As software production achieves a growing importance in the embedded systems world, quality evaluation of embedded software and its impact on physical properties of embedded systems becomes increasingly relevant.
Although there are tools for embedded software design that improve software specification and verification, we are still short of a tool that supports the designer’s decisions on the best design strategy regarding low level, physical characteristics like performance, energy, and memory footprint, which are critical in the embedded domain.
In this paper, we provide an analysis of the correlation between software quality metrics and physical metrics for embedded design.By means of experiments, we investigate the impact of software engineering best practices on embedded software and show that software quality metrics can be used to guide design decisions toward improving physical properties of embedded systems.
In this work, we investigate the relationship between traditional (classical) software quality metrics and the relevant physical metrics for embedded systems. Different design decisions over the application model influence these metrics, thus we intend to find out which software quality metrics are relevant for embedded software design.
Moreover, we show that the best design practices of traditional software can sometimes negatively impact the physical properties of embedded systems, which implies that some sacrifices in terms of reuse or maintainability are required to achieve a better performance.
Finally, we propose to use the knowledge about the relationship between quality and physical metrics to suggest modifications in the modeling solution that will improve this solution regarding the physical metrics.
To read this external content in full, download the paper from the authors’ archive.