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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...
The complexity of embedded devices poses new challenges to embedded software development in addition to the traditional physical requirements. Therefore, the evaluation of the quality of embedded software and its impact on these traditional properties becomes increasingly relevant. Concepts such as reuse, abstraction, cohesion, coupling, and other software attributes have been used as quality metrics...
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