Most conventional performance testing tools donвЂ™t support end-to-end testing. For instance, if there are signs of a bottleneck, developers should diagnose and fix it before deployment. The results of a performance test should be used to address any problems. Stress tests use a high number of users to find the upper limits of an application, or its breaking point.

Load tests should use a realistic number of users that youвЂ™d see in production. For example, add time between someone reaching the product page and adding a product to their cart. Most website visitors donвЂ™t instantly click between workflow items, so tests should account for realistic вЂthink timesвЂ™.

Like a wish-list feature in an ecommerce application, which may not be used a lot but could have a high conversion rate. Even if a workflow doesnвЂ™t get high traffic, failures might be very costly. For instance, an ecommerce application performance test should cover adding a product to a cart and checking out.ДonвЂ™t forget high-risk workflows. There are several best practices to keep in mind when developing end-to-end performance tests to maximize their value. Running performance tests on individual API endpoints with static data hardly gives you enough insight to identify bottlenecks. In API-only applications, such as mobile app backends, end-to-end performance tests should similarly step through common workflows by making sequential API requests for a realistic measure. For example, you might map out the most popular user workflows and step through each part of the process with realistic and dynamic test data, measuring performance along the way. At the same time, SPAs typically make many asynchronous requests that are difficult to record and script when using non-browser-based performance testing frameworks.Д®nd-to-end performance tests involve recreating a complete user workflow in a browser to get the most accurate picture of performance. The performance impact of the browser is becoming especially significant with the rise of single-page applications, or SPAs, where browsers handle the majority of the heavy lifting. The problem with this approach is that it doesnвЂ™t capture the actual user experience вЂ“ HTML/CSS rendering time, JavaScript execution time, and other factors have a significant impact on performance. For example, Apache JMeter might send a GET request to a specific URL endpoint and measure response time. One should evaluate the service if they are considering JMeter and need to expand beyond existing hardware resources.Most performance testing occurs at the protocol level. I've not personally tried it though, but I recently attended a meetup presentation that highlighted nice useful features that BlazeMeter provides. There is a JMeter cloud service called BlazeMeter that I think would be useful for those that need to scale up high load without provisioning their own systems.Things like the Beanshell/BSF pre/post processors, response assertion, regular expression extractor, XPath extractor, CSV data set config Lots of testing/configuration options to suit your needs in making the right load generation (sampling requests), and analyzing the results, including any pre and post processing of the results first.

Automatic management of things like cookies to help with session state support - so you don't specifically have to worry about it or handle it.No coding is necessary at the basic level, just need to understand load testing and the infrastructure being tested. Easy of use - in generate load like HTTP requests, and processing/analyzing the responses.Integration Platform as a Service (iPaaS).

Apache jmeter vs testcomplete