Tomás Fiedor, Jirí Pavela, Adam Rogalewicz, Tomáš Vojnar
{"title":"Perun:性能版本系统","authors":"Tomás Fiedor, Jirí Pavela, Adam Rogalewicz, Tomáš Vojnar","doi":"10.1109/ICSME55016.2022.00067","DOIUrl":null,"url":null,"abstract":"In this paper, we present PERUN: an open-source tool suite for profiling-based performance analysis. At its core, PERUN maintains links between project versions and the corresponding stored performance profiles, which are then leveraged for automated detection of performance changes in new project versions. The PERUN tool suite further includes multiple profilers (and is designed such that further profilers can be easily added), a performance fuzz-tester for workload generation, methods for deriving performance models, and numerous visualization methods. We demonstrate how PERUN can help developers to analyze their program performance on two examples: detection and localization of a performance degradation and generation of inputs forcing performance issues to show up.","PeriodicalId":300084,"journal":{"name":"2022 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Perun: Performance Version System\",\"authors\":\"Tomás Fiedor, Jirí Pavela, Adam Rogalewicz, Tomáš Vojnar\",\"doi\":\"10.1109/ICSME55016.2022.00067\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we present PERUN: an open-source tool suite for profiling-based performance analysis. At its core, PERUN maintains links between project versions and the corresponding stored performance profiles, which are then leveraged for automated detection of performance changes in new project versions. The PERUN tool suite further includes multiple profilers (and is designed such that further profilers can be easily added), a performance fuzz-tester for workload generation, methods for deriving performance models, and numerous visualization methods. We demonstrate how PERUN can help developers to analyze their program performance on two examples: detection and localization of a performance degradation and generation of inputs forcing performance issues to show up.\",\"PeriodicalId\":300084,\"journal\":{\"name\":\"2022 IEEE International Conference on Software Maintenance and Evolution (ICSME)\",\"volume\":\"67 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-07-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE International Conference on Software Maintenance and Evolution (ICSME)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSME55016.2022.00067\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE International Conference on Software Maintenance and Evolution (ICSME)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSME55016.2022.00067","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper, we present PERUN: an open-source tool suite for profiling-based performance analysis. At its core, PERUN maintains links between project versions and the corresponding stored performance profiles, which are then leveraged for automated detection of performance changes in new project versions. The PERUN tool suite further includes multiple profilers (and is designed such that further profilers can be easily added), a performance fuzz-tester for workload generation, methods for deriving performance models, and numerous visualization methods. We demonstrate how PERUN can help developers to analyze their program performance on two examples: detection and localization of a performance degradation and generation of inputs forcing performance issues to show up.