{"title":"负载测试的自动性能分析","authors":"Z. Jiang, A. Hassan, Gilbert Hamann, P. Flora","doi":"10.1109/ICSM.2009.5306331","DOIUrl":null,"url":null,"abstract":"The goal of a load test is to uncover functional and performance problems of a system under load. Performance problems refer to the situations where a system suffers from unexpectedly high response time or low throughput. It is difficult to detect performance problems in a load test due to the absence of formally-defined performance objectives and the large amount of data that must be examined. In this paper, we present an approach which automatically analyzes the execution logs of a load test for performance problems. We first derive the system's performance baseline from previous runs. Then we perform an in-depth performance comparison against the derived performance baseline. Case studies show that our approach produces few false alarms (with a precision of 77%) and scales well to large industrial systems.","PeriodicalId":247441,"journal":{"name":"2009 IEEE International Conference on Software Maintenance","volume":"112 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"122","resultStr":"{\"title\":\"Automated performance analysis of load tests\",\"authors\":\"Z. Jiang, A. Hassan, Gilbert Hamann, P. Flora\",\"doi\":\"10.1109/ICSM.2009.5306331\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The goal of a load test is to uncover functional and performance problems of a system under load. Performance problems refer to the situations where a system suffers from unexpectedly high response time or low throughput. It is difficult to detect performance problems in a load test due to the absence of formally-defined performance objectives and the large amount of data that must be examined. In this paper, we present an approach which automatically analyzes the execution logs of a load test for performance problems. We first derive the system's performance baseline from previous runs. Then we perform an in-depth performance comparison against the derived performance baseline. Case studies show that our approach produces few false alarms (with a precision of 77%) and scales well to large industrial systems.\",\"PeriodicalId\":247441,\"journal\":{\"name\":\"2009 IEEE International Conference on Software Maintenance\",\"volume\":\"112 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-10-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"122\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 IEEE International Conference on Software Maintenance\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSM.2009.5306331\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 IEEE International Conference on Software Maintenance","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSM.2009.5306331","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The goal of a load test is to uncover functional and performance problems of a system under load. Performance problems refer to the situations where a system suffers from unexpectedly high response time or low throughput. It is difficult to detect performance problems in a load test due to the absence of formally-defined performance objectives and the large amount of data that must be examined. In this paper, we present an approach which automatically analyzes the execution logs of a load test for performance problems. We first derive the system's performance baseline from previous runs. Then we perform an in-depth performance comparison against the derived performance baseline. Case studies show that our approach produces few false alarms (with a precision of 77%) and scales well to large industrial systems.