Milan Češka

PNtalk - a Computerized Tool for Object Oriented Petri Nets Modelling

The paper deals with a new formalism and a tool for concurrent systems modelling and prototyping. This formalism, Object Oriented Petri Net (OOPN), combines the descriptive power of Petri nets with the well known advantages of object oriented modelling techniques. In the paper there are presented the structure of system specifications by means of OOPNs, principles of their dynamic behaviour as well as some basic features of a computerized tool which supports practical use of OOPNs. This tool works with a language called PNtalk which is based on the OOPNs and the language Smalltalk.

Syntax-Guided Optimal Synthesis for Chemical Reaction Networks

We study the problem of optimal syntax-guided synthesis of stochastic Chemical Reaction Networks (CRNs) that plays a fundamental role in design automation of molecular devices and in the construction of predictive biochemical models. We propose a sketching language for CRNs that concisely captures syntactic constraints on the network topology and allows its under-specification. Given a sketch, a correctness specification, and a cost function defined over the CRN syntax, our goal is to find a CRN that simultaneously meets the constraints, satisfies the specification and minimizes the cost function. To ensure computational feasibility of the synthesis process, we employ the Linear Noise Approximation allowing us to encode the synthesis problem as a satisfiability modulo theories problem over a set of parametric Ordinary Differential Equations (ODEs). We design and implement a novel algorithm for the optimal synthesis of CRNs that employs almost complete refutation procedure for SMT over reals and ODEs, and exploits a meta-sketching abstraction controlling the search strategy. Through relevant case studies we demonstrate that our approach significantly improves the capability of existing methods for synthesis of biochemical systems and paves the way towards their automated and provably-correct design.

Designing Robust Software Systems through Parametric Markov Chain Synthesis

We present a method for the synthesis of software system designs that satisfy strict quality requirements, are Pareto-optimal with respect to a set of quality optimisation criteria, and are robust to variations in the system parameters. To this end, we model the design space of the system under development as a parametric continuous-time Markov chain (pCTMC) with discrete and continuous parameters that correspond to alternative system architectures and to the ranges of possible values for configuration parameters, respectively. Given this pCTMC and required tolerance levels for the configuration parameters, our method produces a sensitivity-aware Pareto-optimal set of designs, which allows the modeller to inspect the ranges of quality attributes induced by these tolerances, thus enabling the effective selection of robust designs. Through application to two systems from different domains, we demonstrate the ability of our method to synthesise robust designs with a wide spectrum of useful tradeoffs between quality attributes and sensitivity.

Pattern-Based Verification of Programs with Extended Linear Linked Data Structures

The paper deals with the problem of automatic verification of programs with dynamic linked data structures. In particular, the use of pattern-based abstraction of memory configurations is considered. In this approach, one can abstract memory configurations by abstracting away the exact number of adjacent occurrences of certain memory patterns. The paper extends the state-of-the-art in this area by proposing a fully automatic and efficient way of detecting the memory patterns to be used from the memory configurations that the program at hand is generating. The method targets programs manipulating a broad class of extended linear linked data structures having a linear skeleton (possibly bidirectionally-linked or cyclic) with certain additional pointers defined on top of it, which covers many practical dynamic data structures (such as lists, doubly-linked lists, cyclic lists, lists with tail/head pointers, etc.). The experimental results obtained from a prototype implementation of the method show that the method is very competitive and offers a big potential for future extensions.

Generalised multi-pattern-based verification of programs with linear linked structures

The paper deals with the problem of automatic verification of programs working with extended linear linked dynamic data structures, in particular, pattern-based verification is considered. In this approach, one can abstract memory configurations by abstracting away the exact number of adjacent occurrences of certain memory patterns. With respect to the previous work on the subject the method presented in the paper has been extended to be able to handle multiple patterns, which allows for verification of programs working with more types of structures and/or with structures with irregular shapes. The experimental results obtained from a prototype implementation of the method show that the method is very competitive and offers a big potential for future extensions.

RODES: A Robust-Design Synthesis Tool for Probabilistic Systems.

We introduce RODES – a tool for the synthesis of probabilistic systems that satisfy strict reliability and performance requirements, are Pareto-optimal with respect to a set of optimisation objectives, and are robust to variations in the system parameters. Given the design space of a system (modelled as a parametric continuous-time Markov chain), RODES generates system designs with low sensitivity to required tolerance levels for the system parameters. As such, RODES can be used to identify and compare robust designs across a wide range of Pareto-optimal tradeoffs between the system optimisation objectives.

Reachability Computation for Switching Diffusions: Finite Abstractions with Certifiable and Tuneable Precision

We consider continuous time stochastic hybrid systems with no resets and continuous dynamics described by linear stochastic differential equations -- models also known as switching diffusions. We show that for this class of models reachability (and dually, safety) properties can be studied on an abstraction defined in terms of a discrete time and finite space Markov chain (DTMC), with provable error bounds. The technical contribution of the paper is a characterization of the uniform convergence of the time discretization of such stochastic processes with respect to safety properties. This allows us to newly provide a complete and sound numerical procedure for reachability and safety computation over switching diffusions.

Partial-Order Reduction in Model Checking Object-Oriented Petri Nets

The main problem being faced in finite-state model checking is the state space explosion problem. For coping with it, many advanced methods for reducing state spaces have been proposed. One of the most successful methods (especially when dealing with software systems) is the so-called partial-order reduction. In the paper, we examine how this method can be used in the context of object-oriented Petri nets, which bring in features like dynamic instantiation, late binding, garbage collection, etc.

Modelling, prototyping, and verifying concurrent and distributed applications using object‐oriented Petri nets

This paper presents several research issues associated with the PNtalk language that is based on a certain kind of object‐oriented Petri nets (OOPNs) and intended mainly for modelling, prototyping, and verifying concurrent and distributed applications. The paper reviews the main concepts of PNtalk and OOPNs followed by a proposal of a system allowing prototypes based on PNtalk to be run in a distributed way. Furthermore, the first step made towards state spaces‐based formal analysis and verification over PNtalk OOPNs are also briefly mentioned in the paper.

Parametric Multi-step Scheme for GPU-Accelerated Graph Decomposition into Strongly Connected Components

The problem of decomposing a directed graph into strongly connected components (SCCs) is a fundamental graph problem that is inherently present in many scientific and commercial applications. Clearly, there is a strong need for good high-performance, e.g., GPU-accelerated, algorithms to solve it. Unfortunately, among existing GPU-enabled algorithms to solve the problem, there is none that can be considered the best on every graph, disregarding the graph characteristics. Indeed, the choice of the right and most appropriate algorithm to be used is often left to inexperienced users. In this paper, we introduce a novel parametric multi-step scheme to evaluate existing GPU-accelerated algorithms for SCC decomposition in order to alleviate the burden of the choice and to help the user to identify which combination of existing techniques for SCC decomposition would fit an expected use case the most. We support our scheme with an extensive experimental evaluation that dissects correlations between the internal structure of GPU-based algorithms and their performance on various classes of graphs. The measurements confirm that there is no algorithm that would beat all other algorithms in the decomposition on all of the classes of graphs. Our contribution thus represents an important step towards an ultimate solution of automatically adjusted scheme for the GPU-accelerated SCC decomposition.