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Volume Testing

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. We are going to look at some real-world examples of how getting your volume testing correct will ensure that your environments are not oversized or undersized. Both these scenarios have a cost which is both financial and potentially reputational.

Definition of volume testing

From Wikipedia.

Volume testing belongs to the group of non-functional tests, which are a group of tests often misunderstood and/or used interchangeably. Volume testing refers to testing a software application with a certain amount of data to assert the system performance with a certain amount of data in the database. Volume testing is regarded by some as a type of capacity testing, and is often deemed necessary as other types of tests normally don't use large amounts of data, but rather typically use small amounts of data.

We use this test to understand the impact of large data volumes on your application under test. Larger data volumes in the database can:

  • Increase the query time of your SQL statement which impact response times,
  • Impact the amount of CPU or memory your database uses and therefore you can use this test to size this component,
  • Impact the size of responses to end user and API requests, with search requests being an example, this impacts application CPU and Memory.

A volume test looks to not only understand performance of the SQL with an indicative sized database. But also looks to understand how much CPU and Memory is required across you full stack to support peak volumes of load and concurrency. The results you gather from this type of non-functional testing is important in determining how you application needs to be sized in production to support future data volumes.

Defining and Maintaining Performance Test Coverage

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. We have talked about what to performance test in other posts available in the OctoPerf Blog but as part of a wider post about performance testing rather than as the subject of the post. This is an important topic and deserves a post devoted to it.

We are going to discuss the performance coverage topic through a series of questions which we will explore in detail. These questions are:

Playwright vs JMeter

In our quest to provide a comprehensive suite of tools for our load tester community, we, at OctoPerf, developed a full integration with Playwright. Playwright is an end-to-end testing library that has the ability to automate real browser actions. Which means that you will be able to do exactly that on OctoPerf, on top of all the advanced performance monitoring and report features that it already has.

In this article we will start with introducing browser based testing to better understand the advantages it can provide to a load tester. We will then compare a load testing campaign made with browser based and one with protocol based Virtual Users (VUs) to fully understand the differences.

Tracing Slow Performance

You have built your performance test and executed the tests under load and your tests do not meet your requirements in terms of response times.

Or you are unable to execute your tests with the number of concurrent users required.

In this post we will give some insights into where you might want to start looking for the root cause of your performance issues.

These insights are very high-level, and the architecture of your application will determine which ones are of use and which are not as will the language your application is written in and the database technology it uses.

Performance Testing and ChatGPT

Performance testing applications requires a set of skill that are build and gathered over many years of studying and using the various techniques and tools that are required to make sure the application you are testing is fit for production. Now we have all heard of Artificial Intelligence (AI) and the many tools and companies that now exist in the AI space.

Based on a quick look on the internet there are around 15,000 AI startups in the United States alone. So surely with all that technology at our disposal we should be able to use these AI tools to define our performance tests meaning that anyone can determine what performance testing should take place regardless of experience and training.

To build, execute and analyse these tests still requires a competent performance tester but the definition of what should be done could be handed over to Artificial Intelligence right? Let’s find out shall we, we will use ChatGPT as this commonly available and probably the one most have heard of.