In the process of testing of concurrent timed systems, input generation identifies values of temporal parameters that let the Implementation Under Test (IUT) execute selected cases. However, when some parameters are not under control of the driver, test execution may diverge from the selected input and produce an inconclusive behavior. We formulate the problem on the basis of an abstraction of the IUT which we call partially stochastic Time Petri Net (psTPN), where controllable parameters are modeled as nondeterministic values and noncontrollable parameters as random variables with general (GEN) distribution. With reference to this abstraction, we derive the analytical form of the probability that the IUT runs along a selected behavior as a function of choices taken on controllable parameters. In the applicative perspective of real-time testing, this identifies a theoretical upper limit on the probability of a conclusive result, thus providing a means to plan the number of test repetitions that are necessary to guarantee a given probability of test-case coverage. It also provides a constructive technique for an optimal or suboptimal approach to input generation and a way to characterize the probability of conclusive testing under other suboptimal strategies.

Preemptive Time Petri Nets (pTPNs) support modeling and analysis of concurrent timed SW components running under fixed priority preemptive scheduling. The model is supported by a well-established theory based on symbolic state space analysis through Difference Bounds Matrix (DBM) zones, with specific contributions on compositional modularization, trace analysis, and efficient overapproximation and cleanup in the management of suspension deriving from preemptive behavior. In this paper, we devise and implement a framework that brings the theory to application. To this end, we cast the theory into an organic tailoring of design, coding, and testing activities within a V-Model SW life cycle in respect of the principles of regulatory standards applied to the construction of safety-critical SW components. To implement the toolchain subtended by the overall approach into a Model Driven Development (MDD) framework, we complement the theory of state space analysis with methods and techniques supporting semiformal specification and automated compilation into pTPN models and real-time code, measurement-based Execution Time estimation, test case selection and execution, coverage evaluation.

Critical systems require supervising infrastructures to keep their unreliability under control. We propose safety-critical systems to be modeled through a fault-tolerant architecture based on Stochastic Fault Trees (SFTs) and we refer to a scenario where the monitoring infrastructure is a Wireless Sensor Network (WSN). SFTs associate the failure time of leaf events with a non-Markovian (GEN) cumulative distribution function (CDF) and support the evaluation of system unreliability over time. In the reference scenario, the SFT model dynamically updates system unreliability according to samples delivered by the WSN, it maintains a dynamic measure of the safe time-horizon within which the system is expected to operate under a given threshold of unreliability, and it also provides the WSN with a measure of the contribution of each basic event to system unreliability.