{"title":"在推理MongoDB的演化时,要注意可变性层之间的相互作用","authors":"Luc Lesoil, M. Acher, Arnaud Blouin, J. Jézéquel","doi":"10.1145/3491204.3527489","DOIUrl":null,"url":null,"abstract":"With commits and releases, hundreds of tests are run on varying conditions (e.g., over different hardware and workload) that can help to understand evolution and ensure non-regression of software performance. We hypothesize that performance is not only sensitive to evolution of software, but also to different variability layers of its execution environment, spanning the hardware, the operating system, the build, or the workload processed by the software. Leveraging the MongoDB dataset, our results show that changes in hardware and workload can drastically impact performance evolution and thus should be taken into account when reasoning about performance. An open problem resulting from this study is how to manage the variability layers in order to efficiently test the performance evolution of a software.","PeriodicalId":129216,"journal":{"name":"Companion of the 2022 ACM/SPEC International Conference on Performance Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Beware of the Interactions of Variability Layers When Reasoning about Evolution of MongoDB\",\"authors\":\"Luc Lesoil, M. Acher, Arnaud Blouin, J. Jézéquel\",\"doi\":\"10.1145/3491204.3527489\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With commits and releases, hundreds of tests are run on varying conditions (e.g., over different hardware and workload) that can help to understand evolution and ensure non-regression of software performance. We hypothesize that performance is not only sensitive to evolution of software, but also to different variability layers of its execution environment, spanning the hardware, the operating system, the build, or the workload processed by the software. Leveraging the MongoDB dataset, our results show that changes in hardware and workload can drastically impact performance evolution and thus should be taken into account when reasoning about performance. An open problem resulting from this study is how to manage the variability layers in order to efficiently test the performance evolution of a software.\",\"PeriodicalId\":129216,\"journal\":{\"name\":\"Companion of the 2022 ACM/SPEC International Conference on Performance Engineering\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-07-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Companion of the 2022 ACM/SPEC International Conference on Performance Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3491204.3527489\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Companion of the 2022 ACM/SPEC International Conference on Performance Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3491204.3527489","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Beware of the Interactions of Variability Layers When Reasoning about Evolution of MongoDB
With commits and releases, hundreds of tests are run on varying conditions (e.g., over different hardware and workload) that can help to understand evolution and ensure non-regression of software performance. We hypothesize that performance is not only sensitive to evolution of software, but also to different variability layers of its execution environment, spanning the hardware, the operating system, the build, or the workload processed by the software. Leveraging the MongoDB dataset, our results show that changes in hardware and workload can drastically impact performance evolution and thus should be taken into account when reasoning about performance. An open problem resulting from this study is how to manage the variability layers in order to efficiently test the performance evolution of a software.