Arnd Hartmanns

Pedro R. D'Argenio, Arnd Hartmanns, Patrick Wienhöft et al.

Statistical model checking delivers quantitative verification results with statistical guarantees. It scales to model sizes and model types that are out of reach for exhaustive, analytical techniques. So far, it has been used to evaluate one property value at a time only. Many practical problems, however, require finding the Pareto front of optimal tradeoffs between multiple objectives. In this paper, we present the first statistical model checking approach for such multi-objective Pareto queries, based on lightweight strategy sampling. We introduce an incremental scheme that almost surely converges to a statistically sound confidence band around the true Pareto front in the long run. To obtain a close underapproximation of the true front in finite time, we propose three heuristic approaches that try to make the best of an a-priori fixed sampling budget. We implement our new techniques in the modes simulator of the Modest Toolset, and show their effectiveness on benchmarks from the literature.

Pedro R. D'Argenio, Arnd Hartmanns, Annabell Petri

Stochastic automata (SA) are a formal stochastic continuous-time model based on countdown timers whose expiration times follow general probability distributions. SA are particularly useful to faithfully model and analyse dependable systems involving faults, maintenance, and repairs. Effective SA analysis approaches have so far been limited to statistical model checking and thus deterministic SA, while previously proposed model-checking techniques apply to limited subclasses of SA only, or do not scale. In this paper, we present the first dedicated SA model checking approach that is general and effective: It puts few restrictions on the input SA, and we show in our experimental evaluation that it works well for nontrivial examples. It combines a refinable interval abstraction of the continuous distributions with a direct application of the "big time steps" semantics of SA, providing upper/lower bounds on maximum/minimum reachability probabilities. We extend the Modest and Jani modelling formalisms with support for SA, and provide a prototype implementation of our approach in Rust.

Roman Andriushchenko, Arnd Hartmanns, Joshua Jeppson et al.

This paper presents the unified Markov binary (UMB) format, an efficient, extensible, and well-supported explicit-state file format for representing a wide range of probabilistic systems. UMB addresses the problem that, while probabilistic model checking tools often support common high-level modelling languages, there is no effective mechanism for exchanging low-level model representations. In practice, textual, tool-specific formats are used, hampering interoperability and resulting in large overheads in writing and reading model files. UMB provides a clean, unified, and efficient solution, based on a general underlying mathematical model, and encoded using a small set of bit-level primitive data structures. The format has already been adopted by prominent tools and comes with a convenient Python library for reading, manipulating, creating, and validating models, plus infrastructure for cross-tool installation and continuous validation. We report on both the efficiency of the file format and the new practical use cases that it facilitates.