All About Load Testing Software for Application Performance

In order to make sure applications are running smoothly, it is important to implement performance testing. There are six categories of performance testing - load, stress, soak, spike, configuration, and isolation. Load testing is most often used to test performance because it is the simplest way to understand the behaviour of the system under an expected load.

When load testing, software is putting demand on a system and measuring the response. It is used to determine behaviour of an application under normal and anticipated peak load conditions. This testing helps identify the maximum operating capacity of an application, along with any bottlenecks and elements that are causing degradation.

Load testing can be used in multiple ways by the professional software testing community. Most often, software testers use it to model the expected usage of a software program by stimulating multiple users accessing the application at the same time. This makes load testing applicable for systems that utilize multi-users. Most often, the testing is used for a client/server model like web servers.

Other types of software systems can be load tested. Word processors or graphics editors can be forced to read an extremely large document. A financial package me be forced to generate a report based on several years' worth of data. Accurate load testing stimulates actual use. Other testing forms may only use theoretical or analytical modelling.

Load testing measures the website's QOS performance bas on actual user behaviour. All load testing tools and frame works follow a classical paradigm. When users get on the application, a script recorder records the communication and then creates related interaction scripts. Load generators replay the recorded scripts.

The recorded scripts can be modified with different test parameters before replay occurs. When in replay, the hardware and software statistics are monitored and collected by a conductor. Statistics can include CPU, memory, disk IO of physical servers and their response time, and the throughput of the System Under Test (SUT). Statistics are analyzed and a report is generated.

Load and performance testing analyzes software intended for a multi-user audience by subjecting the software to different numbers of virtual and live users while monitoring performance measurements under different loads. These tests are conducted in a test environment identical to the production environment before the software system goes live.

The testing environment should be isolated from other environments in order for results to be consistent. There needs to be a separate performance testing environment resembling the production environment as much as possible. It is crucial, and sometimes difficult, for the test conditions to be similar to the expected use. Sometimes this is not possible in actual practice.

Workload of production systems have a random nature and test workloads do their best to mimic what may happen in the production environment. However, it is impossible to exactly replicate the workload variability unless the system is extremely simple.

Certain organizations utilizing performance testing may have more difficulties than others. Loosely-coupled architectural implementations have created additional complexities with performance testing.

Enterprise services and assets that share a common infrastructure platform need coordinated performance testing. All consumers need to create production-like transaction volumes and load on shared infrastructures. This is the only way to truly replicate production-like states.

The complexity and financial and time requirements for proper testing mean that some organisations employ tools that can monitor and create production-like conditions in their performance testing environments. This is known as noise and is used to understand capacity and resource requirements to verify and validate quality attributes.

Load testing needs to begin at the inception of the development project and extend through to deployment. When performance defects are detected later, there will be very high costs in order to remedy the problem.