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.

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.

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.

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!

In this blog post we are going to look at using JMeter to support business testing in production. This is a slightly different topic to the one discussed in this post on testing in production. The one in the link above is around running your performance testing in production for reasons that are discussed in that post. This post is going to focus on how you can leverage your performance testing tools to support this activity, as discussed above we will focus on JMeter in some of the examples.

In this post we are going to look at the importance of volume testing. We are going to consider how this type of non-functional test can have a significant impact on your size and scale your production environment based on evidence this test provides.

In this blog post we are going to look at how we can use our performance tests to act as Sanity Test. We have touched upon the subject in one of our blog posts on the hidden benefits or performance testing. This post will however look to provide more detail on the subject and provide guidance on how you can accomplish this. We are not suggesting that you write a set of JMeter tests to act as sanity tests for our application under test as that would not be that beneficial.

In this post we are going to look at performance test coverage. What functionality to performance test can range from very little to most of the application under test and both are valid under the right circumstances.

If you'd like to run load tests in a simple way, and possibly share them, while benefiting from a simplified configuration, with a focus on writing your test plan, and its test typology, this article is for you! Docker offers virtualization services that simplify the replication of working environments. Furthermore, each virtualized service is isolated from unrelated services on other containers or the host machine, ensuring portability across host machines and the network.

In this blog post we are going to look at how we take a postman request or collection and translate these into JMeter tests. When web services are being build it is common for Postman to be used to test the endpoints. This is done by: the list goes on. What naturally happens during programmes where web services are part of the design is that postman requests and collections are built and grow to support all manner of requirements.