AI Tools for Testers in the IT Industry
The IT industry is rapidly adopting AI to improve testing processes, making them faster, more efficient, and more reliable. AI-powered tools are transforming how testers design, execute, and analyze tests. In this blog, we’ll explore some of the most impactful AI tools for software testers, along with detailed examples of their use.
- Testim
Category: Test Automation
Overview
Testim uses AI to create, execute, and maintain automated tests. Its self-healing capabilities help manage dynamic applications by identifying and adapting to changes in the application’s UI.
Key Features:
• Self-healing tests using AI.
• Visual test creation with minimal scripting.
• Integration with CI/CD pipelines like Jenkins and GitHub Actions.
Example Use Case:
Suppose you’re testing an e-commerce application. The UI elements, such as buttons or input fields, often change IDs. Testim’s AI identifies these dynamic changes and updates the test cases automatically, reducing maintenance efforts.
// Example Testim script for automating login
test(“User Login Test”, async () => {
. await testim.navigateTo(“https://example.com/login”);
. await testim.type(“username”, “testuser”);
. await testim.type(“password”, “securepassword”);
. await testim.click(“loginButton”);
. await testim.assertText(“welcomeMessage”, “Welcome, Test User!”);
});
2. Applitools
Category: Visual Testing
Overview
Applitools leverages AI to perform visual testing. It compares UI screenshots pixel by pixel, detecting anomalies that could be missed by traditional testing approaches.
Key Features:
• Cross-browser and cross-device visual testing.
• Integration with major testing frameworks like Selenium and Cypress.
• AI-powered “Visual AI” to detect meaningful changes.
Example Use Case:
While testing a banking app, you want to ensure that UI elements align correctly across browsers. Applitools can compare the expected and actual UI, flagging inconsistencies in spacing, colors, or text.
// Using Applitools with Selenium
eyes.open(driver, “Banking App”, “Login Page Test”);
driver.get(“https://examplebank.com/login”);
eyes.checkWindow(“Login Page”);
eyes.close();
3. Mabl
Category: Low-Code Test Automation
Overview
Mabl simplifies automated testing with AI-driven test creation and maintenance. It focuses on end-to-end testing and integrates well with CI/CD pipelines.
Key Features:
• AI-based test healing for dynamic apps.
• Automatic reporting with insights into failures.
• Built-in support for API testing.
Example Use Case:
For a SaaS platform, you need to automate the onboarding process. Mabl allows you to create tests for workflows like form submissions and email verifications with minimal code.
// Workflow in Mabl:
- Start a new test.
- 2. Record the user’s actions (e.g., filling forms).
- 3. Verify form submission and API responses.
4. Functionize
Category: AI-Powered Test Automation
Overview
Functionize enables testers to write natural language test cases. The AI interprets these and converts them into executable scripts.
Key Features:
• Test creation via plain English commands.
• Self-healing capabilities.
• Cross-platform testing support.
Example Use Case:
Imagine testing a travel booking app. You could write:
“Search for flights from New York to London on December 20, 2024.”
The AI generates the test script to automate this scenario, saving time and effort.
5. Percy by BrowserStack
Category: Visual Regression Testing
Overview
Percy helps identify visual changes in your application during development. It integrates seamlessly with CI/CD tools to capture screenshots at every step of the pipeline.
Key Features:
• Screenshot comparison across environments.
• Support for multiple frameworks like React and Angular.
• Real-time collaboration on detected issues.
Example Use Case:
While redesigning your website’s homepage, Percy can capture snapshots before and after code changes, highlighting any unintended visual shifts.
6. Test.ai
Category: Mobile App Testing
Overview
Test.ai uses AI to test mobile apps with minimal configuration. It identifies UI elements using AI and mimics human interactions.
Key Features:
• Automated test creation for mobile apps.
• Coverage analysis to identify untested areas.
• Self-healing tests to adapt to changes.
Example Use Case:
For a fitness tracking app, Test.ai can automatically navigate through menus, start a workout session, and verify data logging without writing detailed scripts.
7. ChatGPT for Test Case Generation
Category: AI-Powered Assistance
Overview
ChatGPT can help testers design test cases, write code snippets, and troubleshoot errors. By leveraging its language understanding, testers can save time on repetitive tasks.
Example Use Case:
You need test cases for an online shopping cart. Ask:
“Generate test cases for adding items to a shopping cart.”
Output:
1. Verify that adding an item updates the cart count.
2. Verify the total price updates after adding items.
3. Verify items persist in the cart after refreshing the page.
8. Cypress AI Plugin
Category: AI-Augmented Automation
Overview
Cypress has a growing ecosystem of plugins, including AI-based plugins that enhance test execution by suggesting optimal selectors and analyzing test failures.
Example Use Case:
Testing a dynamic web page where element selectors frequently change. AI plugins in Cypress can analyze the DOM and recommend the most stable selectors.
cy.get(“button:contains(‘Submit’)”).click(); // Selector recommended by AI
Conclusion
AI tools are revolutionizing software testing, empowering testers to focus on critical tasks while automating repetitive ones. Whether it’s visual testing with Applitools, automated workflows in Mabl, or natural language scripting in Functionize, AI is making testing more efficient and effective.
By integrating these tools into your workflow, you can stay ahead in the fast-paced IT industry. Choose the right combination of tools based on your project’s needs, and you’ll unlock the full potential of AI in testing.
