Dusan Okanovic, A. Hoorn, C. Zorn, Fabian Beck, Vincenzo Ferme, J. Walter
{"title":"关注驱动的软件性能分析结果报告","authors":"Dusan Okanovic, A. Hoorn, C. Zorn, Fabian Beck, Vincenzo Ferme, J. Walter","doi":"10.1145/3302541.3313103","DOIUrl":null,"url":null,"abstract":"State-of-the-art approaches for reporting performance analysis results rely on charts providing insights on the performance of the system, often organized in dashboards. The insights are usually data-driven, i.e., not directly connected to the performance concern leading the users to execute the performance engineering activity, thus limiting the understandability of the provided result. A cause is that the data is presented without further explanations. To solve this problem, we propose a concern-driven approach for reporting of performance evaluation results, shaped around a performance concern stated by a stakeholder and captured by state-of-the-art declarative performance engineering specifications. Starting from the available performance analysis, the approach automatically generates a customized performance report providing a chart- and natural-language-based answer to the concern. In this paper, we introduce the general concept of concern-driven performance analysis reporting and present a first prototype implementation of the approach. We envision that, by applying our approach, reports tailored to user concerns reduce the effort to analyze performance evaluation results.","PeriodicalId":231712,"journal":{"name":"Companion of the 2019 ACM/SPEC International Conference on Performance Engineering","volume":"363 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Concern-driven Reporting of Software Performance Analysis Results\",\"authors\":\"Dusan Okanovic, A. Hoorn, C. Zorn, Fabian Beck, Vincenzo Ferme, J. Walter\",\"doi\":\"10.1145/3302541.3313103\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"State-of-the-art approaches for reporting performance analysis results rely on charts providing insights on the performance of the system, often organized in dashboards. The insights are usually data-driven, i.e., not directly connected to the performance concern leading the users to execute the performance engineering activity, thus limiting the understandability of the provided result. A cause is that the data is presented without further explanations. To solve this problem, we propose a concern-driven approach for reporting of performance evaluation results, shaped around a performance concern stated by a stakeholder and captured by state-of-the-art declarative performance engineering specifications. Starting from the available performance analysis, the approach automatically generates a customized performance report providing a chart- and natural-language-based answer to the concern. In this paper, we introduce the general concept of concern-driven performance analysis reporting and present a first prototype implementation of the approach. We envision that, by applying our approach, reports tailored to user concerns reduce the effort to analyze performance evaluation results.\",\"PeriodicalId\":231712,\"journal\":{\"name\":\"Companion of the 2019 ACM/SPEC International Conference on Performance Engineering\",\"volume\":\"363 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-03-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Companion of the 2019 ACM/SPEC International Conference on Performance Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3302541.3313103\",\"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 2019 ACM/SPEC International Conference on Performance Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3302541.3313103","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Concern-driven Reporting of Software Performance Analysis Results
State-of-the-art approaches for reporting performance analysis results rely on charts providing insights on the performance of the system, often organized in dashboards. The insights are usually data-driven, i.e., not directly connected to the performance concern leading the users to execute the performance engineering activity, thus limiting the understandability of the provided result. A cause is that the data is presented without further explanations. To solve this problem, we propose a concern-driven approach for reporting of performance evaluation results, shaped around a performance concern stated by a stakeholder and captured by state-of-the-art declarative performance engineering specifications. Starting from the available performance analysis, the approach automatically generates a customized performance report providing a chart- and natural-language-based answer to the concern. In this paper, we introduce the general concept of concern-driven performance analysis reporting and present a first prototype implementation of the approach. We envision that, by applying our approach, reports tailored to user concerns reduce the effort to analyze performance evaluation results.