Neoload performance testing manual#
Neoload performance testing software#

Often these goals appear to be precisely defined metrics - “the system must be able to handle 500 transactions per second” - but where do these benchmarks come from? What if users are performing other transactions at the same time? Can the system still handle 500 transactions per second? Empirical load requirements do not guarantee that the system will be able to handle the load in production, as they do not take into account other factors or how users actually use the system. Tests are built based on categorically quantified requirements set forth in SLAs.

Empirical load or stress test requirements.

While this significantly reduces the time required to write the performance test script - and Tosca even uses AI-powered smart impact analysis (LiveCompare) to prioritize what to test functionally - we're still left with the question of what constitutes a representative (real-world) load in production. For example, turning Tricentis Tosca functional tests into NeoLoad performance tests can be accomplished with just one click. Repurposing functional tests as performance tests has become simple and straightforward.

Build scenarios on top of converted functional test plans.

Enterprises define these scenarios in a variety of ways, from relying on their performance experts' experience and tribal knowledge to leveraging insights from the application's business experts to a more methodical but labor-intensive approach that involves collecting data from various sources (logs, JavaScript trackers such as Google Analytics, and APM tools) and manually analyzing it for common user paths.

Usage scenarios that re-create what are thought to be typical user paths.

Today, most performance tests are built from: We interviewed businesses of various sizes and industries with varying degrees of performance engineering sophistication and discovered that a systematic representativeness assessment between performance tests and the load mix/usage in production is performed infrequently to never. Tricentis NeoLoad has developed a prototype solution, codenamed RACHAEL, that we are validating with various enterprises that it can leverage AI to assess production traffic as a data source detect different populations and their load curves, pacing, and think times isolate signal from noise extract variables, dynamic parameters, and pre-identified user paths and more. This could be accomplished manually by collecting and digesting the various available datasets, but AI/ML could automate the analysis of production traffic in order to scope scenario elements that will be used in testing. In order to provide testers with representative synthetic performance scenario elements as fast as possible, a solution needs to synthesize/mimic as closely as possible the behaviors of real users on production systems.

Neoload performance testing manual#

Due to the fact that it is a highly manual and time-consuming endeavor, the majority of performance tests do not accurately reflect real-world conditions. It continues to follow the legacy process established by the market's pioneers. While modern solutions do leverage automation to accelerate many aspects of performance and load testing, the approach to answering the fundamental question “What is a representative test that reflects real-world behavior and traffic?” has not changed in the past 25 years.

Neoload performance testing software#

And, of course, analyzing results and working with software engineering teams to resolve identified issues.Running tests, either manually or programmatically.Actually building robust and maintainable test scripts.Coordinating with business experts and gathering data to build the strategy that tests the right things, under the right load.Performance engineering (of which performance testing is integral) has traditionally been a practice that requires numerous manual steps and long-to-acquire knowledge involving a steep learning curve:

Nevertheless, far too frequently, validating performance via realistic testing has become a bottleneck. Application performance has become a critical differentiator in the competitive landscape. And performance has become critical: users and customers expect a seamless digital experience on any device, at any time, and from any location. Application environments are more complex than ever, with SaaS, microservices, legacy monolithic applications, and packaged enterprise applications such as SAP all highly connected and coexisting. Cycle times for software development and delivery are becoming increasingly shorter and more frequent.