CONC-Automation-Test-Tool 0.2

A Python project to establish a unified, automated testing framework for microservices.

CONC-Automation-Test-Tool

This project aims to establish a unified, automated testing framework for feature, nightly, and scale tests tailored to each microservice, eliminating node setup dependencies. It involves key microservice components like Kafka messaging, REST calls, cache, and database interactions, orchestrated to ensure thorough validation of microservice functionalities. The framework utilizes a Test Orchestrator to automate test inputs, execute workflows, and validate outputs by leveraging dynamically generated orchestration XLS files. Additionally, it supports concurrent testing of multiple microservices on virtual machines, enabling faster test execution and seamless integration of new features and regression tests.

Features

• Modular code structure in the src/ directory.
• Unit tests in the tests/ folder.
• Configurations separated in the config/ directory.
• Comprehensive documentation in docs/.

Setup Instructions

Prerequisites

1. Python 3.6+: Ensure you have Python 3.6 or higher installed on your machine.
2. PostgreSQL: Ensure you have PostgreSQL installed and running.

Step-by-Step Instructions

1. Clone the Repository:

   git clone <repository_url>
   cd conc_automation_test_tool
   

2. Create and Activate a Virtual Environment:

   python3 -m venv venv
   source venv/bin/activate  # On Windows: .\venv\Scripts\activate
   

3. Install Dependencies:

   pip install -r requirements.txt
   

4. Configure Environment Variables: Create a .env file in the root directory of the project. This file will store your environment variables, such as database connection details and secret keys.

   touch .env
   

   Add the following content to the .env file:

   DATABASE_URL=postgresql://myuser:prince123@localhost/test1
   SECRET_KEY=your_secret_key
   

   Replace myuser, prince123, localhost, and test1 with your actual PostgreSQL username, password, host, and database name, respectively. Replace your_secret_key with a secret key of your choice.

5. Set Up the Database:

   • Define Models: The database schema is defined in the models.py file located in src/apps/app1. Ensure that the models are correctly defined.

   • Create Tables: Run the create_tables.py script to create the tables in the database.

     export PYTHONPATH=$(pwd)/src
     python -m db.create_tables
     

6. Running the Flask Application: Start the Flask application by running the run.py script.

   python src/run.py
   

   The application will start, and you can access it at http://127.0.0.1:5000.

7. Accessing API Endpoints: The following API endpoints are available:

   GET Requests

   • GET /api/v1/getDataWorkflow: Fetch data from the WorkFlow table.

     curl http://127.0.0.1:5000/api/v1/getDataWorkflow
     

   • GET /api/v1/getDataSimulatedData: Fetch data from the SimulatedData table.

     curl http://127.0.0.1:5000/api/v1/getDataSimulatedData
     

   Note: The above get responses will be of json format.

   POST Requests

   • POST /api/v1/postDataWorkflow: Insert records into the WorkFlow table by uploading an XLSX file.

     curl -X POST -F "file=@workflow_data.xlsx" http://127.0.0.1:5000/api/v1/postDataWorkflow
     

   • POST /api/v1/postDataSimulatedData: Insert records into the SimulatedData table by uploading an XLSX file.

     curl -X POST -F "file=@simulated_data.xlsx" http://127.0.0.1:5000/api/v1/postDataSimulatedData
     

Flow Diagram

Below is a flow diagram illustrating the process of uploading an XLSX file, validating the data, and inserting it into the database.

graph TD;
    A[Upload XLSX File] --> B[Read XLSX File]
    B --> C[Validate Data]
    C -->|Valid| D[Insert into Database]
    C -->|Invalid| E[Return Error]
    D --> F[Success Response]
    E --> F[Error Response]