We present the next generation of ORIS, a toolbox for quantitative evaluation of concurrent models with non-Markovian timers. The tool shifts its focus from timed models to stochastic ones, it includes a new graphical user interface, new analysis methods and a Java Application Programming Interface (API). Models can be specified as Stochastic Time Petri Nets (STPNs) through the graphical editor, validated using an interactive token game, and analyzed through several techniques to compute instantaneous or cumulative rewards. STPNs can also be exported as Java code to conduct extensive parametric studies through the Java library, now distributed as open-source. A well-engineered software architecture allows the user to implement new features for STPNs, new modeling formalisms, and new analysis methods. The most distinctive features of ORIS include transient and steady-state analysis of STPNs modeling Markov Regenerative Processes (MRPs), and transient analysis of STPNs modeling generalized semi-Markov processes. ORIS also supports state-space analysis of Time Petri Nets (TPNs), simulation of STPNs, and standard analysis techniques for continuous-time Markov chains or MRPs with at most one non-exponential timer in each state. We illustrate the general workflow for the application of ORIS to the modeling and evaluation of non-functional requirements of software-intensive systems.

Dynamic fault trees (DFT) are widely adopted in industry to assess the dependability of safety-critical equipment. Since many systems are too large to be studied numerically, DFTs dependability is often analysed using Monte Carlo simulation. A bottleneck here is that many simulation samples are required in the case of rare events, e.g. in highly reliable systems where components fail seldomly. Rare event simulation (RES) provides techniques to reduce the number of samples in the case of rare events. We present a RES technique based on importance splitting, to study failures in highly reliable DFTs. Whereas RES usually requires meta-information from an expert, our method is fully automatic: By cleverly exploiting the fault tree structure we extract the so-called importance function. We handle DFTs with Markovian and non-Markovian failure and repair distributions—for which no numerical methods exist—and show the efficiency of our approach on several case studies.

We propose an approach for assessing the impact of multi-phased repair procedures on gas distribution networks, capturing load profiles that can depend on time for different classes of users, suspension of activities during non-working hours, and random execution times depending on topological, physical, and geographical characteristics of the network. The problem is characterized through a semi-formal specification based on artifacts of the Systems Modeling Language (SysML), which is then translated into a formal model based on stochastic time Petri nets. The solution method interleaves fluid-dynamic analysis of the gas behavior and stochastic analysis of the time spent in the repair process, decoupling complexities and making stochastic analysis almost insensitive to the network size and topology. Hence, our approach turns out to be applicable to real scale cases, notably computing the optimal time of day to start the repair procedure. Moreover, by encompassing general (non-Markovian) distributions, the approach enables effective fitting of durations.

We present a model-based approach to Activity Recognition (AR) in Ambient Assisted Living (AAL). The approach leverages an a priori stochastic model termed Continuous-Time Hidden Semi-Markov Model (CT-HSMM), capturing the continuous-time durations of activities and inter-event times. The model is enhanced according to the observed statistics, associating the events with an occurrence probability, and the sojourn time and the inter-event time in each activity with a continuous-time probability density function, allowing effective fitting of observed durations through non-Markovian distributions. The model is updated at run time according to a sequence of time-stamped observations, exploiting the method of stochastic state classes to perform transient analysis and derive a measure of likelihood that an activity is currently performed. The approach supports both online AR, predicting the activity performed at time t using only the events observed until that time, and offline AR, applying a forward- backward procedure that exploits all the events observed before and after time t. The approach is experimented on a real dataset of the literature, providing performance measures that can be compared with those of offline Hidden Markov Models (HMMs) and offline Hidden Semi-Markov Models (HSMMs).

Agile production systems face major issues in satisfying fickle market needs in highly demand-driven industry sectors, such as electronics and mechatronics. In this context, the time needed to complete the production of an item tends to be highly variable, and online estimation of the remaining completion time may suffer the lack of adequate sensor data, especially in existing manufacturing systems. To solve this issue, we propose a new analytical technique for the evaluation of an upper and a lower stochastic bound on the remaining completion time of a product, considering an assembly line made of sequential workstations with transfer blocking and buffer capacity. The approach notably encompasses service times with non-Markovian distribution, and avoids the limitation of existing works requiring the system to be at steady state at the inspection time. The technique is experimented on a case study and validated through simulation, providing an empirical analysis of its complexity.