Verifying safety and persistence in hybrid systems using flowpipes and continuous invariants

We describe a method for verifying the temporal property of persistence in non-linear hybrid systems. Given some system and an initial set of states, the method establishes that system trajectories always eventually evolve into some specified target subset of the states of one of the discrete modes of the system, and always remain within this target region. The method also computes a time-bound within which the target region is always reached. The approach combines flowpipe computation with deductive reasoning about invariants and is more general than each technique alone. We illustrate the method with a case study showing that potentially destructive stick-slip oscillations of an oil-well drill eventually die away for a certain choice of drill control parameters. The case study demonstrates how just using flowpipes or just reasoning about invariants alone can be insufficient and shows the richness of systems that one can handle with the proposed method, since the systems features modes with non-polynomial ODEs. We also propose an alternative method for proving persistence that relies solely on flowpipe computation

Advancements and Challenges in Object-Centric Process Mining: A Systematic Literature Review

Recent years have seen the emergence of object-centric process mining techniques. Born as a response to the limitations of traditional process mining in analyzing event data from prevalent information systems like CRM and ERP, these techniques aim to tackle the deficiency, convergence, and divergence issues seen in traditional event logs. Despite the promise, the adoption in real-world process mining analyses remains limited. This paper embarks on a comprehensive literature review of object-centric process mining, providing insights into the current status of the discipline and its historical trajectory

Interim research assessment 2003-2005 - Computer Science

This report primarily serves as a source of information for the 2007 Interim Research Assessment Committee for Computer Science at the three technical universities in the Netherlands. The report also provides information for others interested in our research activities

Mathematics in Software Reliability and Quality Assurance

This monograph concerns the mathematical aspects of software reliability and quality assurance and consists of 11 technical papers in this emerging area. Included are the latest research results related to formal methods and design, automatic software testing, software verification and validation, coalgebra theory, automata theory, hybrid system and software reliability modeling and assessment

A Survey of Challenges for Runtime Verification from Advanced Application Domains (Beyond Software)

Runtime verification is an area of formal methods that studies the dynamic analysis of execution traces against formal specifications. Typically, the two main activities in runtime verification efforts are the process of creating monitors from specifications, and the algorithms for the evaluation of traces against the generated monitors. Other activities involve the instrumentation of the system to generate the trace and the communication between the system under analysis and the monitor. Most of the applications in runtime verification have been focused on the dynamic analysis of software, even though there are many more potential applications to other computational devices and target systems. In this paper we present a collection of challenges for runtime verification extracted from concrete application domains, focusing on the difficulties that must be overcome to tackle these specific challenges. The computational models that characterize these domains require to devise new techniques beyond the current state of the art in runtime verification

Combining Machine Learning and Formal Methods for Complex Systems Design

During the last 20 years, model-based design has become a standard practice in many fields such as automotive, aerospace engineering, systems and synthetic biology. This approach allows a considerable improvement of the final product quality and reduces the overall prototyping costs. In these contexts, formal methods, such as temporal logics, and model checking approaches have been successfully applied. They allow a precise description and automatic verification of the prototype's requirements. In the recent past, the increasing market requests for performing and safer devices shows an unstoppable growth which inevitably brings to the creation of more and more complicated devices. The rise of cyber-physical systems, which are on their way to become massively pervasive, brings the complexity level to the next step and open many new challenges. First, the descriptive power of standard temporal logics is no more sufficient to handle all kind of requirements the designers need (consider, for example, non-functional requirements). Second, the standard model checking techniques are unable to manage such level of complexity (consider the well-known curse of state space explosion). In this thesis, we leverage machine learning techniques, active learning, and optimization approaches to face the challenges mentioned above. In particular, we define signal measure logic, a novel temporal logic suited to describe non-functional requirements. We also use evolutionary algorithms and signal temporal logic to tackle a supervised classification problem and a system design problem which involves multiple conflicting requirements (i.e., multi-objective optimization problems). Finally, we use an active learning approach, based on Gaussian processes, to deal with falsification problems in the automotive field and to solve a so-called threshold synthesis problem, discussing an epidemics case study.During the last 20 years, model-based design has become a standard practice in many fields such as automotive, aerospace engineering, systems and synthetic biology. This approach allows a considerable improvement of the final product quality and reduces the overall prototyping costs. In these contexts, formal methods, such as temporal logics, and model checking approaches have been successfully applied. They allow a precise description and automatic verification of the prototype's requirements. In the recent past, the increasing market requests for performing and safer devices shows an unstoppable growth which inevitably brings to the creation of more and more complicated devices. The rise of cyber-physical systems, which are on their way to become massively pervasive, brings the complexity level to the next step and open many new challenges. First, the descriptive power of standard temporal logics is no more sufficient to handle all kind of requirements the designers need (consider, for example, non-functional requirements). Second, the standard model checking techniques are unable to manage such level of complexity (consider the well-known curse of state space explosion). In this thesis, we leverage machine learning techniques, active learning, and optimization approaches to face the challenges mentioned above. In particular, we define signal measure logic, a novel temporal logic suited to describe non-functional requirements. We also use evolutionary algorithms and signal temporal logic to tackle a supervised classification problem and a system design problem which involves multiple conflicting requirements (i.e., multi-objective optimization problems). Finally, we use an active learning approach, based on Gaussian processes, to deal with falsification problems in the automotive field and to solve a so-called threshold synthesis problem, discussing an epidemics case study