{"title":"PReT:一个基于阶段的自动回归测试工具","authors":"Arnamoy Bhattacharyya, C. Amza","doi":"10.1109/CloudCom2018.2018.00062","DOIUrl":null,"url":null,"abstract":"In this paper, we present our tool PReT, which performs automatic performance regression testing on software. PReT does non-intrusive profiling based on application snapshots to learn behaviour for performance regression tests and can identify any changes in the testing behaviour by comparing the current behaviour against a learned model. PReT annotates resource usage profiles with application stacktraces and uses a variation of k-means to learn the models per regression test online. On top of that, PReT uses version information of the software to identify change(s) that introduce(s) performance issue(s) if any. We show the usefulness of PReT in correctly identifying two real world performance bugs in Cassandra database server. We show that PReT is able to characterize the performance tests being run for the software with higher accuracy than a purely resource utilization based characterization technique.","PeriodicalId":365939,"journal":{"name":"2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"PReT: A Tool for Automatic Phase-Based Regression Testing\",\"authors\":\"Arnamoy Bhattacharyya, C. Amza\",\"doi\":\"10.1109/CloudCom2018.2018.00062\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we present our tool PReT, which performs automatic performance regression testing on software. PReT does non-intrusive profiling based on application snapshots to learn behaviour for performance regression tests and can identify any changes in the testing behaviour by comparing the current behaviour against a learned model. PReT annotates resource usage profiles with application stacktraces and uses a variation of k-means to learn the models per regression test online. On top of that, PReT uses version information of the software to identify change(s) that introduce(s) performance issue(s) if any. We show the usefulness of PReT in correctly identifying two real world performance bugs in Cassandra database server. We show that PReT is able to characterize the performance tests being run for the software with higher accuracy than a purely resource utilization based characterization technique.\",\"PeriodicalId\":365939,\"journal\":{\"name\":\"2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)\",\"volume\":\"101 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CloudCom2018.2018.00062\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CloudCom2018.2018.00062","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
PReT: A Tool for Automatic Phase-Based Regression Testing
In this paper, we present our tool PReT, which performs automatic performance regression testing on software. PReT does non-intrusive profiling based on application snapshots to learn behaviour for performance regression tests and can identify any changes in the testing behaviour by comparing the current behaviour against a learned model. PReT annotates resource usage profiles with application stacktraces and uses a variation of k-means to learn the models per regression test online. On top of that, PReT uses version information of the software to identify change(s) that introduce(s) performance issue(s) if any. We show the usefulness of PReT in correctly identifying two real world performance bugs in Cassandra database server. We show that PReT is able to characterize the performance tests being run for the software with higher accuracy than a purely resource utilization based characterization technique.