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Playwright Test Agent: The Future of AI-Driven Test Automation

by Rajesh K | Nov 7, 2025 | AI Testing, Blog, Latest Post | 0 comments

The test automation landscape is changing faster than ever. With AI now integrated into major testing frameworks, software teams can automate test discovery, generation, and maintenance in ways once unimaginable. Enter Playwright Test Agents, Microsoft’s groundbreaking addition to the Playwright ecosystem. These AI-powered agents bring automation intelligence to your quality assurance process, allowing your test suite to explore, write, and even fix itself. In traditional test automation, QA engineers spend hours writing test scripts, maintaining broken locators, and documenting user flows. But with Playwright Test Agents, much of this heavy lifting is handled by AI. The agents can:

• Explore your application automatically
• Generate test cases and Playwright scripts
• Heal failing or flaky tests intelligently

In other words, Playwright Test Agents act as AI assistants for your test suite, transforming the way teams approach software testing.

This blog will break down:

• What Playwright Test Agents are
• How the Planner, Generator, and Healer work
• How to set them up in VS Code
• Real-world examples of use
• Best practices for AI-assisted QA
• What’s next for the future of Playwright Agents

What Are Playwright Test Agents?

Playwright Test Agents are specialized AI components designed to assist at every stage of the test lifecycle, from discovery to maintenance.

Here’s an overview of the three agents and their unique roles:

Together, they bring AI-assisted automation directly into your Playwright workflow—reducing manual effort, expanding test coverage, and keeping your test suite healthy and up to date.

1. The Planner Agent, Exploring and Documenting User Flows

The Planner Agent acts like an intelligent QA engineer exploring your web app for the first time.

• Launches your application
• Interacts with the UI elements
• Identifies navigational paths and form actions
• Generates a structured Markdown test plan

Example Output

# Login Page Test Plan

  1.Navigate to the login page
  2.Verify the presence of username and password fields
  3.Enter valid credentials and submit
  4.Validate successful navigation to the dashboard
  5.Test with invalid credentials and verify the error message

This auto-generated document serves as living documentation for your test scope, ideal for collaboration between QA and development teams before automation even begins.

2. The Generator Agent, Converting Plans into Playwright Tests

Once your Planner has produced a test plan, the Generator Agent takes over.

It reads the plan and automatically writes executable Playwright test code following Playwright’s best practices.

Example

Input (from the Planner):

• Navigate to login page
• Enter username and password
• Click login button
• Verify navigation to dashboard

import { test, expect } from '@playwright/test';

test('User can log in successfully', async ({ page }) => {
  await page.goto('/login');
  await page.fill('#username', 'testuser');
  await page.fill('#password', 'password123');
  await page.click('button[type="submit"]');
  await expect(page).toHaveURL('/dashboard');
});

This agent eliminates hours of manual scripting, making test authoring faster, consistent, and scalable.

Tip: Always review generated tests before committing to ensure they align with business logic and expected coverage.

3. The Healer Agent – Fixing Tests Automatically

The Healer Agent is your test suite’s maintenance superhero.

When UI changes cause tests to fail (e.g., element IDs change), the Healer detects the issue and auto-updates the locator or selector.

Example

If your test fails due to a missing locator:

await page.click('#loginBtn'); // element not found

The Healer Agent might automatically fix it as:

await page.getByRole('button', { name: 'Login' }).click();

This ensures your automation suite remains stable, resilient, and self-healing, even as the app evolves.

How Playwright Test Agents Work Together

The three agents form a continuous AI-assisted testing cycle:

• Planner explores and documents what to test
• Generator creates the actual Playwright tests
• Healer maintains and updates them over time

This continuous testing loop ensures that your automation suite evolves alongside your product, reducing manual rework and improving long-term reliability.

Getting Started with Playwright Test Agents

Playwright Test Agents are part of the Model Context Protocol (MCP) experimental feature by Microsoft.

You can use them locally via VS Code or any MCP-compatible IDE.

Step-by-Step Setup Guide

Step 1: Install or Update Playwright

npm init playwright@latest

This installs the latest Playwright framework and initializes your test environment.

Step 2: Initialize Playwright Agents

npx playwright init-agents --loop=vscode

This command configures the agent loop—a local MCP connection that allows Planner, Generator, and Healer agents to work together.

You’ll find the generated .md file under the .github folder.

Step 3: Use the Chat Interface in VS Code

Open the MCP Chat interface in VS Code (similar to ChatGPT) and start interacting with the agents using natural language prompts.

Sample Prompts for Each Agent

Planner Agent Prompt

Goal: Explore the web app and generate a manual test plan.

Generator Agent Prompt

Goal: Convert test plan sections into Playwright tests.

Use the Playwright Generator agent to create Playwright automation code for:

### 1. Navigation and Menu Testing

Generate a Playwright test in TypeScript and save it in tests/Menu.spec.ts.

Healer Agent Prompt

Goal: Auto-fix failing or flaky tests.

Run the Playwright Healer agent on the test suite in /tests.

Identify failing tests, fix selectors/timeouts, and regenerate updated test files.

These natural-language prompts demonstrate how easily AI can be integrated into your development workflow.

Example: From Exploration to Execution

Let’s say you’re testing a new e-commerce platform that includes product listings, a shopping cart, and a payment gateway.

Run the Planner Agent – It automatically explores your web application, navigating through product pages, the cart, and the checkout process. As it moves through each flow, it documents every critical user action from adding items to the cart to completing a purchase and produces a clear, Markdown-based test plan.

Run the Generator Agent – Using the Planner’s output, this agent instantly converts those user journeys into ready-to-run Playwright test scripts. Within minutes, you have automated tests for product search, cart operations, and payment validation, with no manual scripting required.

Run the Healer Agent – Weeks later, your developers push a UI update that changes button selectors and layout structure. Instead of causing widespread test failures, the Healer Agent detects these changes, automatically updates the locators, and revalidates the affected tests.

The Result:
You now have a continuously reliable, AI-assisted testing pipeline that evolves alongside your product. With minimal human intervention, your test coverage stays current, your automation remains stable, and your QA team can focus on optimizing performance and user experience, not chasing broken locators.

Benefits of Using Playwright Test Agents

Best Practices for Using Playwright Test Agents

• Review AI-generated tests before merging to ensure correctness and value.
• Store Markdown test plans in version control for auditing.
• Use semantic locators like getByRole or getByText for better healing accuracy.
• Combine agents with Playwright Test Reports for enhanced visibility.
• Run agents periodically to rediscover new flows or maintain old ones.

The Future of Playwright Test Agents

The evolution of Playwright Test Agents is only just beginning. Built on Microsoft’s Model Context Protocol (MCP), these AI-driven tools are setting the stage for a new era of autonomous testing where test suites not only execute but also learn, adapt, and optimize themselves over time.

In the near future, we can expect several exciting advancements:

• Custom Agent Configurations – Teams will be able to fine-tune agents for specific domains, apps, or compliance needs, allowing greater control over test generation and maintenance logic.
• Enterprise AI Model Integrations – Organizations may integrate their own private or fine-tuned LLMs to ensure data security, domain-specific intelligence, and alignment with internal QA policies.
• API and Mobile Automation Support – Playwright Agents are expected to extend beyond web applications to mobile and backend API testing, creating a unified AI-driven testing ecosystem.
• Advanced Self-Healing Analytics – Future versions could include dashboards that track healing frequency, failure causes, and predictive maintenance patterns, turning reactive fixes into proactive stability insights.

These innovations signal a shift from traditional automation to autonomous quality engineering, where AI doesn’t just write or fix your tests, it continuously improves them. Playwright Test Agents are paving the way for a future where intelligent automation becomes a core part of every software delivery pipeline, enabling faster releases, greater reliability, and truly self-sustaining QA systems.

Conclusion

The rise of Playwright Test Agents marks a defining moment in the evolution of software testing. For years, automation engineers have dreamed of a future where test suites could understand applications, adapt to UI changes, and maintain themselves. That future has arrived, and it’s powered by AI.

With the Planner, Generator, and Healer Agents, Playwright has transformed testing from a reactive task into a proactive, intelligent process. Instead of writing thousands of lines of code, testers now collaborate with AI that can:

• Map user journeys automatically
• Translate them into executable scripts
• Continuously fix and evolve those scripts as the application changes

Playwright Test Agents don’t replace human testers; they amplify them. By automating repetitive maintenance tasks, these AI-powered assistants free QA professionals to focus on strategy, risk analysis, and innovation. Acting as true AI co-engineers, Playwright’s Planner, Generator, and Healer Agents bring intelligence and reliability to modern testing, aligning perfectly with the pace of DevOps and continuous delivery. Adopting them isn’t just a technical upgrade; it’s a way to future-proof your quality process, enabling teams to test smarter, deliver faster, and set new standards for intelligent, continuous quality.

Stagehand – AI-Powered Browser Automation

by Rajesh K | Oct 7, 2025 | AI Testing, Blog, Latest Post | 22 comments

For years, the promise of test automation has been quietly undermined by a relentless reality: the burden of maintenance. As a result, countless hours are spent by engineering teams not on building new features or creative test scenarios, but instead on a frustrating cycle of fixing broken selectors after every minor UI update. In fact, it is estimated that up to 40% of test maintenance effort is consumed solely by this tedious task. Consequently, this is often experienced as a silent tax on productivity and a drain on team morale. This is precisely the kind of challenge that the Stagehand framework was built to overcome. But what if a different approach was taken? For instance, what if the browser could be spoken to not in the complex language of selectors, but rather in the simple language of human intent?

Thankfully, this shift is no longer a theoretical future. On the contrary, it is being delivered today by Stagehand, an AI-powered browser automation framework that is widely considered the most significant evolution in testing technology in a decade. In the following sections, a deep dive will be taken into how Stagehand is redefining automation, how it works behind the scenes, and how it can be practically integrated into a modern testing strategy with compelling code examples.

The Universal Pain Point: Why the Old Way is Felt by Everyone

To understand the revolution, the problem must first be appreciated. Let’s consider a common login test. In a robust traditional framework like Playwright, it is typically written as follows:

// Traditional Playwright Script - Fragile and Verbose
const { test, expect } = require('@playwright/test');

test('user login', async ({ page }) => {
  await page.goto("https://example.com/login");
  // These selectors are a single point of failure
  await page.fill('input[name="email"]', '[email protected]');
  await page.fill('input[data-qa="password-input"]', 'MyStrongPassword!');
  await page.click('button#login-btn.submit-button');
  await page.waitForURL('**/dashboard');
  
  // Assertion also relies on a specific selector
  const welcomeMessage = await page.textContent('.user-greeting');
  expect(welcomeMessage).toContain('Welcome, Test User');
});

While effective in a controlled environment, this script is inherently fragile in a dynamic development lifecycle. Consequently, when a developer changes an attribute or a designer tweaks a class, the test suite is broken. As a result, automated alerts are triggered, and valuable engineering time is redirected from development to diagnostic maintenance. In essence, this cycle is not just inefficient; it is fundamentally at odds with the goal of rapid, high-quality software delivery.

It is precisely this core problem that is being solved by Stagehand, where rigid, implementation-dependent selectors are replaced with intuitive, semantic understanding.

What is Stagehand? A New Conversation with the Browser

At its heart, Stagehand is an AI-powered browser automation framework that is built upon the reliable foundation of Playwright. Essentially, its revolutionary premise is simple: the browser can be controlled using natural language instructions. In practice, it is designed for both developers and AI agents, seamlessly blending the predictability of code with the adaptability of AI.

For comparison, the same login test is reimagined with Stagehand as shown below:

import asyncio
from stagehand import Stagehand, StagehandConfig

async def run_stagehand_local():
    config = StagehandConfig(
        env="LOCAL",
        model_name="ollama/mistral", 
        model_client_options={"provider": "ollama"},
        headless=False
    )

    stagehand = Stagehand(config=config)
    await stagehand.init()

    page = stagehand.page
    await page.act("Go to https://the-internet.herokuapp.com/login")
    await page.act("Enter 'tomsmith' in the Username field")
    await page.act("Enter 'SuperSecretPassword!' in the Password field")
    await page.act("Click the Login button and wait for the Secure Area page to appear")

    title = await page.title()
    print("Login successful" if "Secure Area" in title else "Login failed")

    await stagehand.close()

asyncio.run(run_stagehand_local())

The difference is immediately apparent. Specifically, the test is transformed from a low-level technical script into a human-readable narrative. Therefore, tests become:

• More Readable: What is being tested can be understood by anyone, from a product manager to a new intern, without technical translation.
• More Resilient: Elements are interacted with based on their purpose and label, not a brittle selector, thereby allowing them to withstand many front-end changes.
• Faster to Write: Less time is spent hunting for selectors, and more time is invested in defining meaningful user behaviors and acceptance criteria.

Behind the Curtain: The Intelligent Three-Layer Engine

Of course, this capability is not magic; on the contrary, it is made possible by a sophisticated three-layer AI engine:

• Instruction Understanding & Parsing: Initially, the natural language command is parsed by an AI model. Subsequently, the intent is identified, and key entities’ actions, targets, and data are broken down into atomic, executable steps.
• Semantic DOM Mapping & Analysis: Following this, the webpage is scanned, and a semantic map of all interactive elements is built. In other words, elements are understood by their context, labels, and relationships, not just their HTML tags.
• Adaptive Action Execution & Validation: Finally, the action is intelligently executed. Additionally, built-in waits and retries are included, and the action is validated to ensure the expected outcome was achieved.

A Practical Journey: Implementing Stagehand in Real-World Scenarios

Installation and Setup

Firstly, Stagehand must be installed. Fortunately, the process is straightforward, especially for teams already within the Python ecosystem.

# Install Stagehand via pip for Python
pip install stagehand

# Playwright dependencies are also required
pip install playwright
playwright install

Real-World Example: An End-to-End E-Commerce Workflow

Now, let’s consider a user journey through an e-commerce site: searching for a product, filtering, and adding it to the cart. This workflow can be automated with the following script:

import asyncio
from stagehand import Stagehand

async def ecommerce_test():
    browser = await Stagehand.launch(headless=False)
    page = await browser.new_page()

    try:
        print("Starting e-commerce test flow...")
        
        # 1. Navigate to the store
        await page.act("Go to https://example-store.com")
        
        # 2. Search for a product
        await page.act("Type 'wireless headphones' into the search bar and press Enter")
        
        # 3. Apply a filter
        await page.act("Filter the results by brand 'Sony'")
        
        # 4. Select a product
        await page.act("Click on the first product in the search results")
        
        # 5. Add to cart
        await page.act("Click the 'Add to Cart' button")
        
        # 6. Verify success
        await page.act("Go to the shopping cart")
        page_text = await page.text_content("body")
        
        if "sony" in page_text.lower() and "wireless headphones" in page_text.lower():
            print("TEST PASSED: Correct product successfully added to cart.")
        else:
            print("TEST FAILED: Product not found in cart.")

    except Exception as e:
        print(f"Test execution failed: {e}")
    finally:
        await browser.close()

asyncio.run(ecommerce_test())

This script demonstrates remarkable resilience. For instance, if the “Add to Cart” button is redesigned, the AI’s semantic understanding allows the correct element to still be found and clicked. As a result, this adaptability is a game-changer for teams dealing with continuous deployment and evolving UI libraries.

Weaving Stagehand into the Professional Workflow

It is important to note that Stagehand is not meant to replace existing testing frameworks. Instead, it is designed to enhance them. Therefore, it can be seamlessly woven into a professional setup, combining the structure of traditional frameworks with the adaptability of AI.

Example: A Structured Test with Pytest

For example, Stagehand can be integrated within a Pytest structure for organized and reportable tests.

# test_stagehand_integration.py
import pytest
import asyncio
from stagehand import Stagehand

@pytest.fixture(scope="function")
async def browser_setup():
    browser = await Stagehand.launch(headless=True)
    yield browser
    await browser.close()

@pytest.mark.asyncio
async def test_user_checkout(browser_setup):
    page = await browser_setup.new_page()
        
    # Test Steps are written as a user story
    await page.act("Navigate to the demo store login page")
    await page.act("Log in with username 'test_user'")
    await page.act("Search for 'blue jeans' and select the first result")
    await page.act("Select size 'Medium' and add it to the cart")
    await page.act("Proceed to checkout and fill in shipping details")
    await page.act("Enter test payment details and place the order")
    
    # Verification
    confirmation_text = await page.text_content("body")
    assert "order confirmed" in confirmation_text.lower()

This approach, often called Intent-Driven Automation, focuses on the what rather than the how. Consequently, tests become more valuable as living documentation and are more resilient to the underlying code changes.

The Strategic Imperative: Weighing the Investment

Given these advantages, adopting a new technology is a strategic decision. Therefore, the advantages offered by Stagehand must be clearly understood.

A Comparative Perspective

The Horizon: What Comes Next?

Furthermore, Stagehand is just the beginning. Looking ahead, the future of QA is being shaped by AI, leading us toward:

• Self-Healing Tests: Scripts that can adjust themselves when failures are detected.
• Intelligent Test Generation: Critical test paths are suggested by AI based on analysis of the application.
• Context-Aware Validation: Visual and functional changes are understood in context, distinguishing bugs from enhancements.

Ultimately, these tools will not replace testers but instead will empower them to focus on higher-value activities like complex integration testing and user experience validation.

Conclusion: From Maintenance to Strategic Innovation

In conclusion, Stagehand is recognized as more than a tool; in fact, it is a fundamental shift in the philosophy of test automation. By leveraging its power, the gap between human intention and machine execution is being bridged, thereby allowing test suites to be built that are not only more robust but also more aligned with the way we naturally think about software. The initial setup is straightforward, and the potential for reducing technical debt is profound. Therefore, by integrating Stagehand, a team is not just adopting a new library,it is investing in a future where tests are considered valuable, stable assets that support rapid innovation rather than hindering it.

In summary, the era of struggling with selectors is being left behind. Meanwhile, the era of describing behavior and intent has confidently arrived.

Is your team ready to be transformed?
The first step is easily taken: pip install stagehand. From there, a new, more collaborative, and more efficient chapter in test automation can be begun.

Frequently Asked Questions

GitHub Copilot vs Microsoft Copilot: What’s the Real Difference?

by Rajesh K | Jul 18, 2025 | AI Testing, Blog, Latest Post | 7 comments

Artificial Intelligence (AI) continues to revolutionize industries, driving unprecedented productivity and efficiency. One of its most transformative effects is on the field of automation testing, where AI tools are helping QA teams write test scripts, identify bugs, and optimize test coverage faster than ever. Among today’s standout AI tools are GitHub Copilot vs Microsoft Copilot. Though similarly named and under Microsoft’s ecosystem, these tools address entirely different needs. GitHub Copilot is like a co-pilot for developers, always ready to jump in with smart code suggestions and streamline your programming and test automation workflow. Meanwhile, Microsoft Copilot feels more like a business assistant that’s embedded right into your day-to-day apps, helping you navigate your workload with less effort and more impact.

So, how do you decide which one fits your needs? Let’s break it down together. In this blog, we’ll explore their differences, use cases, benefits, and limitations in a conversational, easy-to-digest format. Whether you’re a developer drowning in code or a business professional juggling meetings and emails, there’s a Copilot ready to help.

Understanding the Basics: What Powers GitHub and Microsoft Copilot?

Shared Foundations: OpenAI Models

Both GitHub Copilot and Microsoft Copilot are powered by OpenAI’s language models, but they’re trained and optimized differently:

Deep Dive into GitHub Copilot

If you write code regularly, you’ve probably wished for an assistant who could handle the boring stuff like boilerplate code, test generation, or fixing those annoying syntax errors. That’s exactly what GitHub Copilot brings to the table.

Core Capabilities:

• Smart code completion as you type
• Entire function generation from a simple comment
• Generate test cases and documentation
• Translate comments or pseudo-code into working code
• Refactor messy or outdated code instantly

Supported Programming Languages:

GitHub Copilot supports a wide array of languages including:

Python, JavaScript, TypeScript, Java, Ruby, Go, PHP, C++, C#, Rust, and more

Why Developers Love It:

• It helps cut development time by suggesting full functions and reusable code snippets.
• Reduces errors early with syntax-aware suggestions.
• Encourages best practices by modeling suggestions on open-source code patterns.

Real-world Example:

Let’s say you’re building a REST API in Python. Type a comment like # create an endpoint for user login, and Copilot will instantly draft a function using Flask or FastAPI, including error handling and basic validation. That’s time saved and fewer bugs.

Comprehensive Look at Microsoft Copilot

Now, imagine you’re in back-to-back meetings, drowning in emails, and you’ve got a massive report to prepare. Microsoft Copilot jumps in like a helpful assistant, reading your emails, summarizing documents, or generating entire PowerPoint presentations—all while you focus on bigger decisions.

Core Capabilities:

• Rewrite and summarize documents or emails
• Draft email responses with tone customization
• Analyze spreadsheets and create charts using natural language
• Turn meeting transcripts into organized action items
• Build presentations from existing content or documents

Practical Use Cases:

• Word: Ask Copilot to summarize a 20-page legal document into five bullet points.
• Excel: Type “show sales trends by quarter” and it creates the charts and insights.
• Outlook: Auto-generate replies, follow-ups, or even catch tone issues.
• Teams: After a meeting, Copilot generates a summary and assigns tasks.
• PowerPoint: Turn a planning document into a visually appealing slide deck.

Why Professionals Rely on It:

• It eliminates repetitive manual tasks.
• Helps teams collaborate faster and better.
• Offers more clarity and focus by turning scattered data into actionable insights.

Security and Privacy Considerations

Pricing & Licensing Overview

Why Were GitHub Copilot and Microsoft Copilot Created?

GitHub Copilot’s Purpose:

GitHub Copilot was born out of the need to simplify software development. Developers spend a significant portion of their time writing repetitive code, debugging, and referencing documentation. Copilot was designed to:

• Reduce the friction in the coding process
• Act as a real-time mentor for junior developers
• Increase code quality and development speed
• Encourage best practices through intelligent suggestions

Its goal? To let developers shift from mundane code generation to building more innovative and scalable software.

Microsoft Copilot’s Purpose:

Microsoft Copilot emerged as a response to the growing complexity of digital workflows. In enterprises, time is often consumed by writing reports, parsing emails, formatting spreadsheets, or preparing presentations. Microsoft Copilot was developed to:

• Minimize time spent on repetitive office tasks
• Maximize productivity across Microsoft 365 applications
• Turn information overload into actionable insights
• Help teams collaborate more effectively and consistently

It’s like having a productivity partner that understands your business tools and workflows inside out.

Which Copilot Is Right for You?

Choose GitHub Copilot if:

• You write or maintain code daily.
• You want an AI assistant to speed up coding and reduce bugs.
• Your team collaborates using GitHub or popular IDEs.

Choose Microsoft Copilot if:

• You spend most of your day in Word, Excel, Outlook, or Teams.
• You need help summarizing, analyzing, or drafting content quickly.
• You work in a regulated industry and need enterprise-grade security.

Conclusion

GitHub Copilot and Microsoft Copilot are both designed to make you more productive but in totally different ways. Developers get more done with GitHub Copilot by reducing coding overhead, while business professionals can focus on results, not grunt work, with Microsoft Copilot.

Frequently Asked Questions

AI Agents for Automation Testing: Revolutionizing Software QA

by Rajesh K | Jun 11, 2025 | AI Testing, Blog, Latest Post | 3 comments

In today’s fast-paced development world, AI agents for automation testing are no longer science fiction they’re transforming how teams ensure software quality. Imagine giving an intelligent “digital coworker” plain English instructions, and it automatically generates, executes, and even adapts test cases across your application. This blog explains what AI agents in testing are, how they differ from traditional automation, and why tech leads and QA engineers are excited about them. We’ll cover real-world examples (including SmolAgent from Hugging Face), beginner-friendly analogies, and the key benefits of AI-driven test automation. Whether you’re a test lead or automation engineer, this post will give you a deep dive into the AI agent for automation testing trend. Let’s explore how these smart assistants are freeing up testers to focus on creative problem-solving while handling the routine grind of regression and functional checks.

What Is an AI Agent in Test Automation?

An AI testing agent is essentially an intelligent software entity dedicated to running and improving tests. Think of it as a “digital coworker” that can examine your app’s UI or API, spot bugs, and even adapt its testing strategy on the fly. Unlike a fixed script that only does exactly what it’s told, a true agent can decide what to test next based on what it learns. It combines AI technologies (like machine learning, natural language processing, or computer vision) under one umbrella to analyze the application and make testing decisions

• Digital coworker analogy: As one guide notes, AI agents are “a digital coworker…with the power to examine your application, spot issues, and adapt testing scenarios on the fly” . In other words, they free human testers from repetitive tasks, allowing the team to focus on creative, high-value work.
• Intelligent automation: These agents can read the app (using tools like vision models or APIs), generate test cases, execute them, and analyze the results. Over time, they learn from outcomes to suggest better tests.
• Not a replacement, but a partner: AI agents aren’t meant to replace QA engineers. Instead, they handle grunt work (regression suites, performance checks, etc.), while humans handle exploratory testing, design, and complex scenarios

In short, an AI agent in automation testing is an autonomous or semi-autonomous system that can perform software testing tasks on its own or under guidance. It uses ML models and AI logic to go beyond simple record-playback scripts, continuously learning and adapting as the app changes. The result is smarter, faster testing where the agentic part its ability to make decisions and adapt distinguishes it from traditional automation tools

How AI Agents Work in Practice

AI agents in testing operate in a loop of sense – decide – act – learn. Here’s a simplified breakdown of how they function:

• Perception (Sense): The agent gathers information about the application under test. For a UI, this might involve using computer vision to identify buttons or menus. For APIs, it reads endpoints and data models. Essentially, the agent uses AI (vision, NLP, data analysis) to understand the app’s state, much like a human tester looking at a screen.
• Decision-Making (Plan): Based on what it sees, the agent chooses what to do next. For example, it may decide to click a “Submit” button or enter a certain data value. Unlike scripted tests, this decision is not pre-encoded – the agent evaluates possible actions and selects one that it predicts will be informative.
• Action (Execute): The agent performs the chosen test actions. It might run a Selenium click, send an HTTP request, or invoke other tools. This step is how the agent actually exercises the application. Because it’s driven by AI logic, the same agent can test very different features without rewriting code.
• Analysis & Learning: After actions, the agent analyzes the results. Did the app respond correctly? Did any errors or anomalies occur? A true agent will use this feedback to learn and adapt future tests. For example, it might add a new test case if it finds a new form or reduce redundant tests over time. This continuous loop sensing, acting, and learning is what differentiates an agent from a simple automation script.

In practice, many so-called “AI agents” today may be simpler (often just advanced scripts with AI flair). But the goal is to move toward fully autonomous agents that can build, maintain, and improve test suites on their own. For example, an agent can “actively decide what tasks to perform based on its understanding of the app” spotting likely failure points (like edge case input) without being explicitly programmed to do so. It can then adapt if the app changes, updating its strategy without human intervention.

AI Agents vs. Traditional Test Automation

It helps to compare traditional automation with AI agent driven testing. Traditional test automation relies on pre-written scripts that play back fixed actions (click here, enter that) under each run. Imagine a loyal robot following an old instruction manual it’s fast and tireless, but it won’t notice if the UI changes or try new paths on its own. In contrast, AI agents behave more like a smart helper that learns and adapts.

• Script vs. Smarts: Traditional tools run pre-defined scripts only. AI agents learn from data and evolve their approach.
• Manual updates vs. Self-healing: Normal automation breaks when the app changes (say, a button moves). AI agents can “self-heal” tests – they detect UI changes and adjust on the fly.
• Reactive vs. Proactive: Classic tests only do what they’re told. AI-driven tests can proactively spot anomalies or suggest new tests by recognizing patterns and trends.
• Human effort: Manual test creation requires skilled coders. With AI agents, testers can often work in natural language or high-level specs. For instance, one example lets testers write instructions in plain English, which the agent converts into Selenium code.
• Coverage: Pre-scripted tests cover only what’s been coded. AI agents can generate additional test cases automatically, using techniques like analyzing requirements or even generating tests from user stories

A handy way to see this is in a comparison table:

This table illustrates why many teams view AI agents as the “future of testing.” They dramatically reduce the manual overhead of test creation and maintenance, while providing smarter coverage and resilience. In fact, one article quips that traditional automation is like a robot following an instruction manual, whereas AI automation “actively learns and evolves” , enabling it to upgrade tests on the fly as it learns from results.

Key Benefits of AI Agents in Automation Testing

Integrating AI agents into your QA process can yield powerful advantages. Here are some of the top benefits emphasized by industry experts and recent research:

• Drastically Reduced Manual Effort: AI agents can automate repetitive tasks (regression runs, data entry, etc.), freeing testers to focus on new features and explorations, They tackle the “tedious, repetitive tasks” so human testers can use their creativity where it matters.
• Fewer Human Errors: By taking over routine scripting, agents eliminate mistakes that slip in during manual test coding. This leads to more reliable test runs and faster releases.
• Improved Test Coverage: Agents can automatically generate new test cases. They analyze app requirements or UI flows to cover scenarios that manual testers might miss. This wider net catches more bugs.
• Self-Healing Tests: One of the most-cited perks is the ability to self-adjust. For example, if a UI element’s position or name changes, an AI agent can often find and use the new element rather than failing outright. This cuts down on maintenance downtime.
• Continuous Learning: AI agents improve over time. They learn from previous test runs and user interactions. This means test quality keeps getting better – the agent can refine its approach for higher accuracy in future cycles.
• Faster Time-to-Market: With agents generating tests and adapting quickly, development cycles speed up. Teams can execute comprehensive tests in minutes that might take hours manually, leading to quicker, confident releases.
• Proactive Defect Detection: Agents can act like vigilant watchdogs. They continuously scan for anomalies and predict likely failures by analyzing patterns in data . This foresight helps teams catch issues earlier and reduce costly late-stage defects.
• Better Tester Focus: With routine checks handled by AI, QA engineers and test leads can dedicate more effort to strategic testing (like exploratory or usability testing) that truly requires human judgment.

These benefits often translate into higher product quality and significant ROI. As Kobiton’s guide notes, by 2025 AI testing agents will be “far more integrated, context-aware, and even self-healing,” helping CI/CD pipelines reach the next level. Ultimately, leveraging AI agents is about working smarter, not harder, in software quality assurance.

AI Agent Tools and Real-World Examples

Hugging Face’s SmolAgent in Action

A great example of AI agents in testing is Hugging Face’s SmolAgents framework. SmolAgents is an open-source Python library that makes it simple to build and run AI agents with minimal code. For QA, SmolAgent can connect to Selenium or Playwright to automate real user interactions on a website.

• English-to-Test Automation: One use case lets a tester simply write instructions in plain English, which the SmolAgent translates into Selenium actions . For instance, a tester could type “log in with admin credentials and verify dashboard loads.” The AI agent interprets this, launches the browser, inputs data, and checks the result. This democratizes test writing, allowing even non- programmers to create tests.
• SmolAgent Project: There’s even a GitHub project titled “Automated Testing with Hugging Face SmolAgent”, which shows SmolAgent generating and executing tests across Selenium, PyTest, and Playwright. This real-world codebase proves the concept: the agent writes the code to test UI flows without hand-crafting each test.
• API Workflow Automation: Beyond UIs, SmolAgents can handle APIs too. In one demo, an agent used the API toolset to automatically create a sequence of API calls (even likened to a “Postman killer” in a recent video). It read API documentation or specs, then orchestrated calls to test endpoints. This means complex workflows (like user signup + order placement) can be tested by an agent without manual scripting.
• Vision and Multimodal Agents: SmolAgent supports vision models and multi-step reasoning. For example, an agent can “see” elements on a page (via computer vision) and decide to click or type. It can call external search tools or databases if needed. This makes it very flexible for end-to-end testing tasks.

In short, SmolAgent illustrates how an AI agent can be a one-stop assistant for testing. Instead of manually writing dozens of Selenium tests, a few natural-language prompts can spawn a robust suite.

Emerging AI Testing Tools

The ecosystem of AI-agent tools for QA is rapidly growing. Recent breakthroughs include specialized frameworks and services:

• UI Testing Agents: Tools like UI TARS and Skyvern use vision language models to handle web UI tests. For example, UI TARS can take high level test scenarios and visualize multistep workflows, while Skyvern is designed for modern single-page apps (SPA) without relying on DOM structure.
• Gherkin-to-Test Automation: Hercules is a tool that converts Gherkin-style test scenarios (plain English specs) into executable UI or API tests. This blurs the line between manual test cases and automation, letting business analysts write scenarios that the AI then automates.
• Natural Language to Code: Browser-Use and APITestGenie allow writing tests in simple English. Browser-Use can transform English instructions into Playwright code using GPT models. APITestGenie focuses on API tests, letting testers describe API calls in natural language and having the agent execute them.
• Open-Source Agents: Beyond SmolAgent, companies are exploring open frameworks. An example is a project that uses SmolAgent along with tools4AI and Docker to sandbox test execution. Such projects show it’s practical to integrate large language models, web drivers, and CI pipelines into a coherent agentic testing system.

Analogies and Beginner-friendly Example

If AI agents are still an abstract idea, consider this analogy: A smart assistant in the kitchen. Traditional automation is like a cook following a rigid cookbook. AI agents are like an experienced sous-chef who understands the cuisine, improvises when an ingredient is missing, and learns a new recipe by observing. You might say, “Set the table for a family dinner,” and the smart sous-chef arranges plates, pours water, and even tweaks the salad dressing recipe on-the-fly as more guests arrive. In testing terms, the AI agent reads requirements (the recipe), arranges tests (the table), and adapts to changes (adds more forks if the family size grows), all without human micromanagement.

Or think of auto-pilot in planes: a pilot (QA engineer) still oversees the flight, but the autopilot (AI agent) handles routine controls, leaving the pilot to focus on strategy. If turbulence hits (a UI change), the autopilot might auto-adjust flight path (self-heal test) rather than shaking (failing test). Over time the system learns which routes (test scenarios) are most efficient.

These analogies highlight that AI agents are assistive, adaptive partners in the testing process, capable of both following instructions and going beyond them when needed.

How to Get Started with AI Agents in Your Testing

Adopting AI agents for test automation involves strategy as much as technology. Here are some steps and tips:

• Choose the Right Tools: Explore AI-agent frameworks like SmolAgents, LangChain, or vendor solutions (Webo.AI, etc.) that support test automation. Many can integrate with Selenium, Cypress, Playwright, or API testing tools. For instance, SmolAgents provides a Python SDK to hook into browsers.
• Define Clear Objectives: Decide what you want the agent to do. Start with a narrow use case (e.g. automate regression tests for a key workflow) rather than “test everything”.
• Feed Data to the Agent: AI agents learn from examples. Provide them with user stories, documentation, or existing test cases. For example, feeding an agent your acceptance criteria (like “user can search and filter products”) can guide it to generate tests for those features.
• Use Natural Language Prompts: If the agent supports it, describe tests in plain English or high- level pseudo code. As one developer did, you could write “Go to login page, enter valid credentials, and verify dashboard” and the agent translates this to actual Selenium commands.
• Set Up Continuous Feedback: Run your agent in a CI/CD pipeline. When a test fails, examine why and refine the agent. Some advanced agents offer “telemetry” to monitor how they make decisions (for example, Hugging Face’s SmolAgent can log its reasoning steps).
• Gradually Expand Scope: Once comfortable, let the agent explore new areas. Encourage it to try edge cases or alternative paths it hasn’t seen. Many agents can use strategies like fuzzing inputs or crawling the UI to find hidden bugs.
• Monitor and Review: Always have a human in the loop, especially early on. Review the tests the agent creates to ensure they make sense. Over time, the agent’s proposals can become a trusted part of your testing suite.

Throughout this process, think of the AI agent as a collaborator. It should relieve workload, not take over completely. For example, you might let an agent handle all regression testing, while your team designs exploratory test charters. By iterating and sharing knowledge (e.g., enriching the agent’s “toolbox” with specific functions like logging in or data cleanup), you’ll improve its effectiveness.

Take Action: Elevate Your Testing with AI Agents

AI agents are transforming test automation into a faster, smarter, and more adaptive process. The question is: are you ready to harness this power for your team? Start small evaluate tools like SmolAgent, LangChain, or UI-TARS by assigning them a few simple test scenarios. Write those scenarios in plain English, let the agent generate and execute the tests, and measure the results. How much time did you save? What new bugs were uncovered?

You can also experiment with integrating AI agents into your DevOps pipeline or test out a platform like Webo.AI to see intelligent automation in action. Want expert support to accelerate your success? Our AI QA specialists can help you pilot AI-driven testing in your environment. We’ll demonstrate how an AI agent can boost your release velocity, reduce manual effort, and deliver better quality with every build.

Don’t wait for the future start transforming your QA today.

Frequently Asked Questions

AI Generated Test Cases: How Good Are They?

by Rajesh K | Apr 8, 2025 | AI Testing, Blog, Latest Post | 0 comments

Artificial intelligence (AI) is transforming software testing, especially in test case generation. Traditionally, creating test cases was time-consuming and manual, often leading to errors. As software becomes more complex, smarter and faster testing methods are essential. AI helps by using machine learning to automate test case creation, improving speed, accuracy, and overall software quality. Not only are dedicated AI testing tools evolving, but even generative AI platforms like ChatGPT, Gemini, and DeepSeek are proving helpful in creating effective test cases. But how reliable are these AI-generated test cases in real-world use? Can they be trusted for production? Let’s explore the current state of AI in testing and whether it’s truly game-changing or still in its early days.

The Evolution of Test Case Generation: From Manual to AI-Driven

Test case generation has come a long way over the years. Initially, testers manually created each test case by relying on their understanding of software requirements and potential issues. While this approach worked for simpler applications, it quickly became time-consuming and difficult to scale as software systems grew more complex.

To address this, automated testing was introduced. Tools were developed to create test cases based on predefined rules and templates. However, setting up these rules still required significant manual effort and often resulted in limited test coverage.

With the growing need for smarter, more efficient testing methods, AI entered the picture. AI-driven tools can now learn from vast amounts of data, recognize intricate patterns, and generate test cases that cover a wider range of scenarios—reducing manual effort while increasing accuracy and coverage.

What are AI-Generated Test Cases?

AI-generated test cases are test scenarios created automatically by artificial intelligence instead of being written manually by testers. These test cases are built using generative AI models that learn from data like code, test scripts, user behavior, and Business Requirement Documents (BRDs). The AI understands how the software should work and generates test cases that cover both expected and unexpected outcomes.

These tools use machine learning, natural language processing (NLP), and large language models (LLMs) to quickly generate test scripts from BRDs, code, or user stories. This saves time and allows QA teams to focus on more complex testing tasks like exploratory testing or user acceptance testing.

Analyzing the Effectiveness of AI in Test Case Generation

Accurate and reliable test results are crucial for effective software testing, and AI-driven tools are making significant strides in this area. By learning from historical test data, AI can identify patterns and generate test cases that specifically target high-risk or problematic areas of the application. This smart automation not only saves time but also reduces the chance of human error, which often leads to inconsistent results. As a result, teams benefit from faster feedback cycles and improved overall software quality. Evaluating the real-world performance of these AI-generated test cases helps us understand just how effective AI can be in modern testing strategies.

Benefits of AI in Testing:

• Faster Test Writing: Speeds up creating and reviewing repetitive test cases.
• Improved Coverage: Suggests edge and negative cases that humans might miss.
• Consistency: Keeps test names and formats uniform across teams.
• Support Tool: Helps testers by sharing the workload, not replacing them.
• Easy Integration: Works well with CI/CD tools and code editors.

AI Powered Test Case Generation Tools

Today, there are many intelligent tools available that help testers brainstorm test ideas, cover edge cases, and generate scenarios automatically based on inputs like user stories, business requirements, or even user behavior. These tools are not meant to fully replace testers but to assist and accelerate the test design process, saving time and improving test coverage.

Let’s explore a couple of standout tools that are helping reshape test case creation:

1. Codoid Tester Companion

Codoid Tester Companion is an AI-powered, offline test case generation tool that enables testers to generate meaningful and structured test cases from business requirement documents (BRDs), user stories, or feature descriptions. It works completely offline and does not rely on internet connectivity or third-party tools. It’s ideal for secure environments where data privacy is a concern.

Key Features:

• Offline Tool: No internet required after download.
• Standalone: Doesn’t need Java, Python, or any dependency.
• AI-based: Uses NLP to understand requirement text.
• Instant Output: Generates test cases within seconds.
• Export Options: Save test cases in Excel or Word format.
• Context-Aware: Understands different modules and features to create targeted test cases.

How It Helps:

• Saves time in manually drafting test cases from documents.
• Improves coverage by suggesting edge-case scenarios.
• Reduces human error in initial test documentation.
• Helps teams working in air-gapped or secure networks.

Steps to Use Codoid Tester Companion:

1. Download the Tool:

• Go to the official Codoid website and download the “Tester Companion” tool.
• No installation is needed—just unzip and run the .exe file.

2. Input the Requirements:

• Copy and paste a section of your BRD, user story, or functional document into the input field.

3. Click Generate:

• The tool uses built-in AI logic to process the text and create test cases.

4. Review and Edit:

• Generated test cases will be visible in a table. You can make changes or add notes.

5. Export the Output:

• Save your test cases in Excel or Word format to share with your QA or development teams.

2. TestCase Studio (By SelectorsHub)

TestCase Studio is a Chrome extension that automatically captures user actions on a web application and converts them into readable manual test cases. It is widely used by UI testers and doesn’t require any coding knowledge.

Key Features:

• No Code Needed: Ideal for manual testers.
• Records UI Actions: Clicks, input fields, dropdowns, and navigation.
• Test Step Generation: Converts interactions into step-by-step test cases.
• Screenshot Capture: Automatically takes screenshots of actions.
• Exportable Output: Download test cases in Excel format.

How It Helps:

• Great for documenting exploratory testing sessions.
• Saves time on writing test steps manually.
• Ensures accurate coverage of what was tested.
• Helpful for both testers and developers to reproduce issues.

Steps to Use TestCase Studio:

Install the Extension:

• Go to the Chrome Web Store and install TestCase Studio.

Launch the Extension:

• After installation, open your application under test (AUT) in Chrome.
• Click the TestCase Studio icon from your extensions toolbar.

Start Testing:

• Begin interacting with your web app—click buttons, fill forms, scroll, etc.
• The tool will automatically capture every action.

View Test Steps:

• Each action will be converted into a human-readable test step with timestamps and element details.

Export Your Test Cases:

• Once done, click Export to Excel and download your test documentation.

The Role of Generative AI in Modern Test Case Creation

In addition to specialized AI testing tools, support for software testing is increasingly being provided by generative AI platforms like ChatGPT, Gemini, and DeepSeek. Although these tools were not specifically designed for QA, they are being used effectively to generate test cases from business requirements (BRDs), convert acceptance criteria into test scenarios, create mock data, and validate expected outcomes. Their ability to understand natural language and context is being leveraged during early planning, edge case exploration, and documentation acceleration.

Sample test case generation has been carried out using these generative AI tools by providing inputs such as BRDs, user stories, or functional documentation. While the results may not always be production-ready, structured test scenarios are often produced. These outputs are being used as starting points to reduce manual effort, spark test ideas, and save time. Once reviewed and refined by QA professionals, they are being found useful for improving testing efficiency and team collaboration.

Challenges of AI in Test Case Generation (Made Simple)

• Doesn’t work easily with old systems – Existing testing tools may not connect well with AI tools without extra effort.
• Too many moving parts – Modern apps are complex and talk to many systems, which makes it hard for AI to test everything properly.
• AI doesn’t “understand” like humans – It may miss small but important details that a human tester would catch.
• Data privacy issues – AI may need data to learn, and this data must be handled carefully, especially in industries like healthcare or finance.
• Can’t think creatively – AI is great at patterns but bad at guessing or thinking outside the box like a real person.
• Takes time to set up and learn – Teams may need time to learn how to use AI tools effectively.
• Not always accurate – AI-generated test cases may still need to be reviewed and fixed by humans.

Conclusion

AI is changing how test cases are created and managed. It helps speed up testing, reduce manual work, and increase test coverage. Tools like ChatGPT can generate test cases from user stories and requirements, but they still need human review to be production-ready. While AI makes testing more efficient, it can’t fully replace human testers. People are still needed to check, improve, and adapt test cases for real-world situations. At Codoid, we combine the power of AI with the expertise of our QA team. This balanced approach helps us deliver high-quality, reliable applications faster and more efficiently.

Frequently Asked Questions

AI Assistant in Chrome Devtools: Guide for Testers

by Charlotte Johnson | Feb 24, 2025 | AI Testing, Blog, Latest Post | 0 comments

Modern web browsers have evolved tremendously, offering powerful tools that assist developers and testers in debugging and optimizing applications. Among these, Google Chrome DevTools stands out as an essential toolkit for inspecting websites, monitoring network activity, and refining the user experience. With continuous improvements in browser technology, Chrome DevTools now includes AI Assistant, an intelligent feature that enhances the debugging process by providing AI-powered insights and solutions. This addition makes it easier for testers to diagnose issues, optimize web applications, and ensure a seamless user experience.

In this guide, we will explore how AI Assistant can be used in Chrome DevTools, particularly in the Network and Elements tabs, to assist in API testing, UI validation, accessibility checks, and performance improvements.

Uses of the AI Assistant Tool in Chrome DevTools

Chrome DevTools offers a wide range of tools for inspecting elements, monitoring network activity, analyzing performance, and ensuring security compliance. Among these, the AI Ask Assistant stands out by providing instant, AI-driven insights that simplify complex debugging tasks.

1. Debugging API and Network Issues

Problem: API requests fail, take too long to respond, or return unexpected data.

How AI Helps:

• Identifies HTTP errors (404 Not Found, 500 Internal Server Error, 403 Forbidden).
• Detects CORS policy violations, incorrect API endpoints, or missing authentication tokens.
• Suggests ways to optimize API performance by reducing payload size or caching responses.
• Highlights security concerns in API requests (e.g., unsecured tokens, mixed content issues).
• Compares actual API responses with expected values to validate data correctness.

2. UI Debugging and Fixing Layout Issues

Problem: UI elements are misaligned, invisible, or overlapping.

How AI Helps:

• Identifies hidden elements caused by display: none or visibility: hidden.
• Analyzes CSS conflicts that lead to layout shifts, broken buttons, or unclickable elements.
• Suggests fixes for responsiveness issues affecting mobile and tablet views.
• Diagnoses z-index problems where elements are layered incorrectly.
• Checks for flexbox/grid misalignments causing inconsistent UI behavior.

3. Performance Optimization

Problem: The webpage loads too slowly, affecting user experience and SEO ranking.

How AI Helps:

• Identifies slow-loading resources, such as unoptimized images, large CSS/JS files, and third-party scripts.
• Suggests image compression and lazy loading to speed up rendering.
• Highlights unnecessary JavaScript execution that may be slowing down interactivity.
• Recommends caching strategies to improve page speed and reduce server load.
• Detects render-blocking elements that delay the loading of critical content.

4. Accessibility Testing

Problem: The web application does not comply with WCAG (Web Content Accessibility Guidelines).

>How AI Helps:

• Identifies missing alt text for images, affecting screen reader users.
• Highlights low color contrast issues that make text hard to read.
• Suggests adding ARIA roles and labels to improve assistive technology compatibility.
• Ensures proper keyboard navigation, making the site accessible for users who rely on tab-based navigation.
• Detects form accessibility issues, such as missing labels or incorrectly grouped form elements.

5. Security and Compliance Checks

Problem: The website has security vulnerabilities that could expose sensitive user data.

How AI Helps:

• Detects insecure HTTP requests that should use HTTPS.
• Highlights CORS misconfigurations that may expose sensitive data.
• Identifies missing security headers, such as Content-Security-Policy, X-Frame-Options, and Strict-Transport-Security.
• Flags exposed API keys or credentials in the network logs.
• Suggests best practices for secure authentication and session management.

6. Troubleshooting JavaScript Errors

Problem: JavaScript errors are causing unexpected behavior in the web application.

>How AI Helps:

• Analyzes console errors and suggests fixes.
• Identifies undefined variables, syntax errors, and missing dependencies.
• Helps debug event listeners and asynchronous function execution.
• Suggests ways to optimize JavaScript performance to avoid slow interactions.

7. Cross-Browser Compatibility Testing

Problem: The website works fine in Chrome but breaks in Firefox or Safari.

How AI Helps:

• Highlights CSS properties that may not be supported in some browsers.
• Detects JavaScript features that are incompatible with older browsers.
• Suggests polyfills and workarounds to ensure cross-browser support.

8. Enhancing Test Automation Strategies

Problem: Automated tests fail due to dynamic elements or inconsistent behavior.

How AI Helps:

• Identifies flaky tests caused by timing issues and improper waits.
• Suggests better locators for web elements to improve test reliability.
• Provides workarounds for handling dynamic content (e.g., pop-ups, lazy-loaded elements).
• Helps in writing efficient automation scripts by improving test structure.

Getting Started with Chrome DevTools AI Ask Assistant

Before diving into specific tabs, let’s first enable the AI Ask Assistant in Chrome DevTools:

Step 1: Open Chrome DevTools

• Open Google Chrome.
• Navigate to the web application under test.
• Right-click anywhere on the page and select Inspect, or press F12 / Ctrl + Shift + I (Windows/Linux) or Cmd + Option + I (Mac).
• In the DevTools panel, click on the Experiments settings.

Step 2: Enable AI Ask Assistant

• Enable AI Ask Assistant if it’s available in your Chrome version.
• Restart DevTools for the changes to take effect.

Using AI Ask Assistant in the Network Tab for Testers

The Network tab is crucial for testers to validate API requests, analyze performance, and diagnose failed network calls. The AI Ask Assistant enhances this by providing instant insights and suggestions.

Step 1: Open the Network Tab

• Open DevTools (F12 / Ctrl + Shift + I).
• Navigate to the Network tab.
• Reload the page (Ctrl + R / Cmd + R) to capture network activity.

Step 2: Ask AI to Analyze a Network Request

• Identify a specific request in the network log (e.g., API call, AJAX request, third-party script load, etc.).
• Right-click on the request and select Ask AI Assistant.
• Ask questions like:
• “Why is this request failing?”
• “What is causing the delay in response time?”
• “Are there any CORS-related issues in this request?”
• “How can I debug a 403 Forbidden error?”

Step 3: Get AI-Powered Insights for Testing

• AI will analyze the request and provide explanations.
• It may suggest fixes for failed requests (e.g., CORS issues, incorrect API endpoints, authentication errors).
• You can refine your query for better insights.

Step 4: Debug Network Issues from a Tester’s Perspective

Some example problems AI can help with:

• API Testing Issues: AI explains 404, 500, or 403 errors.
• Performance Bottlenecks: AI suggests ways to optimize API response time and detect slow endpoints.
• Security Testing: AI highlights CORS issues, mixed content, and security vulnerabilities.
• Data Validation: AI helps verify response payloads against expected values.

Here I asked: “What is causing the delay in response time?”

Using AI Ask Assistant in the Elements Tab for UI Testing

The Elements tab is used to inspect and manipulate HTML and CSS. AI Ask Assistant helps testers debug UI issues efficiently.

Step 1: Open the Elements Tab

• Open DevTools (F12 / Ctrl + Shift + I).
• Navigate to the Elements tab.

Step 2: Use AI for UI Debugging

• Select an element in the HTML tree.
• Right-click and choose Ask AI Assistant.
• Ask questions like:
• “Why is this button not clickable?”
• “What styles are affecting this dropdown?”
• “Why is this element overlapping?”
• “How can I fix responsiveness issues?”

Practical Use Cases for Testers

1. Debugging a Failed API Call in a Test Case

• Open the Network tab → Select the request → Ask AI why it failed.
• AI explains 403 error due to missing authentication.
• Follow AI’s solution to add the correct headers in API tests.

2. Identifying Broken UI Elements

• Open the Elements tab → Select the element → Ask AI why it’s not visible.
• AI identifies display: none in CSS.
• Modify the style based on AI’s suggestion and verify in different screen sizes.

3. Validating Page Load Performance in Web Testing

• Open the Network tab → Ask AI how to optimize resources.
• AI suggests reducing unnecessary JavaScript and compressing images.
• Implement suggested changes to improve performance and page load times.

4. Identifying Accessibility Issues

• Use the Elements tab → Inspect accessibility attributes.
• Ask AI to suggest ARIA roles and label improvements.
• Verify compliance with WCAG guidelines.

Conclusion

The AI Ask Assistant in Chrome DevTools makes debugging faster and more efficient by providing real-time AI-driven insights. It helps testers and developers quickly identify and fix network issues, UI bugs, performance bottlenecks, security risks, and accessibility concerns, ensuring high-quality applications. While AI tools improve efficiency, expert testing is essential for delivering reliable software. Codoid, a leader in software testing, specializes in automation, performance, accessibility, security, and functional testing. With industry expertise and cutting-edge tools, Codoid ensures high-quality, seamless, and secure applications across all domains.

Frequently Asked Questions