AI Tester Playground

Welcome to the AI Tester Playground, an interactive environment designed to leverage AI for various Quality Assurance (QA) tasks. This repository provides a set of tools and examples that demonstrate how to analyze error messages, generate test descriptions, and create comprehensive test cases using OpenAI's language models.

Table of Contents

• Features
• Prerequisites
• Installation
• Configuration
• Running the Application
• Using the Examples
• API Endpoints
• Viewing Output

Features

• Error Message Analysis: Analyze and extract key information from error stack traces.
• Test Description Generation: Create detailed test descriptions based on feature specifications and test case titles.
• Test Case Generation: Generate comprehensive and categorized test cases from business specifications.
• Test Result Analysis: Provide actionable recommendations based on test failures.
• Extensible Architecture: Easily add new AI-powered QA tools by extending controllers and services.

Prerequisites

Before you begin, ensure you have met the following requirements:

• Node.js: Version 14 or higher. You can download it from Node.js Official Website.
• OpenAI API Key: Obtain an API key from OpenAI.

Installation

1. Clone the Repository

   git clone https://github.com/agyulai/ai-tester-playground.git
   cd ai-tester-playground

2. Install Dependencies

   Navigate to the project directory and install the necessary dependencies using npm:

   npm install

Configuration

1. Environment Variables

   Create a .env file in the root directory of the project to store your environment variables. This file should contain your OpenAI API key and any other necessary configurations.

   OPENAI_API_KEY=your_openai_api_key_here
   PORT=23500
   VERBOSE=false

   • OPENAI_API_KEY: Your OpenAI API key.
   • PORT: (Optional) The port on which the server will run. Defaults to 23500 if not specified.
   • VERBOSE: (Optional) Set to true to enable verbose logging.

Running the Application

Start the Express server by running:

npm start

The server will start on the specified port (default is 23500). You can verify it's running by accessing the health check endpoint:

http://localhost:23500/health

You should receive a JSON response:

{
  \"status\": \"UP\"
}

Using the Examples

The repository includes several example scripts that demonstrate how to interact with the AI-powered endpoints. These examples showcase how to analyze error messages, generate test descriptions, create test cases, and analyze test results.

Example Scripts

1. Test Description Example

   Generates a test description based on a feature specification and test case title.

   node src/examples/test-description-example.js

   Output: A test-description-response.md file containing the generated test description.

2. Test Error Example

   Analyzes an error message to extract relevant information.

   node src/examples/test-error-example.js

   Output: A test-error.md file containing the analysis.

3. Test Generation Example

   Generates test cases from a business specification.

   node src/examples/test-generation-example.js

   Output: A test-generation-response.md file containing the generated test cases.

4. Test Result Example

   Analyzes test results to provide actionable recommendations.

   node src/examples/test-result-example.js

   Output: A test-result.md file containing the analysis.

API Endpoints

The application exposes several API endpoints under the /ai path. Below are the available routes and their functionalities:

• POST /ai/test-result/analyse

  Analyzes test results to provide recommendations.

  Sample Request

  curl -X POST http://localhost:23500/ai/test-result/analyse \
    -H "Content-Type: application/json" \
    -d '{
          "userMessage": "<YOUR_ERROR_MESSAGE_HERE>"
        }'

• POST /ai/test-error/analyse

  Analyzes error messages to extract failure points and related files.

  Sample Request

  curl -X POST http://localhost:23500/ai/test-error/analyse \
    -H "Content-Type: application/json" \
    -d '{
          "userMessage": "<YOUR_ERROR_MESSAGE_HERE>"
        }'

• POST /ai/test-description/generate

  Generates detailed test descriptions based on provided specifications.

  Sample Request

  curl -X POST http://localhost:23500/ai/test-description/generate \
    -H "Content-Type: application/json" \
    -d '{
          "featureSpecification": "<YOUR_FEATURE_SPECIFICATION_HERE>",
          "testCaseTitle": "<YOUR_TEST_CASE_TITLE_HERE>"
        }'
  

• POST /ai/test-generation/generate

  Generates comprehensive test cases from business specifications.

  Sample Request

  curl -X POST http://localhost:23500/ai/test-generation/generate \
    -H "Content-Type: application/json" \
    -d '{
          "aiSettings": {
            "aiModel": "gpt-4o",
            "customTemperature": 0.7
          },
          "messages": [
            {
              "author": "system",
              "type": "specification",
              "message": "<YOUR_BUSINESS_SPECIFICATION_HERE>"
            },
            {
              "author": "user",
              "message": "<YOUR_USER_MESSAGE_HERE>"
            }
          ]
        }'

Viewing Output

The example scripts save their outputs as Markdown (.md) files in the src/examples/ directory. You can view these files using any Markdown viewer or text editor to see the AI-generated content.

For example, after running the test description example:

• File: src/examples/test-description-response.md
• Content: Detailed test description based on your input.

Happy Testing!