@inproceedings{7e62b29ad2dc410488df39ff92936ad4,
title = "NFRs early estimation through software metrics",
abstract = "We propose the use of regression analysis to generate accurate predictive models for physical metrics using design metrics as input. We validate our approach with 40+ implementations of three systems in two development scenarios: system evolution and first design. Results show maximum prediction errors of 1.66\% during system evolution. In a first design scenario, the average error is 15\% with the maximum error still below 20\% for all physical metrics. This approach provides a fast and accurate strategy to boost embedded software productivity and quality, by estimating Non-Functional Requirements (NFRs) during the first design stages.",
keywords = "Embedded Systems, Performance Estimation, Software Metrics, Regression Analysis",
author = "Andrws Vieira and Pedro Faustini and Luigi Carro and {\'E}rika Cota",
year = "2015",
doi = "10.7873/date.2015.0877",
language = "English",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
pages = "329--332",
booktitle = "Proceedings of the 2015 Design, Automation and Test in Europe Conference and Exhibition (DATE)",
address = "United States",
note = "2015 Design, Automation and Test in Europe Conference and Exhibition, DATE 2015 ; Conference date: 09-03-2015 Through 13-03-2015",
}