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Performance testing Load Modelling - Calculate no of threads , Ramp up time , Step up load test plan , Analyze Performance degradation and generate report , AI for Predictive Analysis

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performancetestadvisor

Performance Load Modeling Tool

Overview

The Performance Load Modeling Tool aids testers and developers in planning and executing performance tests. By inputting the expected daily user traffic, this tool calculates the recommended Virtual User (VU) ramp-up strategy, simulating real-world usage patterns. The tool also provides recommendations and suggested open-source tools for conducting performance tests.

image

Features

  • User Input: Allows users to input the total number of expected users per day.
  • Calculation: Computes the number of Virtual Users (VUs) to step up every 1, 10, 30, and 60 seconds.
  • Recommendations: Provides guidelines on maintaining the peak load and monitoring application performance.
  • Suggested Tools: Recommends open-source performance testing tools like Apache JMeter and Locust.

How to Use

  1. Input the total number of users expected per day.
  2. Click on "Calculate".
  3. View the ramp-up strategy in the "Formulas" section.
  4. Review recommendations and suggested tools for a comprehensive performance test.

Predictive Analysis Tool (AI-powered)

Overview

The Predictive Analysis Tool uses AI to generate predictions based on historical user data. By uploading historical data in CSV format, the tool can simulate and visualize predicted user growth for the next 90 days.

Features

  • User Input: Allows users to upload a CSV file containing historical data with columns named 'date' and 'users'.
  • Visualization: Displays a line chart showing historical data along with predicted user growth.

How to Use

  1. Navigate to the Predictive Analysis page.
  2. Upload your CSV file containing historical user data.
  3. Click on "Generate Predictions".
  4. View the line chart showing historical and predicted user growth.

Notes

  • Ensure your CSV file has columns named 'date' (in 'YYYY-MM-DD' format) and 'users' (integer values).
  • The prediction is based on a mock linear regression model for demonstration purposes.

Hosted Version

https://ilamvazhuthi.github.io/performancetestadvisor/

Static Website Docker Container

This repository provides a Dockerized setup for a static website using Nginx.

Prerequisites

  • Ensure you have Docker installed on your machine.

Usage

  1. Navigate to the directory containing your Dockerfile, index.html, styles.css, and scripts.js.
  2. Build the Docker image using the following command:
    docker build -t my-static-website .
  3. Once built, you can run the container with:
    docker run -d -p 80:80 my-static-website
  4. You should then be able to access the website at http://localhost.

Pushing to Docker Registry

If you want to push the Docker image to a Docker registry (like Docker Hub), ensure you tag the image appropriately and then use the docker push command.

Future Improvements

  • Email subscription feature for news and updates.

Credits

Developed by Ilamvazhuthi Mathivanan. Check the source code on GitHub. Follow on Twitter.


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Performance testing Load Modelling - Calculate no of threads , Ramp up time , Step up load test plan , Analyze Performance degradation and generate report , AI for Predictive Analysis

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