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The paper introduces a method which allows quantitative dependability analysis of systems modeled by using the Unified Modeling Language (UML) statechart diagrams. The analysis is performed by transforming the UML model to stochastic reward nets (SRNs). A large subset of statechart model elements is supported including event processing, state hierarchy and transition priorities. The transformation is presented by a set of SRN patterns. Performance-related measures can be directly derived using SRN tools, while dependability analysis requires explicit modeling of erroneous states and faulty behavior.

Recommender systems evaluation is usually based on predictive accuracy metrics with better scores meaning recommendations of higher quality. However, the comparison of results is becoming increasingly difficult, since there are different recommendation frameworks and different settings in the design and implementation of the experiments. Furthermore, there might be minor differences on algorithm implementation among the different frameworks. In this paper, we compare well known recommendation algorithms, using the same dataset, metrics and overall settings, the results of which point to result differences across frameworks with the exact same settings. Hence, we propose the use of standards that should be followed as guidelines to ensure the replication of experiments and the reproducibility of the results.

The paper introduces a method to model embedded dependability−critical systems as AND−composition of Guarded Statecharts which are special UML− statecharts. With Guarded Statecharts we can model the reactive behavior of embedded systems so that their quantitative analysis can be performed. First, we present our motivation for using Guarded Statecharts to express the interaction between hardware and software compo− nents of embedded systems, and to model faults and errors as state perturbations. Then we discuss how these models are transformed into Stochastic Reward Nets amenable to a quantitative dependability analysis. Finally, our approach is illustrated by an example.

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