Parametric model checking (PMC) computes algebraic formulae that express key non-functional properties of a system (reliability, performance, etc.) as rational functions of the system and environment parameters. In software engineering, PMC formulae can be used during design, e.g., to analyse the sensitivity of different system architectures to parametric variability, or to find optimal system configurations. They can also be used at runtime, e.g., to check if non-functional requirements are still satisfied after environmental changes, or to select new configurations after such changes. However, current PMC techniques do not scale well to systems with complex behaviour and more than a few parameters. Our paper introduces a fast PMC (fPMC) approach that overcomes this limitation, extending the applicability of PMC to a broader class of systems than previously possible. To this end, fPMC partitions the Markov models that PMC operates with into \emph{fragments} whose reachability properties are analysed independently, and obtains PMC reachability formulae by combining the results of these fragment analyses. To demonstrate the effectiveness of fPMC, we show how our fPMC tool can analyse three systems (taken from the research literature, and belonging to different application domains) with which current PMC techniques and tools struggle.

中文翻译：

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通过模型分段进行快速参数模型检查

参数模型检查（PMC）计算代数公式，这些公式将系统的关键非功能属性（可靠性，性能等）表示为系统的有理函数和环境参数。在软件工程中，PMC公式可在设计过程中使用，例如，分析不同系统体系结构对参数可变性的敏感性，或查找最佳系统配置。它们也可以在运行时使用，例如，检查环境更改后是否仍满足非功能性要求，或在更改后选择新的配置。但是，当前的PMC技术无法很好地扩展到具有复杂行为和多个参数的系统。我们的论文介绍了一种克服了这一局限性的快速PMC（fPMC）方法，将PMC的适用性扩展到比以前更广泛的系统类别。为此，fPMC将与PMC一起使用的Markov模型划分为\ emph {fragments}，并对其可达性进行独立分析，并通过结合这些片段分析的结果获得PMC可达性公式。为了证明fPMC的有效性，我们展示了fPMC工具如何分析当前PMC技术和工具所遇到的三个系统（来自研究文献，属于不同的应用领域）。