Raj Dutt, Grafana Labs CEO, said load testing will complement the observability platform Grafana Labs currently provides based on open source Prometheus software. Most of the output of any load testing processes are typically evaluated using a platform that analyzes metrics, he noted.
The k6 platform has been gaining traction among developers that require access to a tool that makes it simpler to dynamically scale load testing up and down as required versus having to guess how much infrastructure might be required to test an application workload, said Dutt. That capability not only makes it simpler to integrate testing within a DevOps workflow, Dutt added, but also serves to reduce testing costs.
In general, Dutt noted testing is shifting further left as developers assume more responsibility for quality assurance. The challenge is finding a way to enable developers to automatically incorporate testing within a continuous integration (CI) process that allows them to easily observe how synthetic tests are executing, added Dutt.
There’s no doubt developers are running more tests before promoting workloads into a production environment as part of an effort to reduce the number of issues that might be encountered when an application is deployed in a production environment. In many cases, the support calls that arise now go directly to developers who now have a vested interest in reducing the volume of those calls.
The issue that developers are contending with is as they embrace microservices, the dependencies that exist across multiple applications makes it more challenging to successfully deploy those applications. Grafana Labs has been making a case for a cloud platform that makes it simpler to centrally view data collected from instances of Prometheus distributed across multiple application development environments. Prometheus, now being advanced under the auspices of the Cloud Native Computing Foundation (CNCF) that oversees the development of Kubernetes, is now being deployed more widely by IT teams to observe legacy monolithic applications alongside emerging microservices-based applications.
It’s not clear the degree to which organizations are also employing dedicated application testers. In many cases, those testers will surface issues that developers overlooked (or simply chose to ignore) in the face of tight application delivery deadlines.
In the longer term, however, it’s also apparent artificial intelligence (AI) will be playing a larger role in application testing. Someone will still need to review those tests, but many more rote tasks will increasingly be automated.
One way or another, the total cost of testing is declining, thanks in part to increased reliance on open source testing tools that, in many cases, developers are simply downloading themselves. Those tools are reducing much of the licensing friction and cost restrictions that developers might otherwise encounter.
In fact, the easier it becomes to test applications, the better overall application quality will become in the months and years ahead. At at time when organizations have never been more dependent on software, that potential improvement to the overall state of application quality can’t arrive soon enough.
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