Print Email Facebook Twitter Performance prediction for families of data-intensive software applications Title Performance prediction for families of data-intensive software applications Author Verriet, J. Dankers, R. Somers, L. Publication year 2018 Abstract Performance is a critical system property of any system, in particular of data-intensive systems, such as image processing systems. We describe a performance engineering method for families of data-intensive systems that is both simple and accurate; the performance of new family members is predicted using models of existing family members. The predictive models are calibrated using static code analysis and regression. Code analysis is used to extract performance profiles, which are used in combination with regression to derive predictive performance models. A case study presents the application for an industrial image processing case, which revealed as benefits the easy application and identification of code performance optimization points. Subject ICTESI - Embedded Systems InnovationsTS - Technical SciencesInformaticsIndustrial InnovationSoftware performance engineeringLoop analysisProduct familiesData-intensive systems To reference this document use: http://resolver.tudelft.nl/uuid:08d568f1-c98e-4c75-9a18-2e136b1995ad DOI https://doi.org/10.1145/3185768.3186405 TNO identifier 785814 Publisher ACM ISBN 9781450356299 Source 9th ACM/SPEC International Conference on Performance Engineering (ICPE 2018), Berlin, Germany - April 9-13, 2018 Document type conference paper Files To receive the publication files, please send an e-mail request to TNO Library.