Performance test-oriented monitoring method and system

Abstract

The invention relates to a performance test-oriented monitoring method and system. According to the method, after a performance test scene is started, monitoring of related indexes of a machine deployed by a tested system, a system process, middleware and a database is started, monitoring data is stored, after the test scene is finished, statistical calculation analysis is conducted on the monitoring data, an execution log and a monitoring data trend chart are generated, and the performance of the tested system is tested. The method is used for performance test bottleneck analysis and performance test report data. Through actual performance test implementation, the workload of performance testers can be reduced, and the performance test execution efficiency is improved.

Description

technical field [0001] The invention belongs to the field of intelligent operation and maintenance, and specifically relates to a performance test-oriented monitoring method and system, which are mainly used in the performance test industry and used for performance test implementation to monitor the system and application indicators. Background technique [0002] At present, the existing monitoring tools are mainly performed separately for a single performance test scenario. It mainly includes test preparation, test scenario execution, and test result statistical analysis. Since each link requires testers to execute each test scenario separately, it has problems such as long time consumption and heavy workload of testers. In addition, it also Has the following deficiencies: [0003] ①The readability of the monitored indicator information is low; [0004] ②The monitoring granularity cannot be adjusted according to the demand; [0005] ③ It has a relatively high impact on t...

AI Technical Summary

Problems solved by technology

It mainly includes test preparation, test scenario execution, and test result statistical analysis. Since each link requires testers to execute each test scenario separately, it has problems such as long time consumption and heavy workload of testers. In addition, it also Has the following deficiencies:

[0003] ①The readability of the monitored indicator information is low;

[0004] ②The monitoring granularity cannot be adjusted according to the demand;

[0005] ③ It has a relatively high impact on the use of monitored machine resources;

[0006] ④ It cannot be closely related to the stress test scenario;

[0007] ⑤Monitoring data cannot generate directly usable data such as test reports

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