Performance analysis with confidence intervals for embedded software processes

The choice of algorithms has a large impact on the performance of embedded real-time systems. Therefore, performance estimation of embedded software is vital in an early design phase. Consequently, high-level estimation techniques have been devised, but the accuracy of the estimations vary a lot depending on the algorithm and its context. We address this problem by proposing an estimation technique that both estimates the performance and computes the expected accuracy. The accuracy is used to provide a confidence interval to the estimated performance. The estimation framework presented is crafted to fit with the MASCOT environment, but the underlying techniques can also be applied to other high-level design exploration frameworks.