Skip to content

2024

Cooperative U - Case study

Cooperative U (formerly known as Système U), is a huge French retailers cooperative, made up of hundreds of independent hypermarkets and supermarkets, which makes it the one of the largest retail group in France. It's entire IT development and management is handled by U Tech (ex U IRIS), making performance testing one of its many responsibilities, in collaboration with SIGMA, a digital solutions provider they work closely with.

In 2022, while a Neoload customer for multiple years, U Tech decided to rethink it's performance strategy and looked for ways to improve its efficiency.

That's during this process that discussions with Octoperf started which ultimately lead Cooperative U's decision to make the switch from Neoload to Octoperf.

This article will explain why Cooperative U made this decision, how they implemented that change with SIGMA's help and what they are planning to do next to integrate Octoperf even more in their IT environment.

  • in JMeter
  • 10 min read

Generating Quality Data

The problem with test data is that it can become stale very quickly. This is either through its use from testing or from the fact that it is naturally aging in the test environments.

This is not just an issue for performance testing, although the volumes of data sometimes required for performance testing do make it harder. This also affects functional testing as well as batch testing and business acceptance testing amongst others.

Now we have previously written posts on how after completion of performance testing you leave data created by the execution of your tests which may be of use to other members of your test or development community. And we have also discussed how you can use existing data in your performance testing environment in your tests in the most effective manner.

But in both cases, this is during or after your performance testing takes place, for performance test to be executed you need quality data in your test environments. This is also true for functional, batch and user acceptance testing, really its true for any type of activity that wants to use data in the test environments.

  • in JMeter
  • 12 min read

Performance Test Results Trend Analysis

In this post we are going to look at how you can spot trends in your performance test results and use this trend analysis to help shape the way you address performance testing. Performance testing can generate a large volume of performance test data and using this data to define your future performance testing coverage, scope and volumes can really add benefit to your non-functional testing process.

To use data to support performance testing you are going to need to store it in a way that makes it accessible and comparable. The ideal solution and one we will discuss in this post is to store this data in a database. This post will look at the positive benefits of storing lots of performance data, especially data that spans a significant period of time, and we will look at ways of using this data to define performance test coverage and identify areas of performance concern.

OctoPerf v15.1 - Continuous integration and new trend report

Version 15 of OctoPerf bring a huge focus on continuous integration and Devops practices. We've taken steps to highlight existing integrations, add many others and we've thrown an entirely new report type on top of it.

Continuous integration improvements

It's always been possible to automate OctoPerf tests using our existing plugins or simply through our API. That said the setup of those integrations comes with a learning curve. In order to make this curve as flat as possible we've added a new wizard and the possibility to get a PDF report automatically at the end of the test.

JMeter ecosystem for your Performance test through docker-compose, monitor, and mock your services

If you'd like to carry out load tests in a simple way, benefit from a simplified configuration with a focus on writing your test plan and its test typology, have the opportunity to monitor through detailed dashboards, store your metrics, and also mock one or several services: you are at the right place!

With this kind of configuration you will be able to make shift-left performance testing as well!

docker-compose is a small library that allows you to run docker-compose This is useful to bootstrap test environments. Docker Compose offers a multitude of benefits which I'll detail below:

  • Simplified configuration: Docker Compose lets you define and manage all the services of a multi-container application in a single YAML file. This makes it easy to configure, start and stop all the containers in an application.
  • Automated deployment: With a single configuration file, you can automate the deployment of all the services required for your application, reducing manual errors and improving consistency between development, test and production environments.
  • Managing dependencies : Compose makes it easy to manage dependencies between services. You can define the startup order of containers and the links between them, ensuring that all services start up in the right order and are properly connected.
  • Portability: Once you've defined your Compose file, you can easily share it and run it on different machines. This ensures that developers and operational teams work in identical environments, reducing compatibility problems.
  • Service isolation: Docker Compose creates isolated networks for containers, ensuring that each service operates in a partitioned environment. This improves security and enables services to be tested without mutual interference.
  • Scalability: Compose makes it easy to scale services. You can quickly adjust the number of containers for a particular service by simply modifying the configuration file and redeploying.
  • Local development and easy testing: Developers can use Docker Compose to create local development environments that faithfully reproduce production environments. This enables problems to be detected and resolved early in the development cycle.
  • CI/CD integration: Docker Compose integrates well with continuous integration and deployment (CI/CD) pipelines. You can use Compose files to orchestrate automatic tests and deployments in your CI/CD workflows.
  • Simplified maintenance: With Docker Compose, updating configurations and services becomes simpler. You can update container images or modify configurations by modifying the Compose file and redeploying services.