Editing Software performance testing (section)

== Technology ==
Performance testing technology employs one or more PCs or Unix servers to act as injectors, each emulating the presence of numbers of users and each running an automated sequence of interactions (recorded as a script, or as a series of scripts to emulate different types of user interaction) with the host whose performance is being tested. Usually, a separate PC acts as a test conductor, coordinating and gathering metrics from each of the injectors and collating performance data for reporting purposes. The usual sequence is to ramp up the load: to start with a few virtual users and increase the number over time to a predetermined maximum. The test result shows how the performance varies with the load, given as number of users vs. response time. Various tools are available to perform such tests. Tools in this category usually execute a suite of tests which emulate real users against the system. Sometimes the results can reveal oddities, e.g., that while the average response time might be acceptable, there are outliers of a few key transactions that take considerably longer to complete – something that might be caused by inefficient database queries, pictures, etc.

Performance testing can be combined with [[Stress testing (software)|stress testing]], in order to see what happens when an acceptable load is exceeded. Does the system crash? How long does it take to recover if a large load is reduced? Does its failure cause collateral damage?

[[Analytical Performance Modeling]] is a method to model the behavior of a system in a spreadsheet. The model is fed with measurements of transaction resource demands ([[CPU]], disk I/O, [[LAN]], [[Wide area network|WAN]]), weighted by the transaction-mix (business transactions per hour). The weighted transaction resource demands are added up to obtain the hourly resource demands and divided by the hourly resource capacity to obtain the resource loads. Using the response time formula (R=S/(1-U), R=response time, S=service time, U=load), response times can be calculated and calibrated with the results of the performance tests. Analytical performance modeling allows evaluation of design options and system sizing based on actual or anticipated business use. It is therefore much faster and cheaper than performance testing, though it requires thorough understanding of the hardware platforms.