🚀 API Testing Automation Tool (With ML Integration)

An advanced API Testing Automation Tool powered by a Machine Learning (ML) model that intelligently classifies API responses and predicts expected outcomes.
It combines a clean frontend interface for user interaction with a robust Python backend that integrates ML-based decision support.

---

🧠 Description

This project integrates API testing automation with Machine Learning intelligence.
The ML component uses a Logistic Regression Classifier trained on a dataset stored in models/training_data.csv.
It enhances the testing process by predicting API outcomes and automating classification for consistent analysis.

---

📁 Folder Structure

API Testing Automation Tool (With ML Integration)/
│
├── .env                         -> Environment variables for the project
├── data/                        -> Placeholder for additional datasets (if required)
├── frontend/html5up-hyperspace  -> Frontend HTML, CSS, and JS files
├── main.py                      -> Main application launcher (runs backend + frontend)
├── models/                      -> Machine Learning-related files
│   ├── training_data.csv         -> Dataset used for training the ML model
│   └── train_model.py            -> Script to train and save the ML model
├── source_code/                  -> Backend source code
│   ├── api.py                    -> API endpoints for frontend-backend communication
│   ├── tester.py                 -> API testing automation logic
│   ├── utilis.py                 -> Utility functions
│   └── __init__.py               -> Package initialization file
├── tests/                        -> Automated tests for APIs
│   ├── test_api.py               -> Test script for API endpoints
│   ├── test_cases/               -> JSON files with test cases
│   └── __pycache__/              -> Cached Python files
├── venv/                         -> Python virtual environment
├── Requirements.txt              -> Python dependencies for the project
└── project vision.txt            -> Vision and description of the project

---

🤖 Machine Learning Details

• Algorithm: Logistic Regression Classifier
• Training Data: models/training_data.csv
• Training Script: models/train_model.py
• Retraining Requirement: Only when the dataset changes
• The trained model is automatically loaded during backend execution to avoid retraining on every launch.

---

⚙️ Usage Instructions

1. Install dependencies

   pip install -r Requirements.txt
   

2. Train the ML model (required only once or when the dataset changes)

   python models/train_model.py
   

3. Run the backend server

   python main.py
   

   If not running, you may encounter the error:
   ❌ "Error: NetworkError when attempting to fetch resource."
   This occurs when the frontend cannot connect to the backend service.

4. Access the frontend

   • Open the frontend/html5up-hyperspace folder locally,
     or
   • Visit the hosted backend URL to access the web interface.

5. Test APIs

   • Input API details in the frontend.
   • View real-time results and ML-assisted predictions for API responses.

---

📝 Notes

• Ensure training_data.csv contains representative examples for reliable predictions.
• Retrain the ML model only when updating or modifying the dataset.
• Populate the .env file with environment-specific configurations (e.g., API keys, database URLs, ports).

---

👤 Author

Harsh Menon
Developer | Machine Learning & Automation Enthusiast