Sebastian Junges

PROPhESY: A PRObabilistic ParamEter SYnthesis Tool

We present PROPhESY, a tool for analyzing parametric Markov chains (MCs). It can compute a rational function (i.e., a fraction of two polynomials in the model parameters) for reachability and expected reward objectives. Our tool outperforms state-of-the-art tools and supports the novel feature of conditional probabilities. PROPhESY supports incremental automatic parameter synthesis (using SMT techniques) to determine “safe” and “unsafe” regions of the parameter space. All values in these regions give rise to instantiated MCs satisfying or violating the (conditional) probability or expected reward objective. PROPhESY features a web front-end supporting visualization and user-guided parameter synthesis. Experimental results show that PROPhESY scales to MCs with millions of states and several parameters. Open image in new window

A Storm is Coming: A Modern Probabilistic Model Checker

We launch the new probabilistic model checker Storm. It features the analysis of discrete- and continuous-time variants of both Markov chains and MDPs. It supports the Prism and JANI modeling languages, probabilistic programs, dynamic fault trees and generalized stochastic Petri nets. It has a modular set-up in which solvers and symbolic engines can easily be exchanged. It offers a Python API for rapid prototyping by encapsulating Storm’s fast and scalable algorithms. Experiments on a variety of benchmarks show its competitive performance.

SMT-RAT: An Open Source C++ Toolbox for Strategic and Parallel SMT Solving

During the last decade, popular SMT solvers have been extended step-by-step with a wide range of decision procedures for different theories. Some SMT solvers also support the user-defined tuning and combination of such procedures, typically via command-line options. However, configuring solvers this way is a tedious task with restricted options.

SMT-RAT: an SMT-compliant nonlinear real arithmetic toolbox

We present

Uncovering Dynamic Fault Trees

\texttt{SMT-RAT}

Fault trees on a diet: automated reduction by graph rewriting

, a

Safety-Constrained Reinforcement Learning for MDPs

\texttt{C++}

Sequential Convex Programming for the Efficient Verification of Parametric MDPs

toolbox offering theory solver modules for the development of SMT solvers for nonlinear real arithmetic (NRA). NRA is an important but hard-to-solve theory and only fragments of it can be handled by some of the currently available SMT solvers. Our toolbox contains modules implementing the virtual substitution method, the cylindrical algebraic decomposition method, a Grobner bases simplifier and a general simplifier. These modules can be combined according to a user-defined strategy in order to exploit their advantages.

Advancing Dynamic Fault Tree Analysis - Get Succinct State Spaces Fast and Synthesise Failure Rates

Fault tree analysis is a widespread industry standard for assessing system reliability. Standard (static) fault trees model the failure behaviour of systems in dependence of their component failures. To overcome their limited expressive power, common dependability patterns, such as spare management, functional dependencies, and sequencing are considered. A plethora of such dynamic fault trees (DFTs) have been defined in the literature. They differ in e.g., the types of gates (elements), their meaning, expressive power, the way in which failures propagate, how elements are claimed and activated, and how spare races are resolved. This paper systematically uncovers these differences and categorises existing DFT variants. As these differences may have huge impact on the reliability assessment, awareness of these impacts is important when using DFT modelling and analysis.

On Gröbner Bases in the Context of Satisfiability-Modulo-Theories Solving over the Real Numbers

Fault trees are a popular industrial technique for reliability modelling and analysis. Their extension with common reliability patterns, such as spare management, functional dependencies, and sequencing—known as dynamic fault trees (DFTs)—has an adverse effect on scalability, prohibiting the analysis of complex, industrial cases. This paper presents a novel, fully automated reduction technique for DFTs. The key idea is to interpret DFTs as directed graphs and exploit graph rewriting to simplify them. We present a collection of rewrite rules, address their correctness, and give a simple heuristic to determine the order of rewriting. Experiments on a large set of benchmarks show substantial DFT simplifications, yielding state space reductions and timing gains of up to two orders of magnitude.