Pressure model analysis method based on TPS in software performance test

Abstract

The invention provides a method for pressure analysis during an application software performance test. The method comprises the following steps: production data of a typical on-line service application system is analyzed based on pressure model analysis of TPS during the software performance test so as to obtain the TPS of a key service, when the number of system users increases, the access pressure of the system also increases, the pressure of the system is proportional to a TPS value, the analyzed TPS is multiplied by a pressure multiplication factor which possibly increases to obtain a pressure design index to be simulated in the performance test so that the pressure of the system is really simulated, and the performance of the system at the pressure is tested. When the number of the system users increases, the access pressure of the system also increases, the pressure of the system is proportional to the TPS value, the TPS value can be estimated after the number of the users increases, and the TPS value is taken as a target for generating pressure during the performance test, which can really simulate the pressure on the system from a great number of user operations, accurately test the handling capacity of the system, and help make a decision to expand the application system or not.

Description

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AI Technical Summary

Benefits of technology

It describes how it helps measure the efficiency at handling high-volume requests by analyzing their numbers during peak hours or when there're increasing demand from customers. By doing that, researchers may better determine if they want another product or service afterwards without having them shut down due to overwhelming workload levels.

Problems solved by technology

This patented technical problem addressed in this patents relates to improving the speed at which data processing systems expand their capabilities for handling increasingly complex tasks such as managing information over multiple networks simultaneously without sacrificing efficiency due to excessive resource usage during simulation-based operation tests that are currently used only on small scales.