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Parasoft SOAtest’s load and performance testing module, Parasoft Load Test, enables users to take SOAtest functional test projects and leverage them for performance testing with ease. With an intuitive user interface that makes common configurations easy, a scripting extension makes extending custom functionality a breeze. The Load Test Continuum (LTC) web-based reporting app makes it easy to make performance testing a part of the CI/CD process to efficiently implement performance test automation.
Take an existing SOAtest project that you built for API and/or web testing, and with a few clicks, you can construct and run a performance test based on it, enabling a true shift-left testing strategy by facilitating early stage testing performance testing as soon as the interfaces available. You can then create rich multi-profile performance test scenarios to further scale your performance testing solution across multiple machines. Parasoft makes this process easy by automatically transferring the Load Test project, the SOAtest project, and its dependencies (for example external data sources) to remote machines.
Once a performance test run is complete, you can examine its results in custom reports that you can configure to record whatever level of detail suits your needs, from high level filterable statistics tables to individual ‘hit’ details with their request and response traffic.
A range of built-in monitors are available for diagnosing performance problems, including integrations with major application performance management systems, such as AppDynamics and Dynatrace.
Parasoft SOAtest goes beyond the basic baseline/smoke test and normal level of performance test. For example, users benefit from the following types of load and performance tests:
Import JUnit tests into Load Test to run performance tests at the code level. With this ability, Parasoft enables teams to achieve earlier-stage load testing that isolates specific parts of the codebase to performance test the internals of the application.
A wide selection of built-in as well as custom-scripted Quality of Service (QoS) metrics enable users to reduce the wealth of data collected during a load test run to a set of pass/fail indicators to help automate the analysis of the load test results. Built in and scripted monitors that include integration with major Application Performance Management (APM) systems allow the user to correlate key performance test graphs to custom AUT and system parameters.
Using Parasoft SOAtest’s Load Test command line script interface together with the Parasoft Load Test Continuum Web application enables users to collect and optimize performance test data from multiple runs. Parasoft provides these tools to accelerate performance testing and provide efficiency for achieving performance test automation through load test batch execution and historical results analysis.
Run globally-distributed performance tests on-demand on the cloud using the publicly available Load Test Agent for AWS. Users can verify the performance of their application when, for example, the front end is hosted in the US and a backend service is running from a datacenter in Singapore, all from their SOAtest desktop. Scalable distributed load testing with automatic transfer of SOAtest projects and SOAtest project dependencies to remote machines.
Instead of having to create different scenarios for functional tests and load tests, with Parasoft you can leverage the same scenarios for both functional and load tests, reducing the time it takes to create and maintain test scenarios.
Visually diagram user profiles to match the realistic usage patterns of user stories, and apply specific load to those profiles to understand how specific user experiences will be impacted during the application’s times of heavy usage.
Parasoft Load Test can be fully automated as part of your CI/CD pipeline. Its web-based reporting helps users understand the incremental impact of multiple performance tests by displaying trending information by project, component, API, etc. Users can also identify performance issues earlier by viewing historical trends that link performance tests to their initial requirements, to properly prioritize issues that arise.