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Blog

AI in Performance Engineering: Revolutionizing Load Testing with WebLOAD.AI

  • 3:29 am
  • 16 Jan 2024
Capacity Testing
SLA
Definition
Load Testing
Performance Metrics
Response Time
User Experience

With over 5 billion people using the internet for various purposes, there is no wonder why performance engineering has been an utmost importance for many companies nowadays. This systematic approach is critical to software development, allowing companies to build websites that meet non-functional requirements, including scalability, security, availability, and reliability. Performance engineering involves various concepts that work together to ensure the website’s optimal performance, including load testing.

Load testing has been a crucial part of performance engineering for years, helping developers determine how websites and applications behave under specific loads. And by integrating various AI tools, load testers can have a powerful solution that can help them get a deeper understanding of test results and efficiently identify issues with their software.

With load testing becoming more crucial nowadays, we at Radview Software have developed a new version of our flagship product which begins the inevitable rise of AI-powered performance testing tools. WebLOAD.AI brings the power of its existing dashboards, panels, and functions. But what makes this version special is the new “Explainer” feature, which gives load testers an in-depth explanation of the graphs that discusses how the website or application performs based on various metrics.

The New Explainer Dashboard

Load testing has long been a complex process, requiring performance engineers to take much time conducting the tests and interpreting data. This is especially true for those who still need more experience in load testing, making them search the internet for possible explanations, miss conclusions, and misinterpret the results of the tests.

This is where the AI-based Explainer feature will come in handy when analyzing load test results. With just a simple click in the dashboard dropdown menu, this new tool will ask for explanations and insights about the graph on one of the most popular AI tools nowadays, ChatGPT. The Explainer tool will send over the charts to ChatGPT, and the latter will provide valuable insights into what the data imply.

What Can the AI-Based Explainer Dashboard Provide?

● Graph and Data Analysis –One of the best things about the new Explainer feature is that it can interpret graphs and test results by providing static data and AI explanations. A few seconds after the Explainer panel sends the data to ChatGPT, load testers will be given an analysis of what the graph implies, helping them understand how the system performs in minimal and peak traffic.

● Customizable Metrics – The Explainer panel can interpret all graphs and data from various parameters after load tests. However, not all charts need explanation all the time. So, the new AI-based Explainer can be customized only to send the selected metrics to ChatGPT, allowing for a faster response from the AI tool.

How Can It Fix Common Issues in Load Testing

The integration of ChatGPT to WebLOAD will significantly impact how load testers work moving forward. For starters, the Explainer dashboard can greatly simplify test results, allowing even those only starting in load testing to understand what the results signify fully. This can substantially reduce the complexity of load tests, allowing even non-experts to conduct and interpret the data.

In addition, having an Explainer tool at hand can decrease the possibility of human errors when defining the graphs, which can cost a lot of time and money in the long run. But with the Explainer panel, every parameter, including anomalies, trends, and correlations, can be thoroughly interpreted. This can help developers and performance engineers make informed decisions when fixing issues with their software.

This, along with its capability to provide the necessary explanations and information in seconds, makes WebLOAD.AI a cutting-edge innovation in the load-testing world. Developers and engineers can move forward from legacy load testing practices and embrace a faster and more efficient performance testing process. Doing so will enable them to accelerate the development of websites and applications that can make them stay ahead of the competition.

Looking Ahead

The development of WebLOAD.AI will play a significant role in the transformation of load testing practices in years to come. It is an innovative solution that addresses the common issues businesses face during load testing, such as complex test results and time management. With ChatGPT on board through the Explainer panel, load testers can use a tool to get valuable insights about graphs and data. This can help them quickly identify bottlenecks in their system and make informed decisions when fixing them.

And with AI expected to improve further as time passes, we at Radview ensure that the newest version of WebLOAD is only the beginning. We aim to make WebLOAD.AI a leading performance testing tool with various AI-powered features that can make it more reliable for modern businesses.

So, if you want to try out the latest WebLOAD version for your website or application, visit www.radview.com/schedule-demo today.

Additional Resources for Further Reading:

The Future of Load Testing: How AI is Changing the Game

Related Posts

CBC Gets Ready For Big Events With WebLOAD

FIU Switches to WebLOAD, Leaving LoadRunner Behind for Superior Performance Testing

Georgia Tech Adopts RadView WebLOAD for Year-Round ERP and Portal Uptime



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