Abstract Process mining is concerned with deriving formal models capable of reproducing the behaviour of a given organisational process by analysing observed executions collected in an event log . The elements of an event log are finite sequences (called also traces or words ) of actions. Many effective algorithms have been introduced which issue a control flow model (commonly in Petri net form) aimed at reproducing, as precisely as possible, the language of the considered event log. However, given that identical executions can be observed several times, traces of an event log are associated with a frequency and, hence, an event log inherently yields also a stochastic language . By exploiting the trace frequencies contained in the event log, the stochastic extension of process mining, therefore, consists in deriving stochastic (Petri net) models capable of reproducing the likelihood of the observed executions. In this paper, we introduce a novel stochastic process mining approach. Starting from a non-stochastic Petri net model mined through classical mining algorithms, we employ optimization to identify optimal weights for the transitions of the mined net so that the stochastic language issued by the stochastic interpretation of the mined net closely resembles that of the event log. The optimization is either based on the maximum likelihood principle or on the earth moving distance and we study in detail the characteristics of the associated objective function in both cases. It turns out that the objective function in case of using the maximum likelihood approach lends itself better to optimization. Experiments on some popular real system logs show an improved accuracy with respect to alternative approaches.

Abstract A digital twin is a virtual replica of a physical system that has to interact with it in real-time in order to facilitate decision-making, to reduce failures and costs, and to ensure a coherent and safe system execution. We call effectiveness the ability of the digital twin to direct the physical counterpart. In this paper we provide the means to evaluate the effectiveness of a digital twin in the case that the physical system is operating under uncertainty, and it is therefore subject to perturbations . Specifically, we present the DT-Stark tool, that extends Stark , a tool for modelling and verification of systems operating under uncertainty, with feedback , a special mechanism that allow us to model the communications, and their effects, between the digital and the physical (perturbed) twin in a concise, clean fashion. We can then exploit the features of Stark to compare the behaviour of the twins, to verify properties over them, and to measure effectiveness. We provide some examples of the use of our tool by applying it to the evaluation of the effectiveness of digital twins in two robotic scenarios: an industrial plant and a smart hospital.

Robot Operating System 2 (ROS 2) is the de-facto standard framework for developing distributed robotic applications. However, ensuring the correctness and security of these applications remains a significant challenge. This paper presents a novel approach to statically analyze ROS 2 applications using abstract interpretation. By extracting the architecture graph of the application, our method derives minimal access control policies that can be used to leverage security. We implemented our approach using the Library for Static Analysis (LiSA), providing a toolset that facilitates the development of sound static analyzers for ROS 2. The results demonstrate the effectiveness of our approach in enhancing the security of ROS 2 applications.

Static analysis tools play a crucial role in software development by detecting bugs and vulnerabilities. However, running these tools separately from the code editing process often causes developers to switch contexts, which can reduce productivity. Previous work has shown how Reference Attribute Grammars (RAGs) can be used for declarative implementation of competitive tooling for intraprocedural control-flow and dataflow analysis of Java source code, embodied in the tool IntraJ. In this paper, we demonstrate how IntraJ can be leveraged to provide interactive analysis results directly in the editor, similar to compile-time error detection, relying on automatic on-demand evaluation of RAGs. We discuss the architecture of IntraJ, and demonstrate how it can be integrated into the development process in three different ways: in the command line, in an editor integration based on the Language Server Protocol, and in an integration with the debugging tool CodeProber. We showcase the extensibility of IntraJ by illustrating how new client analyzes and language constructs can be added to the framework through RAG specifications. Finally, we evaluate the interactive performance of IntraJ on a set of real-world Java benchmarks, demonstrating that IntraJ can provide interactive feedback to developers, achieving a response time of under 0.1 seconds for most compilation units.

The synthesis problem for partially observable Markov decision processes (POMDPs) is to compute a policy that provably adheres to one or more specifications. Yet, the general problem is undecidable, and policies require full (and thus potentially unbounded) traces of execution history. To provide good approximations of such policies, POMDP agents often employ randomization over action choices. We consider the problem of computing simpler policies for POMDPs, and provide several approaches to still ensure their expressiveness. Key aspects are (1) the combination of an arbitrary number of specifications the policies need to adhere to, (2) a restricted form of randomization, and (3) a light-weight preprocessing of the POMDP model to encode memory. We provide a novel encoding as a mixed-integer linear program as baseline to solve the underlying problems. Our experiments demonstrate that the policies we obtain are more robust, smaller, and easier to implement for an engineer than those obtained from state-of-the-art POMDP solvers.

Artificial ants are “small” units, moving autonomously on a shared, dynamically changing “space”, directly or indirectly exchanging some kind of information. Artificial ants are frequently conceived as a paradigm for collective adaptive systems. In this paper, we discuss means to represent continuous moves of “ants” in discrete models. More generally, we challenge the role of the notion of “time” in artificial ant systems and models. We suggest a modeling framework that structures behavior along causal dependencies rather than temporal relations. We present all arguments with the help of a simple example. As a modeling framework we employ Heraklit; an emerging framework that has already proven its worth in many contexts. Different concrete collective systems share similar features, despite differences in the size of basic sets, concrete values of functions, etc., and can therefore be conceived as instantiations of a single schema. Hence, we need a representation of systems on the schematic level.