Generative Development Framework
GDF.ai
  • Intro to GDF-FSE
    • Generative AI, Large Language Models, ChatGPT?
    • Knowledge Areas
    • Access a Chat Based LLM
    • Why GDF?
    • Expectations
  • Limitations
  • Prompting
    • Prompt Patterns
    • Prompt Context
    • Prompt Stores
    • Prompt Operators
    • Prompt Chaining
  • Security
    • Protecting Data
    • Protecting Application Security
    • Protecting Intellectual Property
    • Protection Stores
    • AI Security Assessments and Penetration Testing
    • Social Engineering Testing with AI
  • Subject Knowledge Areas
    • Ideation
      • Identifying a Problem Statement
      • Plan and Prioritize Features
      • Develop User Stories
      • Requirement Gathering
      • Ideation Prompting
      • Ideation Template
    • Specification
      • Specifying Languages
      • Specifying Libraries
      • Specifying Project Structures
      • Specify Schemas
      • Specifying Elements
      • Specifying API Specs
    • Generation
      • Generating UI Elements
      • Generating Mock Data
      • Generating Schemas
      • Generating Parsers
      • Generating Databases
      • Generate Functions
      • Generate APIs
      • Generate Diagrams
      • Generating Documentation
    • Transformation
      • Converting Languages
      • Converting Libraries
    • Replacement
      • Replacing Functions
      • Replacing Data Types
    • Integration
      • Connecting UI Components
      • Connecting UI to Backend
      • Connecting Multiple Services Together
      • Connecting Cloud Infrastructure (AWS)
    • Separation
      • Abstraction
      • Model View Controller (MVC)
    • Consolidation
      • Combining UI Elements
      • Deduplicating Code Fragments
    • Templating
      • Layouts
      • Schemas
      • Project Structures
      • Content Management Systems
    • Visualization
      • General Styling
      • Visual Referencing
      • Visual Variations
    • Verification
      • Test Classes
      • Logging and Monitoring
      • Automated Testing
      • Synthetic Monitoring
    • Implementation
      • Infrastructure
      • DevOps / Deployment
    • Optimization
      • General Optimization
      • Performance Monitoring
      • Code Review
  • Guidance
    • Business Process
    • Regulatory Guidance
  • Generative Pipelines
  • Troubleshooting
    • Client Side Troubleshooting
    • Server Side Troubleshooting
    • Troubleshooting with AI
    • Documentation
    • Infrastructure Engineering
  • Terminology
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  • Summary
  • Example 1: Selenium for UI Testing
  • Prompt
  • Response
  • Example 2: Jest for Server-Side Testing
  • Prompt
  • Response

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  1. Subject Knowledge Areas
  2. Verification

Automated Testing

Automated testing helps validate functionality and performance within your application

Automated testing is the process of using software tools to run predefined tests on a software application or system, without the need for manual intervention. In the context of JavaScript, automated testing involves using tools and frameworks to write and run automated tests on JavaScript code, such as web applications, libraries, or APIs.

Summary

Incorporating generative AI in the software development lifecycle can significantly boost efficiency and quality, especially when used for automated testing. By validating code generated by generative AI, developers can ensure that their applications are robust, secure, and performant.

Example 1: Selenium for UI Testing

Selenium is a popular browser automation tool that allows you to write and run automated tests for web applications. By using generative AI to create test cases, you can ensure comprehensive test coverage and validate the code created by the AI.

Prompt

prompt
Create a code snippet for automating a search on the below component using Selenium WebDriver in Node.js, where the search term is 'mountain bike' and the expected page title is 'Mountain Bikes - Bicycle Rental App.

{component_code}

Response

const { Builder, By, Key, until } = require('selenium-webdriver');

(async function example() {
  const driver = await new Builder().forBrowser('chrome').build();
  try {
    await driver.get('http://localhost:3000/bicycle-rental-app');
    await driver.findElement(By.name('search')).sendKeys('mountain bike', Key.RETURN);
    await driver.wait(until.titleIs('Mountain Bikes - Bicycle Rental App'), 1000);
  } finally {
    await driver.quit();
  }
})();

In this example, Selenium WebDriver is used to automate browser actions for a bicycle rental application. The test navigates to the application, enters "mountain bike" into the search field, and checks that the resulting page title includes "Mountain Bikes."

Example 2: Jest for Server-Side Testing

Jest is a popular testing framework for JavaScript, commonly used for testing server-side applications and APIs. By leveraging generative AI to create test cases, you can validate the code generated by the AI and ensure the server-side functionality is reliable and secure.

Prompt

prompt
Generate a code snippet for testing this API: {bicycle_rental_api_code}

Response

const axios = require('axios');
const { API_URL } = process.env;

describe('Bicycle Rental API', () => {
  test('Should return a list of bicycles', async () => {
    const response = await axios.get(`${API_URL}/api/bicycles`);
    expect(response.status).toBe(200);
    expect(response.data).toBeInstanceOf(Array);
  });

  test('Should return a specific bicycle', async () => {
    const response = await axios.get(`${API_URL}/api/bicycles/1`);
    expect(response.status).toBe(200);
    expect(response.data).toMatchObject({
      id: 1,
      name: 'Example Bicycle',
      type: 'Mountain',
    });
  });
});

In this example, Jest is used to test a Bicycle Rental API. The tests check that the API returns a list of bicycles and retrieves a specific bicycle by its ID.

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