlow are used because they are easy to read and promote stakeholder involvement.

BDD’s Approach to Software Development

Behavior-Driven Development (BDD) begins with discussions rather than technical facts. Teams work together to decide how the software should act based on the user’s perspective. Instead of just testing code details, BDD asks, What should this system do for the people using it? The goal is to ensure the system behaves as expected in real-world scenarios. In process, BDD Live Documentation reduces the risk of misunderstandings and helps you clearly understand the system’s functionality. Specifications are written in a natural language format called Gherkin.

• Given some starting situation,
• When When something happens
• Then Then this is what should result

For example:

Given a user is logged in,
When they click "Log Out,"
Then they should be taken to the login page.

These tests show the steps in a scenario, explaining what a user does and how the system must respond. You can automate the tests and run them with tools like Cucumber and SpecFlow. They provide ongoing feedback about how the software works and ensure it follows the planned scenarios.

Three Amigos collaboration helps to uncover hidden ideas, clear up what is necessary, and make sure everyone knows what to expect.

Benefits of Adopting BDD Practices

BDD offers benefits that are more than just improved software quality. It connects business and technical teams, which boosts the chances of achieving the best results for their business goals. This means the software genuinely meets users’ needs, focusing on them, and allows us to create a better experience. Consequently, it boosts the probability of achieving the best results for their goals.

Here are more reasons why many software development teams prefer BDD:

1. Better Teamwork. When everyone works together to define scenarios and acceptance criteria, it helps them better understand the project requirements, which reduces the chances of misunderstandings, making working on development smoother and quicker.
2. Less Rework. By defining clear standards from the start, BDD lowers the risk of creating the wrong product. This helps to save money by reducing repairs later.
3. Stronger User Focus. BDD always considers the end user. This leads to software that is more user-friendly and valuable.
4. Easier Documentation. BDD scenarios serve as active documentation. They provide a clear and simple understanding of how the system works.

Overcoming Obstacles in BDD Implementation

Embracing BDD can be tough. A key issue is getting all team members, such as product owners, business analysts, testers, and developers, to understand and agree on the BDD process. This change needs everyone to think differently. They must be committed to working together and communicating well.

Another problem is choosing the best BDD frameworks and tools. There are many options, and each one has its benefits and drawbacks. It’s important to select tools that match your project and your team’s skills. This choice helps BDD work effectively.

Here are some useful strategies:

1. Define roles and responsibilities clearly. It is vital to understand who is responsible for what in the BDD process.
2. Training and sharing knowledge. Providing effective training on BDD practices and tools supports the change.
3. Start small and improve. Begin using BDD in a small project or a specific area of your work. This allows you to learn and enhance your approach.
4. Be open to experimenting. Different BDD frameworks address different needs. Trying out new tools can help you discover what works best for your team and project.

Comparative Analysis: TDD VS BDD

With a good understanding of TDD and BDD, you might wonder which one is better. The truth is, TDD is one small part of XP. BDD has grown to be broader than s/test/should/, because it’s trying to solve a broader problem. Both approaches have their own advantages. They can also be used together effectively in a project. Instead of viewing them as competitors, consider their strengths and how they support each other.

Shortly,

→ TDD is about ensuring that each part of the software functions properly through small sections of the code
→ BDD focused on making software that meets what users need and brings value to the business.

Key Differences Between TDD VS BDD testing

Case Studies: Successful TDD and BDD Implementations

BDD is getting more popular in agile software development. It helps people work and talk better together. This matches the goals of Agile. Many companies are using BDD with tools such as Cucumber and SpecFlow. This teamwork reduces rework and produces software that meets user needs.

Many success stories show how useful TDD and BDD are in real life. Big companies, like Google and Microsoft, use TDD to improve their code and make their work faster.

Open-source projects on platforms like GitHub are good examples of TDD in practice. They show how developers from various backgrounds use TDD ideas, regardless of the programming language. For instance, Python developers can find useful TDD examples in data analysis libraries.

Conclusion

In short, both Test-Driven Development (TDD) and Behavior-Driven Development (BDD) have their own advantages in software development. The best choice depends on your project’s needs and how well your team works together. Understanding the key points and challenges of each method can help you decide wisely. By looking at successful case studies and the differences between TDD and BDD, you can improve your development process. This change can lead to better results.

The post Exploring TDD vs BDD: Which is Right for You? appeared first on testomat.io.

A Deep Dive into BDD Automation Tools

BDD automation tools are important for making the BDD process simpler. They help turn easy-to-read scenarios into automated tests. These tools use Gherkin syntax, which is a clear and friendly language, to define test scenarios that are logical to all stakeholders, and can also integrate with AI-powered GitHub.

There is a variety of BDD automation tools available, including those that support unit testing, ranging from open-source frameworks to complete open source platforms. Each one meets the distinct needs of different development teams. Let’s look at some top tools that help teams use BDD effectively

Cucumber

Cucumber is a partly open-source testing framework for Behavior-Driven Development automation, even the most popular among the available BDD automation tools. It helps improve communication between business stakeholders and development teams. Typically, Cucumber does this through easy-to-understand Gherkin syntax.

QA teams write test scenarios in plain, human-readable language using the Gherkin syntax words to describe system’s behaviour from the end-user’s perspective e.g., Given, When, Then, ensuring that automated tests strictly reflect requirements.

Cucumber integrates well with automation frameworks like Selenium and supports multiple programming languages: Java, Ruby, JavaScript, and Python, including modern test frameworks as Selenium, TestNG, JUnit, Playwright or Cypress. Supports running features in parallel using tools like Cucumber-JVM Parallel Plugin or native support in Cucumber 6+ (Java).

For example, a .feature file in Java with Cucumber-JVM:

@Given("the user is on the login page")
public void userIsOnLoginPage() {
    driver.get("https://example.com/login");
}

Cucumber seamlessly integrates with GitHub Actions, CircleCI, Jenkins, GitLab CI, and other CI\CD servers, as well as automated regression suites and reporting platforms like Allure, ExtentReports, and Testomat. The last testing tool is interesting because of its capability to connect Cucumber BDD with Jira user stories through the Advanced Jira Plugin. This means teams can easily include BDD in their current testing workflows.

In short, Cucumber offers strong support for automation, scalability, and test maintenance, while bringing test automation closer to business logic. Shared understanding helps to avoid misunderstandings and keeps everyone focused on the same goals.

Thanks to it works with many programming languages and testing tools; a wide range of Cucumber plugins are available on the market, which makes it a good choice for different software development teams.

JBehave

As a pioneering BDD framework in Java, JBehave has opened doors for Behavior-Driven Development in Java projects. With JBehave, developers can write test scenarios in simple text and export them in various formats, including HTML, similar to story runner frameworks. One more time, this makes it easy for Devs, QA testers, and Business Analysts to work together.

Designed for the Java platform, JBehave fits well with popular testing Java frameworks like JUnit, Maven or Spring. Integrates with Selenium, REST-Assured and CI\CD pipelines, Report and Analytics dashboards. This allows for easy use within current Java development processes.

JBehave uses stories made up of scenarios to show how the application should behave. These stories can be grouped into a full suite, giving a clear view of the system’s functionality from the users’ point of view. You can look, an example of how Step Definitions map to Java methods annotated with JBehave keywords like @Given, @When, @Then in the .story file

public class LoginSteps {
  
  @Given("the user is on the login page")
  public void userIsOnLoginPage() {
      // implementation
  }

  @When("the user enters valid credentials")
  public void userEntersValidCredentials() {
      // implementation
  }

  @Then("they should see the dashboard")
  public void userSeesDashboard() {
      // implementation
  }
}

The most common use cases for JBehave are enterprise Java applications with strict architectural standards in banking, insurance, and large systems.

Behat

Behat is a strong and friendly framework among various BDD framework tools.

Behat is written in PHP, inspired by Cucumber. It works well with the PHP environment, especially launching dependency-management PHP Composer for tools like PHPUnit, which makes it a top choice for PHP projects. Thus, it is great for BDD testing of PHP projects, including web applications and different platforms like Magento, Drupal, and Symfony.

Behat is great for BDD testing for PHP developers.

One of Behat’s best features is its focus on clarity in writing Gherkin BDD scenarios. Example of Step Definition in Behat:

/**
 * @Given I am on :page
 */
public function iAmOn($page)
{
    $this->getSession()->visit($this->locatePath($page));
}

/**
 * @Then I should see :text
 */
public function iShouldSee($text)
{
    $this->assertSession()->pageTextContains($text);
}

Also, Behat is very flexible. Developers can adjust the framework to meet their specific testing needs. Browser interactions work within Selenium. Integrates CI\CD tools like GitLab CI, Jenkins, and GitHub Actions. Supports multilingual Gherkin for international teams. There is strong community support, making it simple to find resources, plugins, and help when necessary.

Serenity

Serenity BDD is an open-source test automation framework designed to make writing automated acceptance and regression tests easier and more maintainable.

Serenity BDD is great because it focuses on helpful reports and Living Documentation. We can say that Serenity’s living documentation is even its key feature. It keeps the documentation updated with the code, so when tests run, it automatically changes the documentation. This makes sure it shows the current state of the application.

Serenity creates detailed reports that give useful insights into test results and how the app behaves, providing the ability to track progress easily. Serenity standard HTML report includes: each scenario result, duration and test status, screenshots for every step, historical trends.

Serenity Step implementation:

@Steps
LoginSteps user;

@Given("the user opens the login page")
public void openLoginPage() {
    user.opensLoginPage();
}

@When("they enter valid credentials")
public void login() {
    user.logsInWithCredentials("admin", "password123");
}

@Then("they should see the dashboard")
public void dashboardVisible() {
    user.shouldSeeDashboard();
}

In addition, Serenity operates such categories as Page Objects, Tasks and Actions, which help organize test framework logic with more clarity.

It is evident that Serenity is ideal for complex enterprise-grade software projects, where teams practise Agile methodologies, especially Scrum, and strong well-structured project documentation plays a role

CodeceptJS

CodeceptJS is an open-source Node.js-based testing tool for end-to-end testing of web apps. It makes testing easier by providing a simple API that hides the difficult parts of using different browsers and devices.

As you can see, CodeceptJS works with different testing frameworks like Cucumber, WebdriverIO, Appium, Puppeteer, Selenium WebDriver and Mocha, last time primarily with Playwright. The CodeceptJS built-in multiple backend allows switching between them without changing the test logic, which is why many developers like to set up and run their tests.

It is designed for writing concise, readable, and high-level tests. It abstracts complex actions behind an intuitive Smart DSL syntax using simple and expressive statements, like: I.click(), I.see(), I.fillField(), which read similarly to Gherkin human instructions. This way, you can check the functionality of the web application thoroughly.

CodeceptJS Playwright Helper test example:

Feature('Login');

Scenario('User logs in with valid credentials', ({ I }) => {
  I.amOnPage('/login');
  I.fillField('Username', 'admin');
  I.fillField('Password', 'password123');
  I.click('Login');
  I.see('Dashboard');
});

It also carries Page Objects, which help you reuse and maintain code. Supports fast and efficient testing in CI\CD pipelines. Own Real-time Reporting and Analytics through testomat.io test management — both are founded by one team. This makes it a great choice for big and complicated web app projects.

Karate

Karate is an open-source, DSL-based API testing framework that combines API Testing with BDD principles. Karate DSL makes API testing easier. Unlike most Java-based frameworks, it doesn’t require users to write Java code — it is similar to Cucumber scenarios in built-in capabilities. This means developers can write API tests simply. This helps the QA teams understand API tests better.

Karate has built-in checks for data-driven testing through loops of test data from JSON, CSV, or tables. This makes it simple to check API responses. So, you can be sure of the functionality and reliability of backend services. Karate can also switch between API and UI testing in one test. This makes it a great choice for testing applications that depend a lot on APIs. Houses REST, SOAP, GraphQL with JSON/XML validation out of the box.

Karate Gherkin .feature file UI test example:

Feature: UI Login test

Scenario: Login with valid credentials
  * configure driver = { type: 'chromium' }
  Given driver 'https://example.com/login'
  And input('#username', 'admin')
  And input('#password', 'admin123')
  When click('#login')
  Then waitFor('#dashboard')
  And match text('#welcome') contains 'Welcome'

Also, Karate supports UI testing via Playwright or Selenium. Mocking and stubs are possible for microservice dependencies without extra tools. Works well with CI\CD pipelines. All together, it helps teams automate API tests efficiently. They can add tests to their continuous integration and delivery processes. This ensures that changes to APIs are checked often and reliably.

Key Considerations in Selecting BDD Automation Tools

Choosing the right BDD automation tool is important and needs careful thought. You have to think about:

→ Programming Language Compatibility
→ Tool Integration
→ Match the team’s expertise
→ Community and Documentation
→ Ease of Use
→ Integration Capabilities

Finally, it is important to pick a tool that works well with what you already have and matches your team’s skills and likes.

Alignment with Programming Languages

When you choose BDD tools, make sure they match the required programming languages of your project. This matching helps things go smoothly and lets developers use familiar syntax and tools.

For example, if your project is in Java, JBehave or Cucumber with Java bindings are good options. On the other hand, if you are working on a PHP project, Behat is a great choice.

Connection with existing project toolset

Using a BDD tool that fits your programming language makes the development process better. Developers can use the skills they already have, making it easier to learn the tool. This also helps with writing and managing automated tests.

Community Support and Documentation Availability

Robust community support and good documentation are very important when you start using any BDD tool. Having access to active forums, online groups, and clear documentation helps teams find answers, fix problems, and make the most of the tool.

When looking at BDD tools, check their online communities, forums, and documentation resources. This will help you see what kind of support and information is available. A strong community offers ongoing help and timely updates. It also gives teams a wider pool of knowledge. Good documentation makes learning easier and helps teams use the tool effectively.

Ease of Adoption and Learning Curve

An Intuitive and low-level curve will be beneficial. The ease of using a tool, along with its usability, and how fast people can learn it are important when you bring BDD to a team that may not have much experience. A tool that is simple to learn and has an easy interface can help the team start using it quickly and get more people involved.

Choose BDD tools that have user-friendly interfaces, clear documentation, and helpful tutorials or examples. Think about how complicated it is to set up the tool, write tests, and understand the results. A tool that is easy to learn and use can help new team members join in faster. This encourages a quicker adoption of BDD and allows more people to take part. It’s best to select a tool that matches the team’s skills or provides enough resources to help anyone who may need to learn more.

Integration Capabilities with CI\CD Pipelines

Seamless integration with existing CI\CD pipelines is crucial for maximizing the benefits of BDD automation tools like Selenium. The selected tool should integrate with popular CI\CD platforms like Jenkins, GitLab CI\CD, or Azure DevOps, enabling automated test execution as part of the development workflow.

Seamless integration empowers teams to incorporate BDD tests into their continuous integration and delivery processes efficiently. This automation ensures that tests are run regularly, providing rapid feedback on code changes and enabling early detection of potential issues.

	Language	Test type	BDD Syntax	Integrations
	Java, JS, Ruby, Python etc.	UI, API, Functional	Gherkin	Selenium, Appium, CI tools
	Java	Functional UI testing	JBehave DSL Gherkin-like	Selenium, JUnit, CI tools
	PHP	Web UI	Gherkin	Mink (browser), Drupal
	Java	UI, API, Acceptance	Gherkin	JUnit, Cucumber, Jenkins
	JavaScript / TypeScript	UI, API, Mobile	Scenario-style steps	Playwright, WebDriver, REST
		API, UI, Performance	DSL language based on Gherkin	Gatling, Mocking tools, CI\CD tools

How to Implement BDD in Your Development Process?

Integrating BDD into your development process takes more than just picking the right tools. It is important to build a team culture that supports BDD. This includes writing good BDD Specifications scenarios using Gherkin and BDD tools for testing all the time.

Teams should also check and improve BDD processes regularly. This helps keep them in line with what your project needs as it changes.

Remember:  

Successful BDD setup depends on teamwork, clear communication, and a focus on delivering high-quality software.

#1 Step: Establishing a BDD Culture within Teams

Creating a BDD culture is more than just using BDD tools at the daily desktop. It is about building teamwork where business stakeholders, developers, and testers come together. They all work to define and create useful software. Start by highlighting the benefits of BDD. Talk about how it can improve communication, cut down on errors, and make software better.

Motivate your QA team members to talk regularly and share knowledge about best BDD practices. You can set up workshops or training sessions to help everyone learn BDD ideas, Gherkin syntax, and the BDD tools you will use. This teamwork will help everyone have a good grasp of BDD concepts.

Make a habit to check and improve your BDD methods regularly. Keep them in line with what your project needs as it changes. It is important to remember that successful BDD depends on teamwork, clear communication, and a promise to create high-quality software for its users.

#2 Step: Writing Effective and Clear Gherkin Scenarios

Writing Gherkin scenarios that work well is key to successful BDD. Keep your scenarios clear and short. This way, everyone involved can easily understand them.

Understandable Given-When-Then format helps to show the conditions needed, the actions taken, and the results expected. Your scenarios should focus on a single behavior, so they are simple and clear.

Work together with both technical and business stakeholders to review and improve the scripts. This collaboration helps make sure the scenarios are accurate and complete. It is also important to check and update scenarious regularly as the application changes. This keeps everything consistent and helps avoid mistakes. By getting good at this, teams can make sure that BDD stays effective and useful.

#3 Step: Leveraging BDD Tools for Continuous Testing

To get the most out of BDD, use BDD automation tools for continuous testing. Connect your chosen BDD tool with your CI\CD pipeline. This way, automated tests will run every time code changes happen.

This feedback loop helps you find issues early and stops problems from coming back. Check out the automation capabilities of your BDD automation tools, like data-driven testing, which can help you simulate different server conditions to boost your testing work. This method lets you run tests with different data sets. Using BDD automation tools powered by AI ensures better coverage and spots unique cases.

Continuous testing helps teams stay confident in the software’s quality. It also makes sure that new features or bug fixes do not cause unintended issues after regression. Regularly look at test results, compare their historical runs and learn from them insights to make the application better and more stable. Keep a spirit of continuous BDD improvement in your testing process.

#4 Step: Measuring Success and ROI from BDD Implementation

Measuring how well BDD works and its ROI means keeping track of important testing metrics and seeing how it affects the Software Development Life Cycle. Focus on things like fewer bugs, quicker delivery cycles, and better teamwork between business and tech teams.

Watch for fewer late-stage bugs in development. This shows that BDD helps find problems early. When there is less rework and quicker delivery, it saves money. This shows the real benefits of BDD.

It is also important to gather thoughts from stakeholders about how BDD helps communication and teamwork. This shows the positive effect of BDD.

By keeping an eye on these areas, teams can show the value of BDD and keep it successful in their organization.

Common Challenges and Solutions in BDD Automation

While BDD has many benefits, using BDD automation tools has its challenges. Teams might fight against change or have trouble communicating between technical and non-technical members. They may also struggle to keep documentation consistent.

By recognizing these challenges early, teams can use strategies to overcome them. This will help make BDD automation implementation smoother and allow them to enjoy all the benefits. Being open about challenges and focusing on solutions is important to get the most out of BDD automation tools in software development projects.

Overcoming Resistance to Change

Implementing BDD means changing how we think and work. This change can cause some team members to resist since they are used to their old ways. Start by explaining the benefits of BDD clearly. Highlight how it can improve teamwork, software quality, and the success of the project.

Listen to concerns from team members and explain everything clearly. Make sure to provide good training and support so everyone understands BDD and can adapt to the new method.

Encourage teams to try BDD on smaller projects first. This will help them get comfortable before they tackle larger ones. Celebrate early wins and show off the real benefits from BDD automation tools. This will help prove its value and make team members more open to accepting it.

Bridging the Gap Between Technical and Non-Technical Team Members

Effective communication is very important in BDD. It is key to bridging the gap between technical and non-technical team members. Use clear and simple specifications when defining scenarios. Avoid technical terms that may confuse non-technical stakeholders.

Hold regular meetings or workshops where technical and non-technical team members work together to define scenarios and set acceptance criteria. Visual tools, like diagrams or process flows, can help show scenarios and make them easier to understand.

Keep open communication channels. Use shared documents or online platforms to encourage ongoing talks. This keeps everyone on the same page about the project’s needs and progress. This way of working together helps reduce misunderstandings and ensures software development meets business needs.

Ensuring Consistent and Up-to-Date Documentation

Maintaining clear and current documentation is very important for long-lasting success in BDD. However, keeping the documentation aligned with the changing code can be tough. You can generate the Living Documentation that some BDD automation tools provide.

These BDD automation tools (Serenity or Testomat test management) update the documentation automatically as tests run. This keeps it in line with the current condition of the application. Even if you use these tools, you should regularly review and update your documents. Periodic checks on your documentation help ensure it is clear, correct, and complete.

Doing this allows you to spot any mistakes or old info early. It is also helpful to set clear rules and responsibilities for documentation. This way, team members can take charge and help keep everything consistent over time.

Conclusion

In summary, using BDD automation tools and a comprehensive test automation tool can change how you develop software. These tools help teams work together better, improve communication, and make testing easier.

Of course, each tool brings its own benefits suited for different programming languages and testing needs. When you pick the right tool for your goals, you should connect it smoothly to your CI\CD processes.

Building a BDD culture in your teams will help you achieve more efficient software development. By tackling issues with good communication and proper documentation, BDD will play an important role in your development plan and enhance the overall development phase. This will lead to ongoing improvement and a positive return on investment over time.

The post The Best BDD Automation Tools and Frameworks for Testing Teams appeared first on testomat.io.

At the same time, software development accelerates and becomes more iterative and user-centric due to the growth of Agile methodologies and Artificial Intelligence (AI). The test pyramid remains a key tool for effectively organising test automation efforts. Each QA team can adapt this approach to better align with their ongoing Software Development Cycles (SDLC) and Continuous Integration (CI\CD) processes.

Let’s explore the principles of the testing pyramid in today’s dynamic software development landscape in more detail

Understanding the Testing Pyramid Definition

The test pyramid, which many link back to Mike Cohn, is an important idea in software testing to show how to match requirements, along with the different types of tests in a robust software testing strategy.

The testing pyramid suggests using a layered method to distribute testing work across various levels. Following the pyramid, development teams can create a strong test plan that helps them stay balanced and improve efficiency. Namely, it is one such approach to balancing Automated Testing.

Pay attention to the arrows in the picture! The pyramid is built to highlight the importance of evaluating testing costs. Testing is generally “cheaper” closer to the base layer.

Setup highlights the need to find bugs early in the development cycle, as test cost is lower and test speed is higher. Similarly, Unit tests focus on smaller components and are easier to write faster tu run and cheaper than E2E and manual testing efforts. This is considered a best practice that gives us the best ratio of time spent in testing and debugging versus the likelihood of finding bugs. By combining different testing levels, like unit tests and UI tests, we can ensure good test coverage. This is also one of the pillars of product quality.

Testing Pyramid Importance in Modern Development

→ Enables a smart approach to automated testing.
→ The pyramid’s structured layers help to utilise resources wisely.
→ It emphasises the necessity for automated testing, particularly at the lower levels.
→ Provides quick feedback, enabling teams to identify and resolve issues early in the process.
→ Prevents bugs from progressing to later development stages, resulting in a reduced need for slow and costly manual testing later on.

The Layers of the Testing Pyramid Explained

At the very beginning, before starting automation, we should conduct minimal statistical analysis at the requirements level to be sure that our application will work exactly as end users expect.

At the bottom are automated tests. It focuses on unit tests. These are the building blocks of a solid testing strategy. They check individual components, by themselves, ensuring each code unit works correctly as expected in isolation.

Next, we go up to integration tests. These tests check how different modules of the software work together. They make sure that all the parts function well when combined, and the data from the API flow successfully between different units or modules without errors.

At the top of the pyramid, we find UI tests. These tests validate how the system works from the user’s point of view. UI tests act like a user touching the screen to see if everything behaves as it should and provides a smooth experience.

According to the testing pyramid, the most tests are on the unit-level, fewer tests on the integration level and the least amount of tests on the system level.

— Does that mean that we should write tests for all levels?
— No! We just should select carefully which functionality is tested on which level.

If we can show a problem with a unit test, we use a unit test. Only if a problem cannot be shown with a unit test we go one level upwards by using an integration test or even a system test.

For failing tests we work our way up the testing pyramid. First, all unit-tests need to pass before it makes sense to start debugging either the integration or system tests.

Using this flow, you can debug and fix issues most effectively and efficiently.

Generally, there is a trade-off between speed and confidence, so you can’t skip any level of testing. Gradually, move up; however, you can decide how much focus to place on the different levels. Essentially, it’s about finding the right balance between your time investment and the optimal return in terms of confidence in your software application. That’s what the different shapes of testing signify.

Implementing the Testing Pyramid

Implementing the testing pyramid is a step-by-step process. During this process, it’s important to choose the right tools and technologies. They should help support your automated testing efforts. A strong testing system is very important to ensure your testing is efficient and reliable. 

Everything sounds good in theory. But in real life, it is a bit complicated, so let’s go deeper into the testing pyramid levels.

Unit Testing: The Foundation of the Pyramid

Unit testing is the foundation of the testing pyramid. It starts with creating a strong base of unit tests. This process tests the smallest parts of an application, focusing on individual components or modules. Then, we will go higher and combine these pieces, checking how they work together. The goal is to make sure they work as expected. It is important for creating reliable and easy-to-maintain software.

By checking the behavior of these individual units of code, developers can find and fix bugs early in the development cycle. At this stage, issues are easier and cheaper to fix. A good test suite that covers all parts of a unit’s functionality helps developers change code confidently. They will know that any unexpected problems will be found early.

The advantages of strong unit testing go beyond just finding bugs. It also helps create clean code design. Developers are motivated to write modular and loosely connected code, making it simpler to test and maintain. A solid foundation of unit tests is key for any software project. It ensures that developers can make changes and improve the system without worrying about causing new problems.

Integration Testing: The Middle Layer

While unit tests look at single parts, integration tests check how these parts work together and share information. Integration tests are essential for finding problems that happen when separate units join to make bigger modules or when they connect with outside services or databases.

A key part of integration testing is using real test data that mimics how people actually use the system. This approach helps to find issues with data consistency, how well components communicate, and how they manage outside dependencies.

By finding and fixing integration issues early, development teams can avoid expensive repairs later in the software development life cycle (SDLC). At that point, bugs are harder to find and fix, and they can lead to bigger problems.

UI Testing: The Apex of the Pyramid

At the top of the testing pyramid is UI testing. This type of testing looks at how the application behaves and how users experience it. UI tests imitate what users do, like clicking buttons, filling out forms, and moving between different screens. This helps make sure the application works well and gives a smooth user experience.

Setting up UI tests is often more complicated than unit and integration tests because they check everything from start to finish. They need special tools and frameworks that work with the application’s user interface. This makes them more sensitive to changes, which can cause them to break easily.

Even so, UI tests are crucial for checking important steps of user interaction. They help ensure the application meets the expected quality standards. A good UI test suite can address many parts of the user experience, including usability, accessibility, and how well the application works on different browsers and devices.

How Agile Methodologies Influence Testing Approaches

The rise of Agile methods has changed software development and how QA teams test it. Traditional ways, which followed a straight path, have been replaced by iterative and step-by-step methods, and now testing happens all the time during the development cycle. This change highlights the need for early and regular testing during development.

Agile teams know that testing early and often helps find and solve problems fast. They mix testing into the development process from the start. This way, they keep a steady pace in development. Instead of only doing manual testing at the end, Agile teams use automated tests throughout the process. This gives them more confidence when releasing software.

Test automation is key in Agile testing. It plays a very important role in getting fast feedback, managing quick changes and frequent releases.

By automating tests that take a lot of time to run, teams can spend their energy on exploratory testing, usability testing, and other important work that needs human insight and creativity.

In addition, new Agile methods focus on teamwork. This breaks down old barriers and encourages everyone to share the responsibility for quality. Developers, testers, and business people work closely together on all layers of the testing pyramid. Working together like this allows for finding and fixing problems sooner, leading to quicker feedback and better software quality. This teamwork helps everyone stay on the same page about the project’s quality goals, leading to better and more efficient testing efforts.

Shift-Left Testing Strategy within the Testing Pyramid

Shift-Left Testing emphasizes moving testing activities earlier in the software development lifecycle, even before the start of the code stage. When applied to the Testing Pyramid, it focuses on investing more in unit and integration tests — the lower layers of the pyramid — where it is expected.

In Agile settings, the testing pyramid is still an important tool. Anyway, its formulation undergoes.

Evolution of the Testing Pyramid in Agile Environments

The testing pyramid has changed to support Agile ideas. Thus, in addition to the classic testing pyramid, sometimes Unit, Integration, E2E testing are presented in alternative forms like the Trophy, Hexagon, Diamond, and others have also emerged to represent different testing strategies and priorities.

However, it is important to fit these interpretations into the ongoing workflow.

Challenges and Solutions in Applying the Testing Pyramid

The testing pyramid is a helpful guide, but using it can be tough. It is important to know the common mistakes and follow good practices to work through them. You need to balance the different testing levels to get the best test coverage.

To tackle these issues, you must really understand your application and what testing it needs. Your testing strategy should focus on being flexible and improving all the time.

Common Pitfalls and How to Avoid Them

• Using too many UI tests. It is a common mistake. While UI tests are important, having too many can make the testing process weak and slow. Since UI tests are at the top of the pyramid, they should be fewer and more focused than integration or unit tests.
• Ignoring integration testing. This type of testing is important to make sure different parts of the system work well together. If you skip this step, some bugs may not show up until the application is nearly finished. This can lead to expensive corrections.

To avoid these problems, start with a strong group of unit tests as the base of your testing efforts. A balanced testing strategy is essential for good test coverage and for creating high-quality software.

There are challenges to face, like common mistakes, but using the right tools can make testing better.

Tools and Technologies for Effective Automation Testing

The ever-evolving landscape of software development offers a plethora of tools and technologies designed to streamline and enhance the testing process. Leveraging these tools is essential for software developers to maintain the efficiency and effectiveness of their test automation pyramid.

Selecting the appropriate tools depends on various factors, including the programming languages used, the application’s architecture, and the specific testing needs of the project.

Trends Shaping the Testing Landscape

Leading trends are changing software development. They are affecting how we will test and ensure software quality soon.

• The rise of AI and machine learning presents new challenges for testers. Testing AI-powered applications needs special techniques and tools. We need these to check their behavior and make sure they work well.
• More people are using cloud computing. This adds new challenges in the testing process. We must think about things like scalability, reliability, and security in cloud-based environments.
• As development and operations start to blend with DevOps, testing is becoming a key part of the continuous integration and continuous delivery (CI\CD) pipeline.

This means we must shift to automated testing from the earliest stages. It also encourages teamwork between developers, testers, and operations teams. This way, we can ensure fast and smooth software delivery.

The Future of Testing Pyramid Usage

As software development moves forward, the testing pyramid’s future, as we considered on top, will focus on improving test automation. Agile teams need quick feedback, which means an advanced test reporting and analytics dashboard with reach test metrics set on different testing levels, like unit tests and UI tests, we can ensure good test coverage.

In today’s software development, the testing pyramid plays a key role by putting test automation at the base. They should also use manual testing for complicated cases. Agile teams must find a good balance to make the testing process smooth and keep the software quality high.

Adapting the Testing Pyramid for New Technologies

New technologies ask us to change how we test things. As technology becomes a bigger part of software systems, we need our testing strategies to adapt as well.

Changing the testing pyramid for new technologies means we should mix old testing methods with new tools. This helps us handle the special challenges that come with new tech.

If we keep learning about new trends and stay flexible, the testing pyramid can still help us deliver great software, even with all the changes in technology.

Conclusion

In the quick-moving future, keeping up with new AI trends will be key to shaping testing practices. The Testing Pyramid will remain very important in future software development, ensuring speed, flexibility and quality in Agile environments.

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