Software Performance Testing Scheme Using Virtualization Technology

Author(s):  
Gwang-hun Kim ◽  
Hui-choun Moon ◽  
Gi-Pyeung Song ◽  
Seok-Kyu Shin
2014 ◽  
Vol 490-491 ◽  
pp. 1553-1559
Author(s):  
Jun Luo ◽  
Wei Yang

With the rapid development of the information age, computer software develops toward systematization and complication. In application areas such as commerce, finance and medical treatment,the performance of software is attracting more and more attention which even becomes one of the important factors to determine whether users are willing to use a piece of software. Currently, static checking tools are mostly designed to check the code errors but pay little attention to the performance problems. In order to detect the defects in source code that may cause performance problems, this paper designs and achieves a performance testing tool based on static analysis method. The experiments of detecting several open source projects using our testing tool demonstrate that it can quickly find the defects in source code with high accuracy rate. The result of defection removing shows that it can significantly reduce the memory consumption of software, and it can effectively improve software performance.


LISS2019 ◽  
2020 ◽  
pp. 757-773
Author(s):  
Chongxuan Yuan ◽  
Jianming Zhu

The article provides an overview of existing performance testing types. A test plan using the Jmeter tool is considered, the ability to compile tests and analyze them to obtain results is provided, while ensuring appropriate quality and ease of use. Software testing is one of the most important steps in software development. The universal approach to test development is complicated by the sheer data volume, the availability of various techniques and tools. Of particular importance is testing the computational performance of applications. Compared to other types of testing, this type is extremely complex and requires some personnel skills. Performance is mostly tested by automated methods, but statistical testing becomes important in parametric program research.


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