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We propose a novel research line integrating Statistical Model Checking (SMC), a family of simulation-based analysis techniques from quantitative formal methods, with Process Mining (PM), a collection of data-driven process-oriented techniques. SMC and PM are complementary. SMC focuses on performing the right number of simulations to obtain statistically-reliable estimations (e.g., the probability of success of an attack). PM focuses on reconstructing a model of a system using logs of its traces. Nevertheless, both approaches aim at providing evidence of issues/guarantees of the system, and at proposing enhancements. We aim at enriching SMC by explaining why it produced specific estimates. This might help, e.g., identifying issues in the model (validation) or suggesting improvements (enhancement). Given that SMC uses statistics to decide what is the correct number of simulations (or traces), we avoid by-construction the complex issue of under-representation of system behavior in the logs crucial to many PM exercises. This work-in-progress paper demonstrates the proposed methodology and its usefulness using a simple example from the security threat modeling domain. We show how PM helps highlighting both mistakes in the model, and possibilities for improvement.

Boolean Networks (BN) are established tools for modelling biological systems. However, their analysis is hindered by the state space explosion: the exponentially many states on the variables of a BN. We present an extension of the tool for model reduction ERODE with support for BNs and their reduction with a recent method called Backward Boolean Equivalence (BBE). BBE identifies maximal sets of variables that retain the same value whenever initialized equally. ERODE has been also extended to support importing and exporting between different formats and model repositories, enhancing interoperability with other tools.

Models of complex systems often consist of state variables with structurally similar dynamics that differ in the specific values of some parameters. Examples are multi-class epidemiological models, chemical reaction networks describing multiple components (e.g., binding sites) with equivalent functional behavior, and models of electric circuits with replicated designs. In these cases, the analysis may be expensive due to the model size. Here we consider models defined as systems of polynomial ordinary differential equations (ODEs) with positive solutions. We present an algorithm to reduce the computational cost by transforming the original ODE model into one for which we can compute an appropriate over-approximation on a smaller set of state variables. The algorithm is based on the theory of differential inequalities and consists of two steps. The first step computes a differential hull, which is an ODE system providing lower and upper bounds for each state variable. The hull is constructed such that variables with structurally similar dynamics but originally different parameters may now be represented by the same lower and upper bounds. Based on this, the second step exploits already developed notions of exact model reduction for ODEs to lump such variables. The algorithm is showcased on several case studies and its results are favourably compared against CORA, a well-known tool for reachability analysis of dynamical systems

Boolean Networks (BN) are established tools for modelling biological systems. However, their analysis is hindered by the state space explosion: the exponentially many states on the variables of a BN. We present an extension of the tool for model reduction ERODE with support for BNs and their reduction with a recent method called Backward Boolean Equivalence (BBE). BBE identifies maximal sets of variables that retain the same value whenever initialized equally. ERODE has been also extended to support importing and exporting between different formats and model repositories, enhancing interoperability with other tools

Boolean Networks (BNs) are established models to qualitatively describe biological systems. The analysis of BNs might be infeasible for medium to large BNs due to the state-space explosion problem. We propose a novel reduction technique called \emph{Backward Boolean Equivalence} (BBE), which preserves some properties of interest of BNs. In particular, reduced BNs provide a compact representation by grouping variables that, if initialized equally, are always updated equally. The resulting reduced state space is a subset of the original one, restricted to identical initialization of grouped variables. The corresponding trajectories of the original BN can be exactly restored. We show the effectiveness of BBE by performing a large-scale validation on the whole GINsim BN repository. In selected cases, we show how our method enables analyses that would be otherwise intractable. Our method complements, and can be combined with, other reduction methods found in the literature.

QFLan offers modeling and analysis of highly reconfigurable systems, like product lines, which are characterized by combinatorially many system variants (or products) that can be obtained via different combinations of installed features. The tool offers a modern integrated development environment for the homonym probabilistic feature-oriented language. QFLan allows the specification of a family of products in terms of a feature model with quantitative attributes, which defines the valid feature combinations, and probabilistic behavior subject to quantitative constraints. The language's behavioral part enables dynamic installation, removal and replacement of features. QFLan has a discrete-time Markov chain semantics, permitting quantitative analyses. Thanks to a seamless integration with the statistical model checker MultiVeStA, it allows for analyses like the likelihood of specific behavior or the expected average value of non-functional aspects related to feature attributes.