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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...
In this paper, an approach is proposed to assist the designer in evaluating the impact of a design choice with respect to the Non-Functional Requirements (NFRs) in embedded systems. We use design metrics extracted from class diagrams and regression models to estimate, in the early development stages, the impact of software changes on NFRs. This prediction can be used during the first design to compare...
This paper proposes a strategy to assist the designer in evaluating the impact of a design choice with respect to the non-functional requirements in embedded systems. We use several regression models to predict physical metrics from design metrics in order to estimate the impact on performance of software changes in the early stages of its development. This prediction can be used both during maintenance...
This paper presents an analysis of embedded applications based on Android Platform. Analyzing performance and energy consumption from different algorithmic versions this work tries to find a performance and energy pattern for the paradigm used in each used algorithm. Thus, the developer can select the best algorithm version for each application based on the requirements. Android is a Linux based mobile...
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