Tools at the Frontiers of Quantitative Verification

TL;DR

This paper surveys the current state of quantitative verification tools, focusing on advanced property types and models, highlighting progress, challenges, and future directions in the field.

Contribution

It provides a comprehensive overview of the latest tools and research developments in quantitative verification for complex models and properties.

Findings

Tools support advanced properties like LTL and parameter synthesis.

Support for complex models such as stochastic games is still developing.

Active research areas include tool integration and handling of sophisticated models.

Abstract

The analysis of formal models that include quantitative aspects such as timing or probabilistic choices is performed by quantitative verification tools. Broad and mature tool support is available for computing basic properties such as expected rewards on basic models such as Markov chains. Previous editions of QComp, the comparison of tools for the analysis of quantitative formal models, focused on this setting. Many application scenarios, however, require more advanced property types such as LTL and parameter synthesis queries as well as advanced models like stochastic games and partially observable MDPs. For these, tool support is in its infancy today. This paper presents the outcomes of QComp 2023: a survey of the state of the art in quantitative verification tool support for advanced property types and models. With tools ranging from first research prototypes to well-supported integrations into established toolsets, this report highlights today's active areas and tomorrow's challenges in tool-focused research for quantitative verification.