A modern React-based project utilizing the latest frontend technologies and tools for building responsive web applications.

• React 18 - React version with improved rendering and concurrent features
• Vite - Lightning-fast build tool and development server
• Redux Toolkit - State management with simplified Redux setup
• TailwindCSS - Utility-first CSS framework with extensive customization
• React Router v6 - Declarative routing for React applications
• Data Visualization - Integrated D3.js and Recharts for powerful data visualization
• Form Management - React Hook Form for efficient form handling
• Animation - Framer Motion for smooth UI animations
• Testing - Jest and React Testing Library setup

• Node.js (v14.x or higher)
• npm or yarn

1. Install dependencies:

   npm install
   # or
   yarn install

2. Start the development server:

   npm start
   # or
   yarn start

react_app/
├── public/             # Static assets
├── src/
│   ├── components/     # Reusable UI components
│   ├── pages/          # Page components
│   ├── styles/         # Global styles and Tailwind configuration
│   ├── App.jsx         # Main application component
│   ├── Routes.jsx      # Application routes
│   └── index.jsx       # Application entry point
├── .env                # Environment variables
├── index.html          # HTML template
├── package.json        # Project dependencies and scripts
├── tailwind.config.js  # Tailwind CSS configuration
└── vite.config.js      # Vite configuration

To add new routes to the application, update the Routes.jsx file:

import { useRoutes } from "react-router-dom";
import HomePage from "pages/HomePage";
import AboutPage from "pages/AboutPage";

const ProjectRoutes = () => {
  let element = useRoutes([
    { path: "/", element: <HomePage /> },
    { path: "/about", element: <AboutPage /> },
    // Add more routes as needed
  ]);

  return element;
};

This project uses Tailwind CSS for styling. The configuration includes:

• Forms plugin for form styling
• Typography plugin for text styling
• Aspect ratio plugin for responsive elements
• Container queries for component-specific responsive design
• Fluid typography for responsive text
• Animation utilities

The app is built with responsive design using Tailwind CSS breakpoints.

Build the application for production:

npm run build

• Built with Rocket.new
• Powered by React and Vite
• Styled with Tailwind CSS

Perfect. Here’s the complete project overview with a clear explanation of what we are doing, step by step. This ties together all your requirements into one cohesive, agent-based, modular test case generation system that can continuously learn and adapt.

⸻

PROJECT NAME:

Hybrid Agent-Based Test Case Generation System

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PROJECT GOAL:

To build an automated backend system that processes Japanese Excel design documents and generates high-quality test cases using a multi-agent modular pipeline, supports feedback-based continuous training, and is built in a way that is future-ready for API and frontend integration.

⸻

WHAT ARE WE DOING?

In Simple Terms: 1. Input: You provide 10–15 Japanese design documents for training. 2. The system processes each document in four transformation stages using intelligent agents: • Translate → Convert Sentences → Logical Linking → Validate 3. These transformed documents are used to train a hybrid AI model to generate test cases. 4. A second model refines the test cases using the output of the first and all previous inputs. 5. You can later input a new design document, and the system will auto-generate test cases. 6. You can give feedback, and the system will learn and retrain itself to improve over time.

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KEY MODULES IN THE PROJECT

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1. INPUT SETUP • You drop 10–15 raw design documents into a folder. • You also provide sample design + test cases in a separate folder (for training reference). • The system auto-detects and prepares everything for processing.

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2. AGENT-BASED DOCUMENT TRANSFORMATION

Each input document is processed through a multi-step transformation pipeline:

a. Translation Agent • Converts Japanese Excel content into English using MarianMT or an equivalent local model.

b. Sentence Conversion Agent • Turns English content into readable, structured sentence-style format.

c. Logical Linking Agent • Combines content from multiple sheets and creates a logically structured, single-sheet design format.

d. Validator • Performs formatting and business rule validation to ensure the document is clean and consistent.

All these steps produce four output files per document: • translated.xlsx • sentence_converted.xlsx • logical_linked.xlsx • validated.pkl (input features for models)

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3. MODEL TRAINING

Model 1: Initial Test Case Generator • Input: • All four processed documents above • Sample design doc + sample test case file (from sample_folder/) • Output: Initial predicted test cases (stored per document) • Tech: Hybrid approach (rules + XGBoost or similar model)

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Model 2: Test Case Refiner • Input: • Output of Model 1 • All inputs used for Model 1 (to give more context) • Output: Final, refined test cases with higher accuracy • Tech: Second ML model with deeper input context

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4. TEST CASE PREDICTION (INFERENCE) • You provide one new Japanese design document. • The system automatically: • Translates → Converts → Links → Validates • Sends to Model 1 → Model 2 • Generates final test cases and stores them in the output/final_test_cases/ folder.

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5. FEEDBACK LOOP AND CONTINUOUS LEARNING • You provide feedback (structured or sentence-based) via an Excel file. • The Feedback Agent interprets it and retrains: • Model 1 and/or Model 2 • Using both past and new feedback • Feedback and retraining history is logged for transparency.

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6. FUTURE EXTENSIONS

Even though we’re skipping API/frontend for now, the whole backend is built to be: • Modular: Easy to hook endpoints • Configurable: Supports Swagger, group types (API, ESB, ODM) • Frontend-ready: Easy to integrate a UI later via API

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WHY THIS APPROACH WORKS? • Agent-based design makes it flexible and maintainable. • Modular pipeline allows debugging and improvements at each stage. • Continuous training with real-time feedback improves accuracy over time. • Open source only: Fully deployable in secure enterprise systems.

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SUMMARY WORKFLOW

flowchart TD A[Input Design Docs] --> B[Translator Agent] B --> C[Sentence Converter Agent] C --> D[Logical Link Agent] D --> E[Validator + Feature Builder] E --> F[Model 1: Test Case Generator] F --> G[Model 2: Test Case Refiner] G --> H[Final Test Cases Output]

subgraph Training E --> F F --> G end

subgraph Feedback I[Feedback File] --> J[Feedback Agent] J --> K[Retrain Models] K --> F K --> G end

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WHAT’S NEXT?

We now proceed to Step-by-Step Implementation: 1. Set up main.py, config/settings.py 2. Build translator_agent.py and each agent 3. Create the full pipelines and models 4. Implement feedback loop and validators

Let me know if you’re ready, and we’ll start implementation step 1.