Mandelbug-caused software failures are significant threats to system availability, especially in the context of mission-critical and safety-critical systems. However, there is still no systematic method for keeping the software free from Mandelbugs before release. To guarantee the availability of systems suffering from Mandelbugs, environmental-diversity-based fault tolerance techniques have been proposed to recover from the failures caused by them. In this article, we develop and study an analytic model to assess the availability of systems that utilize a sequence of environmental-diversity-based recovery methods. Improving over previous relevant studies, the availability formula we obtain in this article works for any number of recovery methods the system is equipped with; it is also independent on both the nature of those recovery methods and the order of their utilization. In addition, we consider the problem of how to arrange the set of available recovery methods to achieve the largest system availability. Based on the results of our analysis, we develop an open-source tool, called OPENS, which assists in the calculation of the optimal system availability. We validate the effectiveness of the proposed modeling approach in two ways, namely by comparing our results with those obtained for specific systems considered in relevant studies and by conducting numerical analyses for more general scenarios of its application.

Clostridium botulinum produces botulinum neurotoxins (BoNTs), highly potent substances responsible for botulism. Currently, mathematical models of C. botulinum growth and toxigenesis are largely aimed at risk assessment and do not include explicit genetic information beyond group level but integrate many component processes, such as signalling, membrane permeability and metabolic activity. In this paper we present a scheme for modelling neurotoxin production in C. botulinum Group I type A1, based on the integration of diverse information coming from experimental results available in the literature. Experiments show that production of BoNTs depends on the growth-phase and is under the control of positive and negative regulatory elements at the intracellular level. Toxins are released as large protein complexes and are associated with non-toxic components. Here, we systematically review and integrate those regulatory elements previously described in the literature for C. botulinum Group I type A1 into a population dynamics model, to build the very first computational model of toxin production at the molecular level. We conduct a validation of our model against several items of published experimental data for different wild type and mutant strains of C. botulinum Group I type A1. The result of this process underscores the potential of mathematical modelling at the cellular level, as a means of creating opportunities in developing new strategies that could be used to prevent botulism; and potentially contribute to improved methods for the production of toxin that is used for therapeutics.

Multiple-Phased Systems (MPS), i.e., systems whose operational life can be partitioned in a set of disjoint periods, called "phases", include several classes of systems such as Phased Mission Systems and Scheduled Maintenance Systems. Because of their deployment in critical applications, the dependability modeling and analysis of Multiple-Phased Systems is a task of primary relevance. The phased behavior makes the analysis of Multiple-Phased Systems extremely complex. This paper describes the modeling methodology and the solution procedure implemented in DEEM, a dependability modeling and evaluation tool specifically tailored for Multiple Phased Systems. It also describes its use for the solution of representative MPS problems. DEEM relies upon Deterministic and Stochastic Petri Nets as the modeling formalism, and on Markov Regenerative Processes for the model solution. When compared to existing general-purpose tools based on similar formalisms, DEEM offers advantages on both the modeling side (sub-models neatly model the phase-dependent behaviors of MPS), and on the evaluation side (a specialized algorithm allows a considerable reduction of the solution cost and time). Thus, DEEM is able to deal with all the scenarios of MPS which have been analytically treated in the literature, at a cost which is comparable with that of the cheapest ones, completely solving the issues posed by the phased-behavior of MPS.

The General Packet Radio Service (GPRS) extends the Global System Mobile Communication (GSM) by introducing a packet-switched transmission service. This paper analyzes the GPRS behavior under critical conditions. In particular, we focus on outages, which significantly impact the GPRS dependability. In fact, during outage periods, the cumulative number of users trying to access the service grows proportionally over time. When the system resumes its operations, the overload caused by accumulated users determines a higher probability of collisions on resources assignment and, therefore, a degradation of the overall QoS. This paper adopts a stochastic activity network modeling approach for evaluating the dependability of a GPRS network under outage conditions. The major contribution of this study lies in the novel perspective the dependability study is framed in. Starting from a quite classical availability analysis, the network dependability figures are incorporated into a very detailed service model that is used to analyze the overload effect GPRS has to face after outages, gaining deep insights on its impact on user's perceived QoS. The result of this modeling is an enhanced availability analysis, which takes into account not only the bare estimation of unavailability periods, but also the important congestion phenomenon following outages that contribute to service degradation for a certain period of time after operations resume.

This study deals with model-based dependability transient analysis of phased mission systems. A review of the studies in the literature showed that several aspects of multiphased systems pose challenging problems to the dependability evaluation methods and tools. To attack the weak points of the state-of-the-art we propose a modeling methodology that exploits the power of the class of Markov regenerative stochastic Petri net models. By exploiting the techniques available in the literature for the analysis of the Markov Regenerative Processes, we obtain an analytical solution technique with a low computational complexity, basically dominated by the cost of the separate analysis of the system inside each phase. Last, the existence of analytical solutions allows us to derive the sensitivity functions of the dependability measures, thus providing the dependability engineer with additional means for the study of phased mission systems.

Multiple-phased systems, whose operational life can be partitioned into a set of disjoint periods called "phases", include several classes of systems, such as phased mission systems and scheduled maintenance systems. Because of their deployment in critical applications, the dependability modeling and analysis of multiple-phased systems is a task of primary relevance. However, the phased behavior makes the analysis of multiple-phased systems extremely complex. This paper is centered on the description and application of DEEM, a dependability modeling and evaluation tool for multiple-phased systems. DEEM supports a powerful and efficient methodology for the analytical dependability modeling and evaluation of multiple-phased systems, based on deterministic and stochastic Petri nets and on Markov regenerative processes.

The paper deals with the automatic dependability analysis of systems designed using UML. An automatic transformation is defined for the generation of models to capture systems dependability attributes, like reliability. The transformation concentrates on structural UML views, available early in the design, to operate at different levels of refinement, and tries to capture only the information relevant for dependability to limit the size (state space) of the models. Due to the modular construction, these models can be refined later as more detailed, relevant information becomes available. Moreover a careful selection of those critical parts to be detailed allows one to avoid explosion of the size. An implementation of the transformation is in progress and will be integrated in the toolsets available for the ESPRIT LTR HIDE project.