{"title":"白盒性能影响模型:分析和学习方法(复制包)","authors":"Max Weber, S. Apel, Norbert Siegmund","doi":"10.1109/ICSE-Companion52605.2021.00107","DOIUrl":null,"url":null,"abstract":"These artifacts refer to the study and implementation of the paper 'White-Box Performance-Influence Models: A Profiling and Learning Approach'. In this document, we describe the idea and process of how to build white-box performance models for configurable software systems. Specifically, we describe the general steps and tools that we have used to implement our approach, the data we have obtained, and the evaluation setup. We further list the available artifacts, such as raw measurements, configurations, and scripts at our software heritage repository.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"White-Box Performance-Influence Models: A Profiling and Learning Approach (Replication Package)\",\"authors\":\"Max Weber, S. Apel, Norbert Siegmund\",\"doi\":\"10.1109/ICSE-Companion52605.2021.00107\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"These artifacts refer to the study and implementation of the paper 'White-Box Performance-Influence Models: A Profiling and Learning Approach'. In this document, we describe the idea and process of how to build white-box performance models for configurable software systems. Specifically, we describe the general steps and tools that we have used to implement our approach, the data we have obtained, and the evaluation setup. We further list the available artifacts, such as raw measurements, configurations, and scripts at our software heritage repository.\",\"PeriodicalId\":136929,\"journal\":{\"name\":\"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)\",\"volume\":\"46 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSE-Companion52605.2021.00107\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSE-Companion52605.2021.00107","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
White-Box Performance-Influence Models: A Profiling and Learning Approach (Replication Package)
These artifacts refer to the study and implementation of the paper 'White-Box Performance-Influence Models: A Profiling and Learning Approach'. In this document, we describe the idea and process of how to build white-box performance models for configurable software systems. Specifically, we describe the general steps and tools that we have used to implement our approach, the data we have obtained, and the evaluation setup. We further list the available artifacts, such as raw measurements, configurations, and scripts at our software heritage repository.