However, the flexibility of exploratory testing comes with the risk of ad hoc, random outcomes with no structure or consistency between testers, test managers, and projects. While testers should not be limited in their exploratory testing efforts, having a disciplined approach is a fantastic tool for gathering feedback, detecting errors, and enhancing UX.

In this Blog Post we are going to look at the GraphQL HTTP Request sampler and look at how GraphQL requests can also be made using a HTTP Request sampler in case you are for some reason restricted to an earlier version of JMeter (the sampler was only introduced in JMeter 5.4). We will also look at some of the principles of GraphQL.

Building performance tests that conform to a very specific level of load and concurrency is a standard approach to performance testing. You determine your peak levels of load and concurrency, and you build a test that meets this. You build soak test and scalability tests that conform to pre-determined levels of load and concurrency, and you execute these alongside the other scenarios you build to meet your performance requirements.

Pressure to rigorously test software under strict launch times leaves over 54% of software teams struggling to meet demands for new and updated products. But even as the software development lifecycle shifts to incorporate testing, it’s hard to iterate quickly. Developers need a standard two-week cycle to ship code over the wall for QA teams to run with, but how long do QA teams need to release a final report?

Automated testing early in the pipeline for both web and mobile applications has become the standard for most development teams. Catching issues when they are faster, easier, and cheaper to fix provides tremendous value to a business both financially and through the improved quality of end-user experience.