Is JMeter old technology or is it still suitable for modern day challenges?
Introduction “JMeter is old technology”, I hear this a lot.
“Let’s use this tool or that tool instead of JMeter as it’s the latest”, I hear this a lot.
“We need a lightweight tool without the GUI interface to write our tests as that will make us more agile”, I hear this a lot.
It’s all nonsense it really is, there seems to always be a call for using the most modern technology for all forms of testing whether its Performance Testing or Automated Functional Testing and the result of this is that the people writing the tests spend to long learning the new tools and not enough time building something that will ensure the software being developed is the best it can be.
Overview of JMeter plugins and how they can be used
Introduction JMeter in its vanilla form is a very powerful tool for performance testing, the ability to create complicated test scenarios using the out-of-the box samplers, timers and logic controller can sometimes be extremely difficult and require you to write your own complimentary code.
There are however Plugin’s that exist that can help in building some of the more challenging scenarios.
In this post we are going to look at some of these and discuss how they can be used.
How to send an HTTP POST request using Gatling and how to create modular scripts to reuse chunks of load testing virtual users?
This article is the fourth part of a series of tutorials dedicated to Gatling Load Testing.
Kraken is used to ease the debugging of Gatling simulations and to speed up the process of load testing a fake e-commerce website: PetStore.
We will focuse on POST requests and script modularization:
In the previous blog post we created a realistic Virtual User that browses the store without buying anything. On the contrary, here we are going to simulate the behavior of a user that connects to the web store, searches for items, adds some to his cart and proceeds to the checkout.
Overview of statistical techniques for results gennerated using JMeter
Introduction What does statistical analysis have to do with performance testing you may ask, more than you would think is the answer.
Due to the large volumes of result data that is generated and analysed you are performing statistical analysis of your data when presenting your results.
90th Line, 95th Line, 99th Line, Average, Median. Are all examples of values in the JMeter Aggregate Report.
Average, Std.Dev. Are examples of values in the JMeter Summary Report as well as the Graph Results.
How to replace Angular Material CdkTree (FlatTreeControl) by a CdkVirtualScroll loop? This blog post is a complete step by step guide.
While developing Kraken’s frontend I quickly stumbled upon performance issues with Angular Material tree when too many nodes where opened. Kraken is an open source load testing IDE. As such, it displays a tree of directories and files used to script the load testing scenarios:
You can have a look by creating a free account on the demo or check the source code of the UI on GitHub. It uses the latest version of Angular and components provided by Angular Material such as the tree.
Kraken 3.0: a new step towards an Enterprise grade solution. Users management with KeyCloak, Online Demo SaaS, Administration improvements.
This third version of Kraken represents one more step towards a load testing solution suitable to teams and enterprises. Kraken can already be installed on your own Kubernetes cluster thanks to Helm charts: You own all data and can handle the security inhouse.
But until now it was lacking users management, making it cumbersome to use it for a team of performance testers. This point is now addressed in the version 3.
Using Test Fragments, Module Controllers and Include Controllers in JMeter
Introduction The key to all good automation is modularisation which is effectively isolating small chunks of your application under test into separate tests and then re-using these smaller chunks in your larger, more complicated tests.
Example of good candidates for modularisation are logon and logoff as well as any other high-volume journeys through your application such as search functionality.
The rationale for this approach is that if these high volume common functional activities change you only need to make the update in a single test rather than in multiple tests.
An in-depth look at OctoPerf report engine and all the report items that can make your analysis easier. We’ll even check the report comparison and throw in a few tips for good measure.
Introduction OctoPerf’s report engine provides many graphs to sort and presents test metrics in a comprehensive way. We’ve tried to improve it over the years so that you can access critical information very quickly. But requirements vary from one project to the other.
In this post we will look at how you can configure the report to show your preferred metrics, and also all the shortcuts you can take to achieve this goal.
Introduction You may have spent a considerable amount of time configuring Postman requests for your in-house API tests, and you wish to use them without having to create them again from scratch on Octoperf.
That’s one of the many situations where Octoperf’s compatibility with Jmeter is going to come in a handy.
Postman to Jmeter The first step will be to convert your requests into a JMeter-friendly format, using Postman Code Generation Snippet :
Introduction When we think of performance testing we normally think of thousands of requests with thousands of users generating huge volumes on our application under test or increasing the load until the application under test fails or runs out of resources.
This is not a good approach when determining load for your performance tests for several reasons and can in some cases render your performance testing meaningless.
This post will outline some of the pitfalls that are commonly made when it comes to generating load and will look at ways to suggest improvements to your approach to load profiles.