6 posts tagged with "Automation"

The landscape of software testing has evolved dramatically over the past decade, with artificial intelligence emerging as a transformative force in quality assurance. While traditional testing methods have served us well, the complexity of modern user interfaces demands more sophisticated approaches. Enter AI-assisted visual testing - a revolutionary methodology that goes far beyond simple screenshot comparisons to deliver intelligent, context-aware validation of user interfaces.

The Evolution beyond Traditional Visual Testing​

Traditional visual testing has long relied on pixel-perfect screenshot comparisons, an approach that, while useful, comes with significant limitations. These methods often flag irrelevant differences as failures - a shifted timestamp, a different user avatar, or dynamic content that changes between test runs. The result is a high rate of false positives that can overwhelm testing teams and reduce confidence in the testing process.

AI-assisted visual testing represents a paradigm shift, introducing intelligence that can differentiate between meaningful visual regressions and inconsequential variations. By leveraging computer vision, machine learning, and natural language processing, these systems can understand the intent behind UI elements and focus on what truly matters for user experience.

Computer Vision and AI in UI Layout Validation​

Modern AI-powered visual testing tools employ sophisticated computer vision algorithms to analyze user interfaces at a semantic level. Rather than simply comparing pixels, these systems can identify and categorize UI components - buttons, forms, navigation elements, content areas - and understand their relationships within the overall layout structure.

This semantic understanding enables several powerful capabilities. The AI can detect when a button has moved to an unexpected location, when text alignment has shifted in a way that affects readability, or when color contrast changes might impact accessibility. More importantly, it can distinguish between these meaningful changes and superficial variations that don't affect the user experience.

Intelligent Screenshot Comparison: Focusing on What Matters​

One of the most significant advances in AI-assisted visual testing is the development of intelligent screenshot comparison algorithms. These systems use deep learning models to understand which visual differences are significant and which should be ignored.

The AI can be trained to recognize dynamic elements that naturally change between test runs - such as timestamps, user-generated content, or rotating banners - and exclude these from comparison. This dramatically reduces false positives while ensuring that genuine visual regressions are caught.

Accessibility-Focused Testing Through AI Analysis​

AI-powered accessibility testing can analyze visual designs for color contrast ratios, text readability, and visual hierarchy issues that might impact users with disabilities. Computer vision models can detect when text is too small, when color choices create insufficient contrast, or when interactive elements are placed too close together for users with motor difficulties.

More sophisticated AI systems can even simulate different visual impairments and test how interfaces perform under various accessibility conditions.

Detecting Visual Regressions and UX Issues​

AI-assisted visual testing fills the gap by detecting subtle problems that might not cause functional failures but could harm the user experience. These systems can identify when loading states take too long to resolve, when animations feel jarring or inconsistent, or when visual feedback for user actions is inadequate.

Advanced Pattern Recognition and Anomaly Detection​

AI-powered visual testing systems excel at pattern recognition, learning from extensive datasets of UI designs to identify both common patterns and unusual anomalies. This capability enables them to flag potential issues that might not be immediately obvious to human testers.

The Future of Intelligent UI Validation​

Natural language processing could enable testers to describe desired visual states in plain English, with AI translating these descriptions into comprehensive test scenarios. Computer vision models could understand brand guidelines and design principles, automatically flagging deviations from established visual standards.

Implementing AI-Assisted Visual Testing in Your Organization​

Start by identifying the most critical user journeys and interfaces in your application. These high-impact areas are ideal candidates for AI-powered testing. Begin with pilot projects that can demonstrate value while minimizing risk.

Integration with existing testing frameworks and CI/CD pipelines is crucial for adoption.

Measuring Success and ROI​

Track metrics such as time saved, reduced false positives, confidence in visual testing results, and reduction in visual bugs reaching production. When testing becomes more reliable, development teams can move faster while maintaining quality standards.

Conclusion: The Intelligent Future of Visual Testing​

AI-assisted visual testing represents a fundamental shift in how we approach user interface validation. For organizations willing to invest in this technology, the benefits extend far beyond improved test coverage. The future of visual testing is intelligent, and that future is available today for those ready to embrace it.

Modern applications are evolving fast, and so should our testing. Manual test case writing can't keep pace with complex UIs, rapid development, and ever-increasing test coverage demands. This is where AI-powered test generation shines.

In this article, you'll discover how to leverage GPT models to generate Playwright tests automatically from user stories, mockups, and API specs—cutting test creation time by up to 80% and boosting consistency.

🚀 The AI Testing Revolution – Why Now?​

Web applications today have:

• Dynamic UIs
• Complex workflows
• Rapid iteration cycles

Manual testing falls short due to:

• ⏳ Time-consuming scripting
• 🎯 Inconsistent test quality
• 🛠 High maintenance overhead
• 📉 Skill gaps in Playwright expertise

IMPORTANT: AI-generated tests solve these issues by converting high-level specifications into consistent, executable scripts—within minutes.

🔍 Core Use Cases​

1. 🧾 User Story → Test Script​

User Story: “As a customer, I want to add items to my shopping cart, modify quantities, and checkout.”

An LLM can auto-generate Playwright tests for:

• Item addition/removal
• Quantity updates
• Cart persistence
• Checkout validation

2. 🧩 UI Mockups → UI Tests​

From screenshots or Figma mockups, AI identifies UI components and generates:

• Field validation tests
• Button click paths
• Navigation workflows

3. 📡 API Docs → Integration Tests​

Given OpenAPI specs or Swagger files, AI generates:

• API response validators
• Auth flow tests
• Data transformation checks

4. 🔁 Regression Suite Generation​

Scan your codebase and let AI generate:

• Tests for business-critical paths
• Version-aware regression scenarios
• Cross-browser validations

✍️ Prompt Engineering: The Secret Sauce​

High-quality output requires high-quality prompts.

TIP: Craft prompts like you're onboarding a new teammate. Be specific, structured, and clear.

🧠 Tips for Better Prompts​

1. Context-Rich: Include business logic, user persona, architecture info.
2. Structured Templates: Use consistent input formats.
3. Code Specs: Tell the AI about your conventions, selectors, assertions.

🛠️ How AI Builds Playwright Tests​

The test generation pipeline includes:

• Requirement parsing
• Scenario/edge case detection
• Selector/locator inference
• Assertion strategy
• Setup/teardown

TIP: AI-generated tests often contain proper waits, good selectors, and meaningful error handling out of the box.

🔬 Examples by Domain​

🛒 E-commerce Cart​

• Add/remove items
• Update quantity
• Validate prices
• Empty cart flows

📋 Form Validation​

• Required field checks
• Format enforcement
• Success + error paths
• Accessibility & UX feedback

🔄 API Integration​

• GET/POST/PUT/DELETE tests
• 401/403/500 handlers
• JSON schema validation
• Token expiration

⚖️ AI vs. Manual Tests​

IMPORTANT: Use AI for breadth, and humans for depth. Combine both for maximum coverage.

🧠 Advanced Prompt Engineering​

🧪 Multi-Shot Prompting​

Provide several examples for the AI to follow.

🧵 Chain-of-Thought Prompting​

Ask AI to reason before generating.

🔁 Iterative Refinement​

Start, review, improve. Repeat.

🎭 Role-Based Prompting​

“Act like a senior QA” gets better results than generic prompts.

🧩 Integrating AI into Your CI/CD Workflow​

Phase 1: Foundation​

• Define test structure
• Create reusable prompt templates
• Set up review pipelines

Phase 2: Pilot​

• Begin with UI flows, login, cart, or forms
• Involve human reviewers

Phase 3: Scale​

• Add coverage for APIs, edge cases
• Train team on prompt best practices

🛡️ Maintaining AI-Generated Tests​

Use tagging to distinguish AI-generated files.

Review regularly for:

• Fragile selectors
• Obsolete flows
• Over-tested paths

TIP: Use GitHub Actions to auto-regenerate stale tests weekly.

📈 KPIs to Track​

🔮 Future Trends​

• 🖼️ Visual test generation from screenshots
• 🗣️ Natural language test execution (e.g., “Test checkout flow”)
• 🔁 Adaptive test regeneration on UI changes
• 🔍 Predictive test flakiness detection

✅ Final Thoughts​

AI doesn’t replace your QA team—it supercharges them.

By combining:

• GPT-based prompt generation
• Human review and refinement
• CI/CD integration

You can reduce time-to-test by weeks while increasing test quality.

CTA: Try our GitHub starter kit and let AI handle the boring test boilerplate while your team focuses on real innovation.

The future of testing isn’t just faster—it’s intelligent.

This guide will walk you through the process of creating a web application using Next.js, React, and Tailwind CSS. The application will include pages for the homepage, login, register, and dashboard, and will implement a persistent Redux store with types, actions, reducers, and sagas. Additionally, it will support both database and social login, feature light and dark themes, and include header and footer components. We will also set up private and public routes and provide commands for building, starting, and deploying the application on Vercel.

Prerequisites​

• Node.js installed on your machine
• Basic knowledge of JavaScript and React
• Familiarity with Redux and Next.js

1. Setting Up the Next.js Project​

1. Create a new Next.js application:

npx create-next-app my-next-app
cd my-next-app

Use below command to create a new Next.js application with TypeScript:

npx create-next-app my-next-app --typescript
cd my-next-app

2. Install Tailwind CSS: Follow the official Tailwind CSS installation guide for Next.js:

npm install -D tailwindcss postcss autoprefixer
npx tailwindcss init -p

3. Configure Tailwind CSS: Update tailwind.config.js:

module.exports = {
  content: ['./pages/**/*.{js,ts,jsx,tsx}', './components/**/*.{js,ts,jsx,tsx}'],
  theme: {
    extend: {},
  },
  plugins: [],
};

4. Add Tailwind to your CSS: In styles/globals.css, add the following:

@tailwind base;
@tailwind components;
@tailwind utilities;

2. Setting Up Redux​

1. Install Redux and related libraries:

npm install redux react-redux redux-saga

2. Create Redux Store: Create a folder named store and add the following files:

   • store/index.js:

   import { createStore, applyMiddleware } from 'redux';
   import createSagaMiddleware from 'redux-saga';
   import rootReducer from './reducers';
   import rootSaga from './sagas';
   
   const sagaMiddleware = createSagaMiddleware();
   const store = createStore(rootReducer, applyMiddleware(sagaMiddleware));
   
   -sagaMiddleware.run(rootSaga);
   
   export default store;
   

   • store/reducers/index.js:

   import { combineReducers } from 'redux';
   import authReducer from './authReducer';
   
   const rootReducer = combineReducers({
     auth: authReducer,
   });
   
   export default rootReducer;
   

   • store/reducers/authReducer.js:

   const initialState = {
     user: null,
     loading: false,
     error: null,
   };
   
   const authReducer = (state = initialState, action) => {
     switch (action.type) {
       case 'LOGIN_REQUEST':
         return { ...state, loading: true };
       case 'LOGIN_SUCCESS':
         return { ...state, loading: false, user: action.payload };
       case 'LOGIN_FAILURE':
         return { ...state, loading: false, error: action.payload };
       default:
         return state;
     }
   };
   
   export default authReducer;
   

   • store/sagas/index.js:

   import { all } from 'redux-saga/effects';
   import authSaga from './authSaga';
   
   export default function\* rootSaga() {
   yield all([
   authSaga(),
   ]);
   }
   

   • store/sagas/authSaga.js:

   import { call, put, takeEvery } from 'redux-saga/effects';
   import { loginService } from '../services/authService';
   
   function\* login(action) {
   try {
   const user = yield call(loginService, action.payload);
   yield put({ type: 'LOGIN_SUCCESS', payload: user });
   } catch (error) {
   yield put({ type: 'LOGIN_FAILURE', payload: error.message });
   }
   }
   
   export default function\* authSaga() {
   yield takeEvery('LOGIN_REQUEST', login);
   }
   

3. Create Services for MySQL: Create a folder named services and add the following file:

   • services/authService.js:

import axios from 'axios';

export const loginService = async (credentials) => {
  const response = await axios.post('/api/login', credentials);
  return response.data;
};

3. Creating Pages​

1. Homepage (pages/index.js):

import Head from 'next/head';

const Home = () => {
  return (
    <div>
      <Head>
        <title>Home</title>
      </Head>
      <h1 className="text-4xl">Welcome to My Next.js App</h1>
    </div>
  );
};

export default Home;

2. Login Page (pages/login.js):

import { useDispatch } from 'react-redux';

const Login = () => {
  const dispatch = useDispatch();

  const handleLogin = (e) => {
    e.preventDefault();
    const credentials = {
      username: e.target.username.value,
      password: e.target.password.value,
    };
    dispatch({ type: 'LOGIN_REQUEST', payload: credentials });
  };

  return (
    <form onSubmit={handleLogin}>
      <input name="username" type="text" placeholder="Username" required />
      <input name="password" type="password" placeholder="Password" required />
      <button type="submit">Login</button>
    </form>
  );
};

export default Login;

3. Register Page (pages/register.js):

const Register = () => {
  return (
    <form>
      <input name="username" type="text" placeholder="Username" required />
      <input name="email" type="email" placeholder="Email" required />
      <input name="password" type="password" placeholder="Password" required />
      <button type="submit">Register</button>
    </form>
  );
};

export default Register;

4. Dashboard Page (pages/dashboard.js):

const Dashboard = () => {
  return (
    <div>
      <h1>Dashboard</h1>
    </div>
  );
};

export default Dashboard;

4. Implementing Themes​

1. Create a Theme Context: Create a folder named context and add the following file:

   • context/ThemeContext.js:

import { createContext, useContext, useState } from 'react';

const ThemeContext = createContext();

export const ThemeProvider = ({ children }) => {
  const [theme, setTheme] = useState('light');

  const toggleTheme = () => {
    setTheme((prev) => (prev === 'light' ? 'dark' : 'light'));
  };

  return <ThemeContext.Provider value={{ theme, toggleTheme }}>{children}</ThemeContext.Provider>;
};

export const useTheme = () => useContext(ThemeContext);

2. Wrap the Application with ThemeProvider: In pages/\_app.js, wrap your application with the ThemeProvider:

import { ThemeProvider } from '../context/ThemeContext';

function MyApp({ Component, pageProps }) {
  return (
    <ThemeProvider>
      <Component {...pageProps} />
    </ThemeProvider>
  );
}

export default MyApp;

5. Creating Header and Footer Components​

1. Header Component (components/Header.js):

import Link from 'next/link';
import { useTheme } from '../context/ThemeContext';

const Header = () => {
  const { toggleTheme } = useTheme();

  return (
    <header>
      <nav>
        <Link href="/">Home</Link>
        <Link href="/login">Login</Link>
        <Link href="/register">Register</Link>
        <Link href="/dashboard">Dashboard</Link>
        <button onClick={toggleTheme}>Toggle Theme</button>
      </nav>
    </header>
  );
};

export default Header;

2. Footer Component (components/Footer.js):

const Footer = () => {
  return (
    <footer>
      <p>© 2024 My Next.js App</p>
    </footer>
  );
};

export default Footer;

3. Include Header and Footer in Pages: Update your pages to include the Header and Footer components:

import Header from '../components/Header';
import Footer from '../components/Footer';

const Home = () => {
  return (
    <div>
      <Header />
      <h1>Welcome to My Next.js App</h1>
      <Footer />
    </div>
  );
};

export default Home;

6. Implementing Private and Public Routes​

1. Create a Higher-Order Component for Route Protection: Create a folder named hocs and add the following file:

   • hocs/withAuth.js:

import { useSelector } from 'react-redux';
import { useRouter } from 'next/router';
import React from 'react';

const withAuth = (WrappedComponent) => {
  const AuthenticatedComponent = (props) => {
    const router = useRouter();
    const user = useSelector((state) => state.auth.user);

    // Redirect to login if user is not authenticated
    React.useEffect(() => {
      if (!user) {
        router.push('/login');
      }
    }, [user, router]);

    return user ? <WrappedComponent {...props} /> : null;
  };

  return AuthenticatedComponent;
};

export default withAuth;

2. Protect the Dashboard Page: Update your Dashboard page to use the withAuth HOC:

   • pages/dashboard.js:

   import withAuth from '../hocs/withAuth';
   
   const Dashboard = () => {
     return (
       <div>
         <h1>Dashboard</h1>
       </div>
     );
   };
   
   export default withAuth(Dashboard);
   

3. Public Route Example: For pages like login and register, you can create a similar HOC to prevent logged-in users from accessing these pages:

   • hocs/withPublic.js:

   import { useSelector } from 'react-redux';
   import { useRouter } from 'next/router';
   import React from 'react';
   
   const withPublic = (WrappedComponent) => {
     const PublicComponent = (props) => {
       const router = useRouter();
       const user = useSelector((state) => state.auth.user);
   
       // Redirect to dashboard if user is already authenticated
       React.useEffect(() => {
         if (user) {
           router.push('/dashboard');
         }
       }, [user, router]);
   
       return <WrappedComponent {...props} />;
     };
   
     return PublicComponent;
   };
   
   export default withPublic;
   

4. Update Login and Register Pages: Use withPublic on the login and register pages:

   • pages/login.js:

   import { useDispatch } from 'react-redux';
   import withPublic from '../hocs/withPublic';
   
   const Login = () => {
     const dispatch = useDispatch();
   
     const handleLogin = (e) => {
       e.preventDefault();
       const credentials = {
         username: e.target.username.value,
         password: e.target.password.value,
       };
       dispatch({ type: 'LOGIN_REQUEST', payload: credentials });
     };
   
     return (
       <form onSubmit={handleLogin}>
         <input name="username" type="text" placeholder="Username" required />
         <input name="password" type="password" placeholder="Password" required />
         <button type="submit">Login</button>
       </form>
     );
   };
   
   export default withPublic(Login);
   

   • pages/register.js:

   import withPublic from '../hocs/withPublic';
   
   const Register = () => {
     return (
       <form>
         <input name="username" type="text" placeholder="Username" required />
         <input name="email" type="email" placeholder="Email" required />
         <input name="password" type="password" placeholder="Password" required />
         <button type="submit">Register</button>
       </form>
     );
   };
   
   export default withPublic(Register);
   

7. Implementing Light and Dark Themes​

1. Add Theme Classes: Update your ThemeProvider to apply dark and light theme classes to the app.

   • context/ThemeContext.js:

   const ThemeProvider = ({ children }) => {
     const [theme, setTheme] = useState('light');
   
     const toggleTheme = () => {
       setTheme((prev) => (prev === 'light' ? 'dark' : 'light'));
     };
   
     return (
       <div className={theme}>
         <ThemeContext.Provider value={{ theme, toggleTheme }}>{children}</ThemeContext.Provider>
       </div>
     );
   };
   

2. Add Tailwind CSS for Themes: In your styles/globals.css, include styles for dark mode:

/* Dark mode styles */ 
.dark {
  @apply bg-gray-900 text-white;
}

3. Update Components: Ensure your components utilize the theme classes accordingly.

8. Commands for Build, Start, and Deploy on Vercel​

1. Build Command: Add the following scripts to your package.json file:

"scripts": {
"dev": "next dev",
"build": "next build",
"start": "next start",
"deploy": "vercel --prod"
}

2. Deploying to Vercel:

   • First, install the Vercel CLI globally if you haven't already:

   npm install -g vercel
   

   • To deploy your application, run the following command in your project directory:

   vercel
   

   • Follow the prompts to link your project to a Vercel account.

   To deploy to production, use:

   npm run deploy
   

Conclusion​

You have now set up a Next.js application using React and Tailwind CSS with a complete functionality including user authentication, light and dark themes, and routing. You can further extend this application by adding more features as needed. Happy coding!

Additional Resources​

• Next.js Documentation
• Redux Documentation
• Tailwind CSS Documentation
• Vercel Documentation

This guide serves as a comprehensive starting point. Feel free to customize and enhance your application as you see fit!

GitHub Copilot is an AI-powered coding assistant that helps developers by providing code suggestions and automating repetitive coding tasks. This document outlines the key commands, features, and usage scenarios for GitHub Copilot.

Activation and Basic Commands​

Comment-Based Commands​

GitHub Copilot can be directed using comments to produce specific code snippets, examples, or logic.

Code Completion Commands​

GitHub Copilot can automatically complete your code based on the context provided by your current file.

Advanced Suggestions​

GitHub Copilot can be used for more advanced coding scenarios, including refactoring, generating boilerplate code, and handling specific languages or frameworks.

Testing and Debugging​

GitHub Copilot can assist with writing tests, debugging code, and providing potential fixes.

Copilot in Pair Programming​

When paired with another developer or using pair programming practices, GitHub Copilot can still assist without taking over the coding session.

Language-Specific Commands​

GitHub Copilot is language-agnostic, but you can tailor its suggestions to specific languages by using commands or comments relevant to the language's syntax or idioms.

Settings and Customization​

Users can customize how GitHub Copilot behaves within their IDE or editor.

Miscellaneous​

Other useful commands and features that enhance your coding experience with GitHub Copilot.

This guide should help you make the most of GitHub Copilot's capabilities, enhancing your productivity and coding experience.

This document provides a detailed overview of commonly used Git commands, organized by category.

Git Configuration​

Creating Repositories​

Staging and Committing​

Branching and Merging​

Remote Repositories​

Inspecting and Comparing​

Undoing Changes​

Tagging​

Stashing​

Submodules​

Miscellaneous​

Useful Aliases​

This guide provides a solid foundation for working with Git, whether you're just getting started or need a quick reference for more advanced tasks.

AI agents represent the next evolution in artificial intelligence - autonomous systems that can perceive their environment, make decisions, and take actions to achieve specific goals. This comprehensive guide explores the fundamentals of AI agents and provides practical insights into building effective agent-based systems.