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.

翻译：对包含时间或概率选择等定量方面的形式模型进行分析，是通过定量验证工具实现的。目前已有广泛且成熟的工具支持计算基本属性，例如马尔可夫链等基本模型上的期望奖励。此前针对定量形式模型分析工具的比较项目QComp，主要聚焦于这一基础场景。然而，许多应用场景需要更高级的属性类型，如LTL和参数综合查询，以及更高级的模型，如随机博弈和部分可观测MDP。对于这些高级类型和模型，当前的工具支持尚处于起步阶段。本文介绍了QComp 2023的成果：对支持高级属性类型和模型的定量验证工具最新进展进行了全面调研。本报告涵盖了从早期研究原型到成熟工具集集成良好支持的各类工具，重点指出了当前定量验证工具研究的活跃领域以及未来面临的挑战。