We are going to look at how performance testing can work hand in hand with Artificial Intelligence and Machine Learning: As there are many Artificial Intelligence solutions to choose from and for the purposes of this post this is the easiest way to discuss the principles of Artificial Intelligence working with performance testing rather than discussing a particular framework.
The first month of 2023 has already ended and we are excited to share the new updates and features that we implemented in Loadero over the past month and some of the fixes we made during that time. If you are using Loadero to test WebRTC solutions, make sure to read the section about updates for WebRTC metrics gathering, it also has information about important upcoming changes.
The last thing a business wants is to be known as an unreliable and poorly performing service, especially if there are similar solutions a few clicks away. So being aware of the performance of a WebRTC application or any other software solution is a must to avoid issues in the future. A solution can be developed by experienced people and tested before it is released, but even so it doesn’t mean performance degradations will never appear.
Loadero supports three feature-rich test frameworks: Javascript + Nightwatch Java + TestUI, Python + Py-TestUI, but to expand the functionality further, we also develop custom commands for more insightful performance and load testing. One of the recent additions to the custom commands to use in your Loadero test scripts is the timeExecution() command.
Here are some easy steps to follow in order to sucessfully do a load testing session for your newly created website.
The best time to load test your website is when you are in the development phase. This will allow you to identify potential issues before the application is released to the public.
Recent months were very busy for our team and some exciting new releases are coming soon. Meanwhile, in this post, we’re sharing updates to Loadero that took place in November 2022. There are some updates to our API, to the way Loadero works with test run artifacts in the results reports, new browser versions, and the Loadero Python client that we were developing for quite a long time and finally are happy to offer to our users along with examples of using it for storing test data in Github.
Loadero API allows managing and running tests just as one would via the Loadero web application. However, sending an HTTP request every time a test needs to be created, updated, or run can become cumbersome. We already had a Java client, which makes the processes easier, and now Loadero team has been hard at work creating a new client and we are proud to present – Loadero-Python.
Load testing is one of the most common ways to test the resiliency of your applications. In this blog we show how recording production data with Speedscale and exporting to a K6 load tests gives you the best of both worlds. Whether or not it's important for your organization, there are clear benefits to be had from implementing these types of tests. By doing so, you can: When it comes to load testing, two of the most modern tools are Speedscale and K6. While there are many reasons for choosing one over the other, there are also benefits to using them together. If you want to know what the main differences are, check out the in-depth comparison.
