How GenAI Created a POM for a Login Page Using Page Source? 🤔

As a QA engineer, one of the repetitive yet critical tasks is creating Page Object Models (POMs) for automation frameworks. While effective, the process of manually analyzing a page’s source code and translating it into a well-structured POM can be time-consuming and prone to human error.

This is where Generative AI (GenAI) stepped in and amazed me! 💡

Let me share how I used GenAI to create a POM for a Login Page directly from its page source:

---

The Process:

1️⃣ Input: I provided the page source code of the login page (HTML structure).
2️⃣ GenAI Analysis: GenAI parsed the code, identified key elements (e.g., input fields, buttons), and proposed meaningful names for them.
3️⃣ Code Generation: It generated a clean, reusable POM, including:

• Locators (CSS/XPath/IDs)
• Action methods (e.g., enterUsername(), clickSubmit())
  4️⃣ Output: A ready-to-use POM file following best practices, saving me hours of manual effort.

---

Here’s What It Generated for the Login Page:

Login

Generated POM:

"```" java @FindBy(id = "username") private WebElement usernameField;

@FindBy(id = "password") private WebElement passwordField;

@FindBy(id = "loginBtn") private WebElement loginButton;

public void enterUsername(String username) { usernameField.sendKeys(username); }

public void enterPassword(String password) { passwordField.sendKeys(password); }

public void clickLoginButton() { loginButton.click(); }

The Impact:

Time Saved: What usually took 30-40 minutes now takes a few seconds! Consistency: GenAI follows naming conventions and best practices, ensuring standardization. Scalability: Works seamlessly for complex pages with numerous elements. This small yet powerful use case highlights how AI tools are revolutionizing software testing workflows. Instead of spending time on boilerplate code, we can focus on test strategies, edge cases, and improving overall quality.