Michael Glaß

TL;DR: A new importance measure for time-independent reliability analysis based on the change in mean time to failure caused by the failure (success) of a component is proposed and possesses some attractive properties.

TL;DR: This paper shows by example how probabilistic bit flips are systematically abstracted and propagated towards higher abstraction levels up to the application software layer, and how RAP can be used to parameterize architecture-level resilience methods.

TL;DR: The results show that the proposed method outperforms a state-of-the-art approach regarding optimization quality, particularly in the search for highly-reliable yet affordable implementations - at negligible runtime overhead.

TL;DR: The proposed uncertainty-aware method combines a formal analysis approach and a Monte Carlo simulation to consider uncertain characteristics and their different correlations, delivering a holistic view on the system's reliability with best/worst/average-case behavior and also insights on variance and quantiles.

TL;DR: This paper presents a highly efficient technique to compute the reliability and structural importance measures of components of a system and integrated the approach in an existing multi-objective local-search algorithm that is part of an automatic system-level design space exploration which seeks for system implementations with highest reliability at lowest possible cost.